The value of points of interest information
in predicting cost-effective charging
infrastructure locations
Master Thesis MSc. Business Information Management
Stéphanie Florence Visser
Thirty-sixth International Conference on Information Systems, Forth Worth 2015
The value of points of interest information in predicting
cost-effective charging infrastructure locations
Stéphanie Florence Visser
407153
MSc. Micha Kahlen
Dr Rodrigo Belo
14 August 2016
MSc. Business Information Management
The copyright of the master thesis rests with the author. The author is responsible for its contents. RSM
is only responsible for the educational coaching and cannot be held liable for the content.
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Executive summary
In this study, we assess the value of using information on points of interest in predicting cost-effective
electric vehicle charging infrastructure locations. The benefits of battery electric vehicle adoption are
profound and necessary, as they revolve around the potential to combat climate change. However, battery
electric vehicles (BEVs) currently lack mass adoption, amongst others due to range anxiety of potential
users – which can be diminished by increasing the presence of charging infrastructure. Improved insight
in potential charging demand can help overcome the reluctance of infrastructure providers to place
charging stations before actual demand is known – and as explanatory studies show a relationship
between points of interest and charging demand, we investigate the predictive power of this relationship.
We therefore study potential charging demand in the case city of San Diego, given expected electric
vehicles on-the-road in the year of 2020. We investigate both slow charging and fast charging demand
based on an extensive dataset of parking behavior of battery electric vehicles over the course of 63 weeks,
and combine this with information on points of interest within the city of San Diego. After investigating
different methods to ensure the best possible predictive power, we use a Gradient Boosting Model to
predict potential charging demand.
Our findings show that electric vehicle charging demand can be more accurately predicted when using a
model supplemented with information on points of interest, compared to a model with information on
neighborhood characteristics, seasonality and charging infrastructure presence. Consequently, our
findings show that a supplemented model can assist in decision-making regarding cost-effective charging
locations, by promising 2 to 45 times more profit using the supplemented model, than when using the
base model. Still, we find that the supplemented model under-predicts high-demand locations, hence that
the model loses out on potential profit. We show that although slow charging demand can be more
accurately predicted, the supplemented model provides most gain in fast charging demand prediction.
Further, in our models presence of a food and beverage store, leisure activity, or airport is most influential
on predicting demand. This study therefore shows that information on points of interest is indeed
valuable in predicting cost-effective electric vehicle charging locations.
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Acknowledgements
I would like to take the opportunity to express my sincere gratitude to the following individuals who
significantly contributed to this educational journey.
Micha Kahlen – I honestly could not have wished for a better coach. Thank you for your excellent
guidance, insights and clarity throughout the whole process. I sincerely appreciate your time and
patience, especially in the last weeks when I went full-stalk mode.
Dr Rodrigo Belo, who made me genuinely enthusiastic about statistics and R. Thank you for stimulating
me to think further, for helping me fight the data, and for your time and patience when adopting me lastminute as a thesis student.
My mum, Béatrice, who is the most awesome mum ever – and who still teaches me dedication and
happiness every day. Thank you for your continuous support, and for helping me to bring out the best in
myself.
Lastly, my homeboys Dirk and Job – you guys were the best possible people to conquer BIM with, and I
could not have done it without you. Thank you for teaching me the value of teamwork, for the
encouragement, and for the laughter.
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Table of contents
1. Introduction
8
1.1 Background
8
1.2 Research question
10
1.3 Relevance
10
2. Related work and conceptual model
12
2.1 The importance of infrastructure availability
12
2.1.1 Electric vehicle adoption
12
2.1.2 Range anxiety
13
2.2 Predictors of charging demand
14
2.2.1 Points of interest
14
2.2.2 Other predictors of charging demand
16
2.3 The business case of charging infrastructure
17
2.4 Predictive modelling
18
2.5 Summary of findings and conceptual model
19
2.6 The rest of this paper
19
3. Methodology
21
3.1 Context
21
3.1.1 Expected charging demand in San Diego
21
3.1.2 Charging station types
22
3.2 Study design
22
3.3 Data
22
3.3.1 Data on parking behavior
22
3.3.2 Data on points of interest
25
3.3.3 Data on neighborhood characteristics
26
3.4 Data preparation
27
3.4.1 Response variable
27
3.4.2 Point of interest predictors
28
3.4.3 Base predictors
31
3.5 Model selection
32
3.5.1 Selecting predictors and method
32
3.5.2 Gradient Boosting Method
32
3.5.3 Generalized Linear Models
33
3.5.4 Data partitioning
33
3.5.5 Comparison metrics
33
3.5.6 Overview predictors and methods
35
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4. Assessment of predictive value
36
4.1 Demand predictions base models
36
4.1.1 Base model slow charging
36
4.1.2 Base model fast charging
39
4.2 Demand predictions points of interest model
42
4.2.1 Points of interest model slow charging
43
4.2.2 Points of interest model fast charging
45
4.2.3 Demand prediction differences
48
4.3 Location predictions
48
4.3.1 Costs and revenue
48
4.3.2 Predictive metrics
49
4.3.3 Cost-effective charging locations
50
4.3.4 Charging locations given predetermined investment
52
4.3.5 Improved decision-making
53
5. Discussion
56
5.1 Charging demand prediction using POI information
56
5.1.1 Difference in demand prediction between models
56
5.1.2 Large difference between actual and predicted demand
56
5.1.3 Slow charging more accurately predicted than fast charging demand
57
5.1.4 F&B store, leisure activity and airport most influential on predictions
57
5.1.5 Importance of neighborhood characteristics and seasonality
58
5.1.6 Explanatory power is not necessarily related to predictive power
58
5.2. Decision-making using POI information
59
5.2.1 Better prediction of cost-effective locations using POI information
59
5.2.2 More potential profit can be made using point of interest information
59
5.2.3 Yet we lose out on profit due to under-prediction
60
5.2.4 Model applications
60
5.3 Assessment of methodologies used
61
5.3.1 Predictive power of Gradient Boosting
61
6. Conclusion
62
6.1 The value of points of interest information
62
6.2 Academic relevance
63
6.3 Managerial relevance
63
6.4 Limitations and suggestions for future research
64
Bibliography
65
Appendices
71
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List of tables and figures
Table 1. Overview studies finding influence of points of interest
16
Table 2. Extract raw data parking behavior
23
Table 3. Extract raw data points of interest
25
Table 4. Points of interest categories
26
Table 5. San Diego neighborhoods
26
Table 6. Table of notation
31
Table 7. Overview predictors and methods
35
Table 8. Predictive metrics base model slow charging
39
Table 9. Predictive metrics base model fast charging
42
Table 10. Predictive metrics model comparison
43
Table 11. Predictive metrics supplemented model slow charging
45
Table 12. Predictive metrics supplemented model fast charging
47
Table 13. Lifetime charging station costs $
48
Table 14. Predicted cost-effective parking locations metrics
54
Table 15. Predicted top 100 parking locations metrics
55
Figure 1. Conceptual model
20
Figure 2. San Diego parking locations - where red dots indicate parking locations
24
Figure 3. San Diego points of interest - where red dots indicate points of interest
25
Figure 4. Base model slow charging 50 trees – where red dots indicate lowest MSE
37
Figure 5. Base model slow charging 9 trees – where red dots indicate lowest MSE
38
Figure 6. Predictor importance base model slow charging
39
Figure 7. Base model fast charging 50 trees - where red dots indicate lowest MSE
40
Figure 8. Base model fast charging 8 trees - where red dots indicate lowest MSE
41
Figure 9. Predictor importance base model fast charging
42
Figure 10. Supplemented model slow charging 100 trees - where red dots indicate lowest MSE
44
Figure 11. Predictor importance supplemented model slow charging
45
Figure 12. Supplemented model fast charging 100 trees - where red dots indicate lowest MSE
46
Figure 13. Predictor importance supplemented model fast charging
47
Appendix 1. Extract points of interest
71
Appendix 2. Example non-linearity predictor and response
72
Appendix 3. Density response variable
72
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1. Introduction
1.1 Background
Although CO2 emissions remained stable in 2014, the International Panel on Climate Change (IPCC)
warns that without drastic action, greenhouse gasses will irreversibly change our climate (IEA, 2015).
Already, the number of extreme weather events such as droughts, heat waves, floods, rising sea levels and
typhoons increased over four times from 1980 to 2014, from 38 events to 174 events respectively (World
Energy Council, 2015). The transportation sector significantly contributes to these developments,
accounting for almost 20% of greenhouse gas (GHG) emissions in the EU in 2013 (European
Environment Agency, 2016). To combat climate change, governments have set goals for the
transportation sector to reduce this type of emission. For example, the EU aims to lower GHG emission
from transport by 20% in 2030 compared with 2008 levels, resulting in a cut of 60% in GHG emission by
2050 compared to 1990 (European Commission, 2011). Along the same lines, the US committed to several
new policies for the transportation sector that will assist in reaching the overall US goal of a 17% GHG
emission reduction in 2020 compared to 2005 levels (US Department of State, 2014). As the large
contribution to GHG of the transportation sector can mainly be accounted to the intensive use of fossil
fuels, policy-makers and other stakeholders are increasingly interested in transportation means that are
less dependent on oil. More specifically, deployment of plug-in Electric Vehicles (EVs) offers
opportunities to significantly lower emissions.
Plug-in Electric Vehicles (PEVs) include Battery Electric Vehicles (BEVs, e.g. Nissan Leaf) and Plug-in
Hybrid Electric Vehicles (PHEVs, e.g. Toyota Prius Plug-in). As the latter vehicle type, apart from a
rechargeable battery, also deploys an internal combustion engine, BEVs are preferred over PHEVs in the
light of environmental impact. BEVs produce no tailpipe GHG emissions (Mak, Rong, & Shen, 2013;
Schneider, Stenger, & Goeke, 2014), can be powered by sustainable energy sources, produce minimal
noise (Schneider et al., 2014), and can reduce GHG emissions even when the electricity production at time
of charging is unusually CO2 high (San Román, Momber, Abbad, & Sánchez Miralles, 2011). BEVs
therefore have the potential to become popular on a large scale, especially in urban areas, where
combatting pollution is high on the agenda of every administrator. However, success of these vehicles will
depend on advancements in recharging infrastructure.
At the moment, Battery Electric Vehicle charging poses more challenges to users than refueling internal
combustion engine (ICE) vehicles. Compared to their traditional counterparts, Battery Electric Vehicles
have a shorter driving range hence need more frequent recharging, require special charging stations, and
demand a significantly longer charging time. This necessitates special care when planning public charging
infrastructure; only when EV charging is perceived as efficient and convenient, BEV adoption will be
positively influenced (Guo & Zhao, 2015; Ip, Fong, & Liu, 2010; Lin, 2014). Conversely, perceived low or
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inconvenient availability of charging infrastructure could hinder BEV adoption due to range anxiety – the
fear of stranding with a depleted battery (Cai, Jia, Chiu, Hu, & Xu, 2014; Egbue & Long, 2012).
Convenient public charging infrastructure is especially vital in urban areas to ensure BEV adoption, due to
limited access to home charging (Ip et al., 2010).
However, as placement of EV infrastructure started only years ago, planning and deployment of effective
charging infrastructure is currently subject to a ‘chicken-and-egg dilemma’ (Mak et al., 2013); on the one
hand, customers are willing to purchase BEVs only when plenty of charging facilities are placed at easily
accessible locations. On the other hand, distribution system operators are only inclined to place charging
facilities once a certain demand is observed, as underutilized stations can lead to a waste of resources (Cai
et al., 2014). Indeed, currently the electric vehicle infrastructure landscape is shaped by government
funding (McCormack, Sanborn, & Rhett, 2013), whilst the EV market can only be sustainable in the longrun when private investors are able to make profit on their investment (Madina, Zamora, & Zabala, 2016).
To challenge this dilemma, distribution system operators should be able to determine locations that
expect a certain level of charging demand hence that promise return on investment, before actual demand
is known.
Advancement in BEV charging infrastructure is therefore attained when charging stations are placed at
locations where they are easily accessible to BEV users for refueling, whilst being economically efficient to
the charging station distributor (Brooker & Qin, 2015; Guo & Zhao, 2015; Madina et al., 2016; Shukla,
Pekny, & Venkatasubramanian, 2011). It is for this reason that over the last few years, the question of
effective charging station infrastructure has surged interest amongst scholars. As trip or refueling patterns
of ICE vehicles may not be one-on-one applicable in a battery electric vehicle context, some studies have
focused on destination characteristics; due to their currently long recharging time, BEVs are assumed to
charge more frequently at a destination rather than in the middle of a trip. These studies found a
relationship between points of interest, such as museums or restaurants, and parking demand (Brooker &
Qin, 2015; Wagner, Brandt, & Neumann, 2014, 2015). It may therefore be, that placing charging stations
close to specific points of interest ensures the necessary demand to make privately-funded charging
stations profitable.
Although the relationship between points of interest and parking demand seems promising, there is still a
lot to gain in assessing its practical value. Most studies that report evidence on this relationship, have
used proxies for BEV charging such as ICE vehicle parking and refueling data, or ICE vehicle-focused
survey data (Brooker & Qin, 2015; Cai et al., 2014; Chen, Hall, & Kockelman, 2013; Shahraki, Cai, Turkay,
& Xu, 2015; Wagner et al., 2015). Further, in a BEV context no successful attempt is made to examine the
actual predictive power of this relationship. Should we want private parties to be convinced to invest in
charging infrastructure before demand is known, it is vital to study whether or not we can predict costeffective locations based on this relationship.
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1.2 Research question
Mass-adoption of electric vehicles will only make advancements when private investors can gain return on
investment from placing electric vehicle recharging stations, which requires providing (potential) users
with easily accessible public charging infrastructure. In order to contribute to solving this chicken-andegg dilemma, this study aims to assess the practical value of the promising relationship between points of
interest and demand, by answering the following research question:
RQ. What is the value of using information of points of interest in predicting cost-effective electric
vehicle charging infrastructure locations?
To effectively answer this research question, this study focuses on the following two sub-research
questions:
SRQ1. How is predicted electric vehicle charging infrastructure demand influenced when using
information on points of interest supplementary to information on neighborhood characteristic,
seasonality and charging infrastructure presence?
SRQ2. How can a model supplemented with information on points of interest improve decision-making
in predicting cost-effective electric vehicle charging locations?
1.3 Relevance
The relevance of this study is two-fold, focusing both on academic relevance and on managerial relevance.
We aim to advance current academic knowledge through two main contributions. Firstly, we use a unique
dataset of parking behavior of battery electric vehicles that was recorded over the course of fourteen
months. To our knowledge, no study thus far used such extensive data of parking behavior within a
battery electric vehicle parking demand context. Secondly, we aim to contribute to the understanding of
the relationship between points of interest and potential charging behavior, by assessing the practical
relevance of this relationship through predictive modelling.
From a managerial perspective, this study will assist policy makers and independent charging
infrastructure operators in determination of cost-efficient charging infrastructure locations before actual
demand is known. We thereby hope to contribute to improved decision-making in this field, eventually
making the electric vehicle market more attractive for both car users and infrastructure operators. Lastly,
this predictive approach may also be used in a non-electric vehicle context; as predicted load is directly
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linked to parking time, parking facility operators or point of interest operators such as shopping malls
may also find our methodology useful in predicting parking demand at specific locations.
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2. Related work and conceptual model
In light of the research question of this study, we examine related work and develop a conceptual model
accordingly.
2.1 The importance of infrastructure availability
2.1.1 Electric vehicle adoption
The benefits of electric vehicle adoption are profound and necessary, as they revolve around the potential
to lower emissions hence to combat climate change. Lowering emissions can specifically have a notable
influence on cities and their inhabitants; partially due to transportation emissions, urban areas suffer
from toxic air pollution which severely affects inhabitants’ health (Wagner et al., 2014). Studies on
reduction of emissions from motorized vehicles by introduction of alternative-fuel vehicles such as electric
vehicles, forecast high-impact results such as a reduction in premature deaths, a reduction in years of life
lost, and a reduction in disability-adjusted life-years (Woodcock et al., 2009). However, despite the clear
benefits, EV adoption is still low. In 2014, electric vehicles comprised of only 0.08% of the global number
of passenger cars (International Energy Agengy, 2015). The percentage of electric vehicles is higher when
only taking developed countries into account – yet is still very modest. Electric vehicle sales in the
European Union made up of 0.7% of total vehicle registrations in 2014, with Norway (13.8%) and the
Netherlands (4.0%) as frontrunners due to fiscal incentives promoted by the governments of these
countries (International Council on Clean Transportation, 2015). In the same year, EVs in the United
States made up of 1.5% of the total number of vehicle market share (International Energy Agengy, 2015).
To understand why electric vehicles lack mass adoption, one should be aware of the factors that influence
EV purchase intentions amongst consumers hence that may act as drivers for or barriers against
acceptance. It is for this reason that Rezvani, Jansson, & Bodin (2015) published a meta-analysis
concerning EV purchase intentions, covering studies that were published in peer-reviewed journals
between 2007 and 2014. In this analysis, it becomes clear that multiple studies find evidence for the
perceived importance of charging infrastructure in EV adoption. Charging infrastructure is specified as a
major concern to EV adoption in Egbue & Long (2012), yet these concerns are not made explicit due to the
generic formulation of the study’s survey question regarding this matter (Q: “What do you consider your
biggest concern about EVs?” A: “Charging infrastructure” (Egbue & Long, 2012, p. 727)). Fortunately,
this concern is made clearer in other studies, where results show that drivers express anxiety regarding
availability and safety of public charging points (Graham-Rowe et al., 2012), or that EV demand is
significantly influenced by availability of charging locations both at work and in the public space (Jensen,
Cherchi, & Mabit, 2013). This appears to be especially true amongst customers that describe themselves
as planners who prefer structure; this consumer group would be more likely to purchase a BEV when
charging points were available at supermarkets and in town centers. Perceived availability appears to have
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a strong association with visibility, as EV interest levels are significantly higher amongst potential EV
users that have seen recharging stations in their neighborhood (Carley, Krause, Lane, & Graham, 2013).
Lastly, charging infrastructure concerns do not seem to have a relation with the act of plugging-in the EV
or remembering to do so (Skippon & Garwood, 2011). In conclusion, it is evident that mass adoption of
electric vehicles is related to absence or presence of charging infrastructure.
2.1.2 Range anxiety
Above studies signify the importance of EV charging infrastructure availability – a concept that stems
from potential customer concerns regarding the currently-inherent nature of electric vehicles, namely that
EVs suffer from a shorter travelling distance compared to internal combustion engine vehicles hence need
more frequent recharging, and that this recharging requires a significantly longer time than ICE vehicles
(Ip et al., 2010; Neubauer & Wood, 2014). Although technical advancements will eventually resolve these
issues, currently these EV characteristics cause a “fear of being stranded in a BEV because it has
insufficient range to reach its destination” (Egbue & Long, 2012, p. 273) – a concept dubbed as range
anxiety. Remarkably, (potential) EV users do not appear to experience range anxiety as such, yet a priori
resolve the potential of range anxiety by reaching towards other means of transportation, such as ICE
vehicles (Franke, Neumann, Bühler, Cocron, & Krems, 2012). Therefore, range anxiety indeed is a barrier
for mass adoption of electric vehicles (Egbue & Long, 2012; Rauh, Franke, & Krems, 2015).
Additionally, this tendency to avoid potential range stress gives room to a paradoxical effect; although the
optimal EV range is the smallest sufficient range in light of EV ecological footprint and cost-efficiency
(Franke & Krems, 2013c), EV users prefer a much larger range to feel comfortable (Egbue & Long, 2012) –
a safety buffer which is confirmed by the finding that drivers usually have a large surplus of battery left
when recharging (Franke & Krems, 2013b; Speidel & Braunl, 2014). Multiple studies have shown that for
a large share of drivers, their average daily range needs easily fall into the common 100-mile EV range
(Franke & Krems, 2013a). However, their comfortable range preferences are substantially higher than
these average daily needs; in Egbue & Long (2012), 91% of respondents drove less than 50 miles per day,
yet only 32% of drivers was interested in an EV with a maximum range of 100 miles – 45% of respondents
indicated to only be interested in BEVs with a range greater than 200 miles. This is potentially due to the
finding that although range preferences are higher than average needs, they are not substantially higher
than maximum daily range needs (Franke & Krems, 2013c). Indeed, in a sample of US drivers, only 9% of
drivers never exceeds the 100 miles range in a year – hence for 91% of drivers, a 100-mile range EV that
would be charged once per day would fail to adhere to the needs of the driver at least once a year (Pearre,
Kempton, Guensler, & Elango, 2011).
Range anxiety therefore is a significant barrier to EV adoption that needs to be diminished in order to
ensure EV success. This anxiety is related to multiple factors which can therefore be deployed to reduce
this stress; experience, where more seasoned EV drivers experience less range stress (Franke & Krems,
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2013c; Rauh et al., 2015); personality traits of the driver such as ambiguity tolerance (Franke & Krems,
2013a; Franke et al., 2012); and availability of public charging infrastructure (Neubauer & Wood, 2014).
The first two factors seem to be challenging to deploy in order to reduce range anxiety, as studies suggest
that EV drivers’ learning curves vary due to multiple variables such as domain-specific knowledge (Franke
& Krems, 2013a) and daily range practice (Franke et al., 2012), and may require psychological
intervention. Availability of public charging, however, is a less intrusive manner of decreasing range
anxiety with promising results; ubiquitous public charging possibilities can strongly increase miles
travelled of high-mileage drivers, whilst it can ensure that low-mileage drivers utilize their BEV range to
almost 100% (Neubauer & Wood, 2014). Accessibility of public EV charging infrastructure is therefore
indeed critical to the success of electric vehicles (Cai et al., 2014; Egbue & Long, 2012).
2.2 Predictors of charging demand
2.2.1 Points of interest
Yet what makes public electric vehicle charging infrastructure optimally accessible? To answer this
question, numerous studies have focused on transportation infrastructure network design using highly
theoretical approaches, such as Funke, Nusser, & Storandt (2014), who developed a model to place as few
charging stations as possible on any quickest path, whilst still ensuring sufficient energy supply to BEV
drivers who started with a full battery. Although theoretical approaches as in this study may significantly
contribute to the methodological discussion of infrastructure network design, they may however be
impractical or unfriendly to implement, or may neglect issues in BEV adoption in urbanized areas (Ip et
al., 2010; Wagner et al., 2014) – the latter being applicable to this study. Whilst the authors do address
the problem of infrastructure accessibility by ensuring that there are enough charging stations to
guarantee sufficient energy supply, they bypass the possibility of potential underutilization or capacity
constraints of these charging stations – it seems plausible that especially in urban areas, multiple
charging stations can still operate efficiently or are even necessary to cope with BEV demand; whilst in
rural areas, a small detour may make more sense from a station utilization perspective. Therefore, the
search for optimal infrastructure accessibility should go hand-in-hand with the challenge of charging
utilization prediction.
Expected BEV infrastructure utilization hence charging demand is explored by Cai et al. (2014), who aim
to estimate charging demand based on parking patterns from a Beijing taxi fleet and who find that
“collective vehicle hotspots are good indicators of charging demand” (Cai et al., 2014, p. 39). This thought
is confirmed by Shahraki, Cai, Turkay, & Xu (2015), who use the same Beijing taxi dataset to select
current gas station locations for electric vehicle charging, and who find that charging demand for this fleet
concentrates in the inner-city. In predicting charging demand, these authors base their findings on ICE
taxi parking demand, thereby assuming that BEV parking behavior follows the same patterns as ICE
vehicle parking behavior. This might be an overly simplistic assumption, however, as traditional travel
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patterns may not represent demand for public charging infrastructure (Cai et al., 2014). Indeed, “there
remains significant uncertainty regarding the estimation of demand for plug-in electric vehicle charging,
due to a lack of available information on PEV driver behavior” (Sathaye & Kelley, 2013, p.16). As an
example, as BEV charging usually takes longer than refueling ICE vehicles, it seems fair to assume that
charging of BEVs will more frequently happen at the end of a trip, as opposed to in the middle of a trip –
therefore imposing different requirements on recharging stations. However, although based on ICE
vehicle data, the findings of these two studies do point towards the direction of a connection between
points of interest (POIs) and charging demand – it does not take much to reason that locations proximate
to the city center attract more traffic (or, are vehicle “hotspots”), as this is where most points of interest
are located.
It is maybe for these reasons, the likelihood of higher BEV infrastructure utilization at the end of a trip
and the finding that vehicle parking density is more intense at specific locations, that some scholars
shifted their focus towards destination characteristics as opposed to trip or refueling patterns. Chen, Hall,
& Kockelman (2013) argue that BEV parking duration is influenced by activity type hence points of
interest at destination, with activities related to work, college, religion, social and recreation showing
longer parking durations than, for example, eating out or shopping. This influence of destination type on
parking duration is also recognized by Xi, Sioshansi, & Marano (2013), who in developing their charging
infrastructure location model decide to model parking locations around workplaces, universities and
shopping locations, as “trips to such locations typically entail extended stays” (Xi et al., 2013, p. 64).
Although confirming the influence of destination characteristics on parking duration, the data used by
Chen et al. (2013) is again focused on ICE vehicles which may not be one-on-one applicable to a BEV
context. Further, data is collected from a travel survey, which is usually recorded for a limited duration (in
this case, two days), hence which may not be representative of actual parking demand.
Brooker & Qin (2015) also explore the relationship between points of interest at destination and charging
demand by use of survey results yet combine this with data of actual charging behavior. The authors
determine the likelihood of recharging, at nine point of interest groups; home, work, school, medical,
shopping, social, family, transport, and meals. Of the public destinations, they find that shopping, social
and meal destinations have a high probability of recharging, where school, medical or family-related
destinations are less likely to be recharged at.
Also using data on charging station usage, Wagner, Brandt, & Neumann (2014) analyze utilization of EV
charging points in Amsterdam. Through regression analyses, the authors find significant relationships
between EV infrastructure utilization and different types of POIs within approximately half-an-hour walk
from the parking location, such as banks, hospitals or museums. Despite a relatively low predictive power
of the model, this confirms that proximity to points of interest has an impact on urban charging behavior.
However, using utilization of existing charging stations as a proxy for actual parking behavior hence
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charging demand, may not uncover the potential of other parking locations which may be more frequently
visited.
Using actual parking data within an ICE car-sharing context, Wagner, Brandt, & Neumann (2015) find
significant relationships in regression analyses between the number of ended car-sharing rentals at a
destination, and the points of interest within 1 kilometer of the destination. Although this again indicates
that points of interest have an influence on parking activities in car-sharing, the low explanatory power of
the regression model shows that there are many more aspects influencing car-sharing parking. However,
these findings may be less interesting in an electric vehicle charging station context, as the number of
ended car-sharing rentals may not be predictive of the potential utilization level of charging stations; one
can imagine that at locations where many users end their rental, the number of started rentals is also
high, making that BEVs do not have time to recharge at these locations. It is therefore “more appropriate
to focus on EV arrival and departure times from parking lots since this is when (…) charging can be
reasonably done” (Xi et al., 2013, p. 60).
An overview of above-mentioned studies that find potential influence of points of interest on charging
demand, can be found as Table 1.
Table 1. Overview studies finding influence of points of interest
Overview studies finding influence of points of interest
Author (year)
Brooker & Qin (2015)
Observed variable
Charging likelihood
Focus Method
ICE
Travel survey and
charging data
Findings related to POIs
Cai et al. (2014)
Parking duration
ICE
Traject data from taxi fleet
Collective vehicle hotspots g00d
Chen et al. (2013)
Parking duration
ICE
Travel survey
Shahraki et al. (2015)
Parking duration
ICE
Traject data from taxi fleet
Wagner et al. (2014)
Charging station utilization
EV
Charging data
Wagner et al. (2015)
Ended rentals
ICE
Rental data car-sharing
Charging needs differ per
destination type
indication of charging demand
Parking duration influenced by
activity type
Charging demand concentrates in
inner-city
Relationship infrastructure
utilization and points of interest
Relationship ended rentals and
points of interest
2.2.2 Other predictors of charging demand
Apart from points of interest, there may be other factors influencing charging demand, including
neighborhood characteristics or seasonality.
Demographic characteristics of neighborhoods may influence the charging demand of a parking location.
For example, Wagner et al. (2015) find that neighborhood characteristics such as high population density,
low income, or a high share of foreigners increase the number of ended trips for shared vehicles. Chen et
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al. (2013) find that high population density negatively influences total parking duration, whilst total
parking duration is positively influenced by a high employment density and student density.
Further, multiple studies show that seasonality influences driving behavior; daytime rain in winter and in
spring significantly reduces traffic volume (Keay & Simmonds, 2005), as well as cold and snowfall
(Agarwal, Maze, & Souleyrette, 2005; Datla & Sharma, 2008). Further, holidays or other events influence
travel volume (Festin, 1996).
Lastly, it needs no further elaboration that in a city where charging stations are already present, charging
station presence has an influence on charging demand at specific locations.
2.3 The business case of charging infrastructure
Whilst accessible charging stations appear critical for BEV adoption to ensure sufficient charging demand,
the question on who finances roll-out of an effective charging station infrastructure remains. Currently,
this question has mainly been answered by “federal, state, and local grant money, with some assistance
from automakers” (McCormack et al., 2013, p. 5). Examples include ECOtality Inc., which received $114.8
million government funding for a deployment and evaluation project of BEVs and charging infrastructure,
dubbed as The EV Project. Further private investments for this project raised its project value to about
$230 million (Car Charging Group, 2016). Similarly, charging network start-up ChargePoint recently
received a funding of $50 million, raising its total received funding to almost $150 million (CrunchBase,
2016). However, to further deploy charging infrastructure hence to stimulate BEV adoption, expansion of
private sector investment in public charging stations is necessary (Nigro & Frades, 2015). Therefore, the
EV market can only be sustainable in the long term when, amongst others, charging service operators can
recover their costs and make profit (Madina et al., 2016).
Creating a profitable business case for public EV charging infrastructure, however, is challenging due to
high investment costs, uncertain demand for available charging, and competition of home and work
charging (Madina et al., 2016; Nigro & Frades, 2015; Sadeghi-Barzani, Rajabi-Ghahnavieh, & KazemiKaregar, 2014). Indeed, public charging is still used less than other charging location types, with results
varying from 5% to 33% of charging activities performed at public charging locations by users who are
also able to charge their EV at home or work (Madina et al., 2016; McCormack et al., 2013; Rauh et al.,
2015; Speidel & Braunl, 2014). It may be due to these reasons that studies on cost effectiveness of public
charging stations show mixed results. Focusing on fast chargers, Nigro & Frades (2015) find that under
current market conditions in Washington US, public fast charging stations cannot become financially
viable without public interventions, assuming a desired investment payback of five years. This is
confirmed by Schroeder & Traber (2012), who argue that based on 2011 EV penetration levels, fast
charging in Germany is unlikely to become profitable. McCormack et al. (2013) on the other hand, are
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more positive, arguing that payback over the course of three years is possible at certain utilization levels –
however, to reach these utilization levels EV sales should continue to grow. Madina et al. (2016) conclude
similarly that public charging infrastructure can become profitable when there is enough demand.
2.4 Predictive modelling
In their paper, Shmueli & Koppius (2011) make a case for including more predictive models and testing in
Information Systems research, a field in which most studies focus on explanatory models. Whilst both
types of modelling are complementary to each other, “predictive models and testing play an important
role in assessing practical relevance of existing theories, and quantifying the level of predictability of
phenomena” (Shmueli & Koppius, 2011, p. 559) – therefore, successful prediction will add credibility to
the theories that led to it (Kaplan, 2009). Where explanatory modeling focuses on gaining understanding
to why or how phenomena of interest happen, predictive modeling aims to predict what will happen
should certain preconditions hold (Gregor, 2006).
In predictive modeling, therefore, ‘truthfulness’ of the model is of secondary importance – a true model
may even be less predictively accurate than a less-true model (Sober, 2002). Predictive accuracy therefore
also transcends issues such as correct model specification, model transparency, and multicollinearity;
factors which are predominant in explanatory research (Shmueli & Koppius, 2011). Consequently,
methods that do not assume a parametric form of the data are frequently used in predictive modelling,
such as machine learning algorithms.
Explanatory power does not necessarily imply predictive power, as statistical relationship tests common
in explanatory research provide information on generalizability of the relationship, yet do not inform us
about how well the model is able to predict new instances; however, this is often confused, leading to
studies that state predictive goals yet use inappropriate predictive modeling or testing, or studies that
have explanatory goals yet provide predictive claims deduced from explanatory models (Shmueli &
Koppius, 2011).
Of the studies describing a relationship between points of interest and potential charging demand as
presented in Table 1, Wagner et al. (2014) and Wagner et al. (2015) are the only two studies that, after
explanatory analyses, aim to extend their findings to predictions. Wagner et al. (2014) aim to assess
predictive power of the relationship between presence of points of interest and charging station utilization
by using their model to predict optimal locations for charging infrastructure in Amsterdam. However, the
authors cannot accurately assess the value of their predictions as they merely have data on actual charging
points, and not on potentially optimal locations without a charging station. Therefore, predictive accuracy
could only be determined if an optimal location would be predicted at the exact same location as an
already-existing charging station. Wagner et al. (2015) aim to predict ended ICE car sharing rentals after
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explanatory analyses by predicting data points that were not included in the training data - as is good
practice in predictive research. On a critical note, however, the test data covers one specific area of the
case city, and is therefore no random sample of the entire dataset – whilst a random sample would be
preferred to ensure that the test data is similar to the rest of the data (Shmueli & Koppius, 2011).
2.5 Summary of findings and conceptual model
Based on above, we summarize our findings and construct our conceptual model as Figure 1. Based on the
literature, we found that presence of specific points of interest has a relationship with charging demand,
which in turn influences whether or not charging station infrastructure can become cost-effective. Costeffectiveness of infrastructure through charging demand will increase the presence hence availability of
charging infrastructure, which decreases range anxiety. Eventually, a decrease in range anxiety increases
EV acceptance amongst customers hence increases the likelihood of mass adoption of BEVs. Our study
focuses on the first part of this sequence, by determining the value of using information of points of
interest in predicting cost-effective vehicle charging infrastructure locations.
Given the promising relationship between points of interest and charging demand, we construct the
following predictions in light of our research questions which will be answered using our conceptual
model:
P1. Electric vehicle charging infrastructure demand is more accurately predicted when using
information on points of interest supplementary to information on neighborhood characteristics,
seasonality and charging infrastructure presence.
P2. A model supplemented with information on points of interest can improve decision-making in
predicting cost-effective electric vehicle charging locations.
2.6 The rest of this paper
The rest of this paper is constructed as follows. We firstly will describe the used data in assessing abovementioned predictions, where after we will give an elaborate overview of how we prepared the data for
analyses. We present different ways of building the predictive model, and choose the model that shows
most promising results in light of our response variable. Consequently, we assess predictive value of a
model without point of interest information, and predictive value of a model supplemented with point of
interest information, to assess prediction 1 and prediction 2. We will discuss our findings, and conclude
with limitations of our study and suggestions for future research.
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Figure 1. Conceptual model
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3. Methodology
To effectively answer the stated research question, we describe the study’s context, the study design and
the used data. We explain how we prepared the data for analyses, and how we selected our model.
3.1 Context
3.1.1 Expected charging demand in San Diego
In this study, we aim to predict charging demand in the case city of San Diego, California, US. California is
recognized as a frontrunner in building public charging infrastructure with 20% of nation-wide public EV
chargers located in the state, whilst San Diego currently is being perceived as a projected high EV
adoption city (McCormack et al., 2013). This makes San Diego an excellent case to study potential
demand for EV charging stations.
We base demand prediction on two BEV on-the-road scenarios based on forecasts by Alexander & Gartner
(2012), who in turn use potential environmental regulations for their estimations. In the first scenario,
regulation changes little to nothing and aims for CO2 emission reduction to 95g CO2/km in 2050. In this
scenario, 2% of US vehicles sales are BEV sales – in this study, we make the assumption that this implies
that 2% of all vehicles on-the-road in San Diego are BEVs. In the second scenario, new climate goals to
reduce CO2 emission to 40g/km in 2050, stimulate BEV sales to 6% of all sold vehicles in 2020. This
number of electric vehicles on the road asks for an increase in charging stations – it indeed may be
therefore that San Diego policy makers plan to install 3,500 more public EV charging stations in the city
(San Diego Gas & Electric, 2016).
If we translate these sales percentages to the case city of San Diego, we assume 1.7 cars per household
(Governing, 2016) over a total of 493,446 households (Census Reporter, 2016). We therefore estimate
16,777 BEVs on-the-road in San Diego in the 2% scenario, and 50,331 BEVs on-the-road in the 6%
scenario. However, since we aim to predict charging demand of privately-owned BEVs, we presume that
the main portion of charging is done at home. In line with McCormack et al. (2013), we therefore assume
that 30% of charging activities is performed at public charging stations.
Please note that sub research question 1, the influence of predicted parking location demand by using data
on points of interest, will only be assessed using the 2% scenario, due to limitations imposed by
computational efficiency. However, as the 2% scenario and the 6% scenario are linearly related, we expect
that predicted demand in these scenarios is influenced in a similar manner. In sub research question 2,
where we examine decision-making using the supplemented model, we will make predictions based on
both the 2% and the 6% scenario.
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3.1.2 Charging station types
As Xi et al. (2013) note, one cannot assume that when a car is parked, the battery is loaded until full – as
vehicles may only be partially charged depending on parking time, charging infrastructure studies should
take this into account. This study therefore incorporates parking time hence potential refueling time in
making predictions on charging demand in kWh. However, the type of charging infrastructure makes a
difference in estimating how many kWh can be charged during the potential refueling time, hence makes
a difference in predicting cost-effectiveness of potential charging locations. To account for this difference,
we study potential demand for both ‘slow’ chargers and ‘fast’ chargers. Accounting for fast chargers also
overcomes the problem of popular locations with high turnover; when a parking location is visited
frequently yet for a short time per parking location, fast charging may be more beneficial from an
economical standpoint.
There are currently two fully-developed public wired charging levels; alternating-current (AC) level 2, and
direct-current (DC) fast charging (SAE J1772 US charging infrastructure standard, U.S. Department of
Energy, 2016). Although exact specifications differ amongst reports and literature, we assume that a basic
AC level 2 ‘slow’ charging station operates at an output power of 3.6 kW, with 16 amperes and 240 volts,
and is therefore able to fully recharge an electric vehicle in 4 to 8 hours, depending on the EV battery
capacity. Further, we assume a DC ‘fast’ charging station to be running at 50 kW, with 125 amperes and
500 volts, which enables a user to fully reload an EV in 20 to 30 minutes (Alexander & Gartner, 2012).
3.2 Study design
Serving the ultimate goal of charging demand prediction, the deployed study design is a cross-sectional
observational study, where observations are made without any intervention (Song & Chung, 2011). Where
in explanatory studies, experiments are usually preferred over other study designs to establish causality,
in predictive studies observational data may be favorable as “they better represent the realistic context of
prediction in terms of the uncontrolled factors, the noise, the measured response and other factors”
(Shmueli & Koppius, 2011, p. 562). In order to realistically determine predictability of charging demand
using point of interest data, a cross-sectional observational research design therefore is most suitable.
3.3 Data
3.3.1 Data on parking behavior
Car2Go
We collected data on battery electric vehicle parking behavior in San Diego from Car2Go, a car sharing
service provider that offered at the time of measuring, from April 2014 to June 2015, 300 Smart Fortwo
BEVs with 16.5 kWh battery capacity to its users. Car2Go users can rent the cars unlimitedly after a onetime sign-up fee, and pay on a use base per minute, hour, or day, as well as an extra fee after 150 miles per
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trip. In San Diego, users are allowed to park for free on-street within the Car2Go home area, and are able
to charge the car using one of the 100 public charging stations in the city.
Car2Go San Diego parking data
We retrieved parking data of Car2Go vehicles in San Diego via a private application programming
interface that was provided by the car sharing provider. We wrote a web scraper to automatically retrieve
a list of EVs available to rent from the Car2Go website, and stored this time-stamped information in a
database every 15 minutes over the course of 63 weeks. This data includes a unique car ID; timestamp (in
intervals of 15 minutes); longitude, latitude and corresponding street, zip code, and city; rating of interior
and exterior; fuel level; whether or not it is charging; and type of engine. An extract of the obtained raw
data can be found as Table 2. When a car is rented, it is not recorded in the data – only ended rentals
hence parking instances are documented. In line with San Diego city policy, we assume that when a car is
parked at a charging station (which can be inferred from GPS location), it is charged. From the data
presented in Table 2, we can infer that a car was parked yet not charged at 22.45 hrs and at 23.00 hrs. In
the following 15 minutes, the car was rented, where after it was parked again between 23.45 and 00.00
hrs. A limitation of the data includes the interval time of 15 minutes, which can lead to a situation where a
car is parked and immediately picked up within this 15-minute time period without this showing in the
data. However, we believe that this likelihood is small enough to have a negligible impact on measured
parking behavior.
Table 2. Extract raw data parking behavior
Extract raw data parking behavior
CarID
6RFN700
6RFN700
6RFN700
Timestamp
2014-10-24 22:45:01
2014-10-24 23:00:01
2014-10-25 00:00:01
Longitude
-117.15684
-117.15684
-117.13811
Latitude
32.71047
32.71047
32.70683
Street
Island Ave 856
Island Ave 856
26th St 140
Zip
92101
92101
92101
City
San Diego
San Diego
San Diego
Interior
Good
Good
Good
Exterior
Unacceptable
Unacceptable
Good
Fuel
54
54
52
Charging
No
No
No
Engine type
Electric
Electric
Electric
Car2Go San Diego operating area and parking locations
Although Car2Go users in San Diego are free to drive anywhere around the city, they are allowed to end
their rental only within the Car2Go San Diego operating area. The Car2Go San Diego operating area
consists of multiple closed polygons, where each corner point of each polygon is defined by a longitude
coordinate λ and a latitude coordinate ϕ.
From the raw data we can extract parking location addresses within the San Diego operating area,
consisting of a street name and house number. Due to occasional slight deviance in GPS accuracy, in the
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raw data multiple longitude and latitude coordinates ! and " were assigned to the same address. To
ensure that parking locations are not too spread yet not too granular, in order to still be able to capture
variations between parking locations, we modified parking locations so that each would cover a radius of
maximum 100 x 100 meter, corresponding to about λ = 0.0015 and ϕ = 0.00095. This radius is based on
Wagner et al. (2015), who tested several values of granularity and found this threshold to best balance
computational complexity and spatial detail.
We extracted 90,388 unique parking locations as visualized in Figure 2. As one can deduct from the
figure, contrary to the San Diego operating area rules, few data points lie outside the operating area –
however, as these data points are within 1000 meter from recorded points of interest in the operating
area, these data points were not removed.
Figure 2. San Diego parking locations - where red dots indicate parking locations
Data validity
As this observational data was gathered without Car2Go users directly being aware of it, the data is
expected to provide a true image of BEV parking behavior in San Diego in the context of shared cars.
Although we recognize that there may be differences in parking behavior between BEV shared cars and
BEV privately-owned cars, we believe that this difference is marginal within-city, especially due to the
‘free-float’ nature of Car2Go, where users are able to pick up and drop off a vehicle anywhere within a
designated range.
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3.3.2 Data on points of interest
OpenStreetMap points of interest
Data on points of interest located in the designated Car2Go San Diego operating area was obtained via
OpenStreetMap (OSM), a community-driven open data project that provides free geographic information.
To subtract POI coordinates, we used the osmar open-source R package. We extracted the geographic
location of a total of 1,901 POIs across 133 POI types, of which an overview can be found as Appendix 1.
An extract of the raw data can be found as Table 3. Hereafter, we merged the POIs into 24 mutually
exclusive POI categories closely corresponding to the OSM classification of points of interest
(OpenStreetMap, 2016b). An overview of POIs per category can be found as Table 4, whilst point of
interest location visualization can be found as Figure 3.
Table 3. Extract raw data points of interest
Extract raw data points of interest
POI type
Restaurant
Library
Church
Supermarket
Supermarket
Station
POI name
Thai Time Bistro
Point Loma Branch Public Library
Holy Trinity Episcopal Church
7-Eleven
7-Eleven
Fenton Parkway
Longitude
-117.2487
-117.2294
-117.2448
-117.2470
-117.2352
-117.1271
Latitude
32.74396
32.74003
32.74783
32.75198
32.74380
32.77833
Figure 3. San Diego points of interest - where red dots indicate points of interest
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Data validity
OpenStreetMap greatly values quality assurance and has several systems in
place to detect errors, inaccuracy or sparseness of data (OpenStreetMap,
2016c). However, despite these measures, the service is still based on
volunteered geographic information hence may be prone to limitations in,
for example, accuracy and completeness (Haklay, 2010). Although on US
road-mapping level, OSM performs remarkably well (with over 7 million
roads mapped compared to 4 million roads mapped in the established CIA
World Factbook (Maron, 2015)), it may be that points of interest are
inaccurately mapped due to small GPS errors, or not mapped at all
(OpenStreetMap, 2016a). Whilst the presence or absence of points of
interest is very challenging to measure, with companies getting in and out of
business every day, positional accuracy is studied in Haklay (2010), where
the authors find a 5.8 meter average difference between mapped locations in
London, UK in OSM and Ordnance Survey. A potential six-meter difference
in our study context seems negligible, as an extra six meters walking from a
parking location to a point of interest will reasonably not determine parking
location selection.
Table 4. Points of interest
categories
Points of interest categories
POI category
Accommodation
Airport
Courthouse
Education
Entertainment
F&B
F&B store
Financial
Fire station
Graveyard
Healthcare
Historic
Leisure
Office
Place of worship
Police
Post
Prison
Public transport
Shopping
Sport
Toilets
Tourism
Transportation
Count
69
3
3
100
23
639
106
65
12
3
37
7
57
16
274
1
32
2
48
232
10
29
51
82
3.3.3 Data on neighborhood characteristics
San Diego neighborhoods
Data on neighborhood characteristics is extracted from the San Diego
Table 5. San Diego
neighborhoods
Planning Department (2016). This source provides names and approximate
San Diego neighborhoods
locations of neighborhoods in San Diego – according to this classification,
Neighborhood
Count
Airport
Balboa Park
City Heights
College Area
Coronado
Downtown
Encanto
Greater Golden Hill
Greater North Park
Hillcrest
Kensington-Talmadge
Linda Vista
Mid-City
Mission Beach
Mission Valley
Navajo
Old Town
Pacific Beach
Peninsula
Southerneastern SD
1819
1654
432
483
1
15830
9
3927
20097
5733
1360
2
6534
1056
2591
2
6943
9507
12025
383
the parking locations in our study are found in 20 different neighborhoods,
as Table 5.
Data validity
The San Diego Planning Department does not provide street names or
coordinates on neighborhood boundaries; the division of neighborhoods is
merely determined by visual inspection of the provided map by this source.
It may therefore be that some parking locations are not accurately allocated
to a neighborhood.
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3.4 Data preparation
3.4.1 Response variable
In order to most effectively answer our research question, we aim to predict the potential charging
demand in kWh that can yearly be loaded per parking location in San Diego, given the scenarios as
presented in Section 3.1. This requires modification of the raw parking data as follows. A table of notation
can be found as Table 6.
A unique parking instance # is defined as follows:
# = (&, (, )*+ . . )*- , .)
(1)
Where & is a unique car id, ( is a parking location consisting of a street and house number, )* are
consecutive timestamps of 15 minutes, and . is the initial fuel level of the car upon parking. Once either
the parking location of the car changes, or if two timestamps do not follow each other in 15 consecutive
minutes, the car is expected to be rented and a new parking instance is recorded for that car the moment a
new timestamp appears in the data. Over the course of the recorded data, a total of 444,463 unique
parking instances occurred.
To determine the potential charging demand in kWh per parking instance, we firstly determine the
number of minutes 0 a car is parked per parking instance # :
01 = .234 ()*1+ . . )*1- ∗ 15) + 28
(2)
Where .234 )*1+ . . )*1- indicates the number of consecutive timestamps of 15 minutes for the specific
parking instance #. Presuming an optimistic scenario, we assume that when a parking instance occurs, the
car was parked 14 minutes before the first-recorded timestamp of # , and that the car was rented 14
minutes after the last-recorded timestamp of # .
Assuming that whenever a car is parked, it is being charged, we are presented with two mutually exclusive
possibilities; the car is either rented before it reaches full battery, or the car is parked longer than it
requires to reach full battery. To accurately determine the potential charging demand per parking
instance, the smallest kWh of these two options presents the potential kWh loaded ;<=ℎper parking
instance # as follows:
;<=ℎ1 :
(3)
#.;<=ℎ_)1 < ;<=ℎ_C1 → ;<=ℎ_)1
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#.;<=ℎ_)1 > ;<=ℎ_C1 → ;<=ℎ_C1
Where:
;<=ℎ_)1 = 01 ∗
F
60
;<=ℎ_C1 = (100 − .1 ) ∗
(4)
16.5
100
(5)
Where ;<=ℎ_) signifies the potential kWh loaded given the minutes 0 a car is parked during a parking
instance and is loaded by a charging station with F kW output power from the charging level. Similarly,
;<=ℎ_C marks the potential kWh loaded given the initial fuel level . of the car during a parking instance
– assuming a 16.5 kWh vehicle battery.
To predict the potential kWh charged per parking location ( whilst being able to control for seasonal
variance, we aggregate ;<=ℎ of all parking locations over unique location-week combinations JK . Given
90,388 unique parking locations over the course of 63 weeks, this results in a total of 5,694,444 JK , where
the 444,463 parking instances occurred in 290,241 location-week combinations. Given that the recorded
data measured parking behavior of 300 BEVs, we calculate potential charging demand in kWh over the
course of one year ;<=ℎ_L per parking location ( as follows:
Σ(;<=ℎ1 , JK)
∗ 52
63
;<=ℎ_LM =
∗ 0.3P
300
(6)
Where P is the number of expected on-the-road electric vehicles as described in Section 3.1, that charge
30% of their charging activities at a public station.
3.4.2 Point of interest predictors
To determine the optimal predictive model, we apply four different ways of framing the predictors and
eventually choose the framing that yields best predictive results. As predictor values, we used several
modifications of the distance between the 24 point of interest types in which the 1,901 points of interest
were subdivided, and the 90,388 unique parking locations. The distances between points of interest and
parking locations were calculated using the Vincenty formulae (Veness, 2015). This shortest-distance
calculation uses an ellipsoidal model of the earth, which makes its calculations more accurate than for
example formulae based on a spherical earth model – even though we are calculating short distances
hence improved accuracy is minimal.
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Not every point of interest influences a parking location; for example, few people would find it attractive
to park their car at a location that is a 30-minutes walk away from the café where they aim to have coffee.
In three out of four ways of framing the predictors, we therefore take willingness-t0-walk thresholds into
account. These thresholds are set at 100 meter, 200 meter, 500 meter and 1000 meter – the latter
maximum threshold loosely based on the maximum distance that people are willing to walk for general
purposes (Untermann & Lewicki, 1984). For the fourth way of framing the predictors, we let go of this
willingness-to-walk standard and merely look at the closest points of interest, irrespective of distance.
Framing predictors method 1: Presence points of interest willingness-to-walk
The literature presented in Section 2 gives reason to assume that the mere presence of points of interest
within a willingness-to-walk range, may influence the potential charging demand at a parking location.
Presence of point of interest categories within a willingness-to-walk range from a parking location is
therefore calculated as a categorical variable, as follows:
Q23*3R&3S, (:
(7)
#.TM,UV ≤ 2 → 1
#.TM,UV > 2 → 0
Where the presence of a point of interest category S respective to a parking location ( is determined by
whether the distance T between the parking location and a point of interest location ℎ belonging to point
of interest category S is equal to or smaller than the designated willingness-to-walk range 2 .
Framing predictors method 2: Presence frequency points of interest willingness-to-walk
In their study, Shahraki, Cai, Turkay, & Xu (2015) find that charging demand for the Beijing taxi fleet
concentrates in the inner city, where most POIs are located. The authors hereby hint towards an influence
on changing demand by not only the mere presence of points of interest, but also by the number of points
of interest that can be found within a certain range. We therefore calculate the number of times that a
point of interest category can be found within a willingness-t0-walk range from a parking location:
Q23*3R&3.234X3R&LS, (:
(8)
.234(TM,UV ≤ 2)
Where we count the frequency that the distance T from a POI location ℎ belonging to POI category S to a
parking location ( is equal to or smaller than the given willingness-to-walk range 2 .
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Framing predictors method 3: Weighted distance points of interest willingness-to-walk
Similar to the rationale presented above regarding a maximum willingness-to-walk range, one could argue
that points of interest also have a varying impact on a parking location within a willingness-to-walk range,
depending on their distance from a parking location. As an example, the bar that is located just across the
street of a specific parking location is more relevant to that location than a bar that is situated a tenminutes walk away. This also overcomes a potential drawback of method 2; if all points of interest within
a specific ratio find themselves on the far edge of the ratio (e.g., in a ratio of 100 meter, all POIs are
located 99 meter away from the parking location), in method 2 they would be weighted as if these POIs
would be 1 meter away from the parking location. We base the impact value that each POI has on each
parking location on the model proposed by Van Der Goot (1982), who linearly models the impact that a
parking area has on its destination. We inverse this relationship so to linearly model the impact that a POI
destination has on a parking location, where a destination-location has an impact of 0 when it exceeds the
willingness-to-walk range, and where a destination-location has an impact of 1 when both the POI and the
location are located within one meter from each other. This weighted distance score of a point of interest
category respective to a parking location is calculated as follows:
=3#Sℎ)3T*&Y23S, (:
Σ(
#.TM,UV ≤ 2 →
(9)
1
TM,UV
#.TM,UV > 2 → 0
)
Where a relevance score per point of interest, calculated by the distance T between a parking location
(and a point of interest location ℎ belonging to the point of interest category S, is summed.
Framing predictors method 4: Presence points of interest closeness
In method 1, we stated that based on provided literature it is likely that the presence of a point of interest
influences the charging demand of a parking location. In this method, we relax the assumption of a
willingness-to-walk boundary and merely determine the point of interest types that are closest to a
specific parking location. We therefore determine if a point of interest category is the nth-closest POI
category from a parking location, as a categorical variable:
Q23*3R&3RZU &JY*3*)S, (:
(10)
#.TM,UV = R_min(TM,^ ) → 1
#.TM,UV ≠ R_min(TM,^ ) → 0
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Here, we establish if the distance T between a parking location ( and a point of interest location ℎ
belonging to a point of interest category S , is the nth-smallest distance between a parking location ( and
all points of interest locations ` . In other words, we determine which point of interest category, of all
points of interest, is nth-closest to the parking location.
3.4.3 Base predictors
Neighborhood
We include the neighborhood of a parking location as a categorical predictor of potential charging
demand.
Seasonality
To capture influence of seasonality on potential charging demand, we include the week number in which
potential charging occurs as a categorical variable.
Charging station
During the time of measurement, 100 public charging stations were operational in the city of San Diego.
To account for a higher parking frequency based on the current availability of a charging station, we
include charging station presence as a categorical variable in the analyses.
Table of notation
Table 6. Table of notation
Variable
Description
Unit
a
c
d
f
g
h
H
i
lw
m
pkWh
pkWh_b
pkWh_t
pkWh_y
r
ts
v
z
Parking location
street and house number
Unique car ID
tag
Distance
meter
Initial fuel level of car
%
POI category
tag
POI location
tag
All POI locations
Parking instance
tag
Location-week combination
tag
Parking time
minutes
Potential kWh charged
kWh
Potential kWh charged given a car's initial fuel level kWh
Potential kWh charged given a car's parked minutes kWh
Potential kWh charged in 52 weeks
kWh
Willingness-to-walk range
meter
Consecutive timestamp of 15 minutes
index
On-the-road vehicles
integer
Output charging station
kW
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3.5 Model selection
3.5.1 Selecting predictors and method
Although the aim of this paper is to predict cost-effective locations in several scenarios, for reasons of
time-restrictions and computational efficiency we use a trial scenario to determine which way of framing
the predictors and which statistical method yields the most accurate predictions. We will select predictors
and method based on the following trial scenario:
All responses: 90,388 parking locations over 5,694,444 location-week combinations
All predictors: 1,901 points of interest over 24 point of interest categories
3.6 kW output charging stations
2% of all on-the-road vehicles are BEV
Willingness-to-walk range of 200 meter or 7-th closest points of interest*
*average number of POIs within 200 meter
On this trial scenario, we apply two different methods; Gradient Boosting Method (GBM) and Generalized
Linear Models (GLM), both using 10-fold cross validation. Given its computational power, we perform the
analyses on open source machine learning platform H2O.
3.5.2 Gradient Boosting Method
Gradient Boosting (Click, Lanford, Malohlava, & Parmar, 2015) is a non-parametric, supervised machinelearning ensemble technique that is currently described as one of the most powerful predictive methods
(Dell Software, 2015), and which can be used for both classification tasks and regression tasks. GBM is
based on boosting, where trees are added to the ensemble sequentially – each iteration, a new weak model
is trained with respect to the error of the ensemble so far (Natekin & Knoll, 2013), so that gradually the
loss function of the model is minimized. We decided to apply GBM because of, again, its promise to
deliver one of the most accurate predictions. Further, as the relationship between the predictors and the
dependent variable appears not to be linear (of which an example can be found in Appendix 2), a nonparametric approach may yield better results than a parametric approach would.
Gradient Boosting is a highly flexible method, making that the chosen parameters may greatly influence
results (Natekin & Knoll, 2013). Three parameters that may strongly influence results are as follows (Jain,
2016):
•
Maximum number of trees the model may grow. A high number of trees usually creates a
more robust model, yet the model may overfit at some point.
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•
Maximum depth a tree may grow. A high depth may lead to overfitting due to the model
learning specific relations in the sample.
•
Learning rate of the model, ranging from 0 to 1. A lower learning rate makes the model more
robust hence more generalizable.
After experimenting with different thresholds regarding the three above-mentioned criteria (with learning
rate 0.05 and 0.1 and maximum tree depths of 4, 5 and 6), we decided on a learning rate of 0.1 and
maximum tree depth of 4. For the sake of comparison and computational efficiency, we keep these two
parameters at the same level for all models.
3.5.3 Generalized Linear Models
Generalized Linear Models are a more flexible extension of traditional linear models – where the
assumptions in traditional linear models make these models restrictive to certain problems, in GLM these
assumptions are relaxed by, for example, allowing a non-linear relationship between response and
predictors (Nykodym, Kraljevic, Hussami, Rao, & Wang, 2016). We chose to test GLM due to its
interpretability; parametric approaches such as GLM are in prediction usually easier to interpret yet less
accurate. Although the distribution of the response variable is highly skewed towards zero (Appendix 3),
correct model specification is secondary in predictive analyses (Shmueli & Koppius, 2011) hence we use a
Gaussian distribution as our response is continuous and real valued.
3.5.4 Data partitioning
To ensure model validity hence model performance on cases that were not used to train the model with,
we perform 10-fold cross validation on each modelling technique. In cross-validation, the dataset is
randomly split up in k sets (here, ten), where after it iterates and tests k times where each time a different
set is chosen as holdout data, where the other sets are combined as train data. This approach is preferred
over merely one holdout set, as it better shows model validity hence predictability over previously unseen
cases. We include a random 80% of the total dataset to train and cross-validate the model on; predictions
are made on the remaining 20% of the dataset, as of now referred to as the holdout set.
3.5.5 Comparison metrics
We evaluate the predictors and methods based on three different criteria, thereby implying that these
performance measures provide the most useful information on predictive power of our model.
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1.
Absolute difference between actual and predicted response on the holdout set
2. Root mean squared error on the holdout set
3. Adjusted R-squared on the holdout set
Absolute difference
To extract how much, on average, actual potential kWh demand deviates from predicted potential kWh
demand per parking location, we derive the absolute difference between each actual response and
predicted response on the holdout set and divide this by the number of observations in the holdout set, as
follows:
aC*YJX)3T#..323R&3:
(11)
Σ|(&)X(J23*;YR*3ℎYJTYX) − ;23T#&)3T23*;YR*3ℎYJTYX)|
RYC*32P()#YR*ℎYJTYX)
Root mean squared error
Similar to the absolute difference, the root mean squared error (RMSE) measures the difference between
the actual L1 and the predicted values L1 of the response, and penalizes large differences more severely
than small differences. The RMSE is calculated on the holdout set, given the following generic
formulation:
cdef:
1
R
(12)
-
(L1 − L1 )g 1h+
Adjusted R-squared
The adjusted R-squared gives an indication on how much of the variance in the response variable is
explained by the predictors, taking the number of predictors into account. When the R-squared is given,
the adjusted R-squared on the holdout set is calculated as follows:
aTiX*)3Tce4X(23T:
1 −
(13)
(1 − c g )(R − 1)
R−;−1
Where c g is the holdout R-squared, R the sample size of the holdout set, and; the number of predictors.
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3.5.6 Overview predictors and methods
We made predictions with the different predictors and methods, using an un-optimized GBM model with
50 trees, a 0.1 learning rate and a maximum tree depth of 4, and an un-optimized GLM model with
Gaussian distribution. Please find an overview of the absolute predicted difference, the predicted RMSE
and the predicted adjusted R-squared as Table 7. Here, we find that Gradient Boosting Method
outperforms Generalized Linear Models on each metric. Further, the predictor type that yields the best
results on the three comparison metrics is method 3, the weighted distance score of a point of interest
category respective to a parking location within a willingness-to-walk range.
Table 7. Overview predictors and methods
Overview predictors and methods
Metric
Method 1
GBM
Absolute difference 84.15
RMSE
44.97
Adjusted R-squared 0.1866
GLM
85.21
49.12
0.0175
Method 2
GBM
82.95
44.12
0.1946
GLM
84.81
48.78
0.0173
Master Thesis MSc. Business Information Management |
Method 3
GBM
80.57
43.38
0.1938
GLM
83.58
47.91
0.0174
Stéphanie Florence Visser
Method 4
GBM
81.70
43.65
0.1916
GLM
83.97
48.50
0.0174
35
4. Assessment of predictive value
In this section, we will evaluate the predictive value of our models. According to Shmueli & Koppius
(2011), there are three steps in evaluating predictive models; evaluating overfitting, predictors, and
predictive power. Firstly, as overfitting is the biggest concern in predictive modelling, we assess this based
on the cross-validation MSE to ensure predictive power of the models on unseen cases. Secondly, we
evaluate the predictors and their influence on the response variable. Consequently, we evaluate the
models’ power in predicting charging demand, based on the absolute difference between predicted and
actual potential demand, the root mean squared error on the holdout set, and the adjusted R-squared on
the holdout set. Lastly, we evaluate the models’ power in predicting cost-effective locations.
In Section 3.1 we presented a 2% BEVs on-the-road scenario and a 6% BEVs on-the-road scenario. Given
that the 2% and the 6% scenarios are linearly related, predictions on one of these scenarios will yield
similar results to predictions on the other scenario. In light of computational efficiency, we therefore
assess the value of points of interest on demand predictions only using the 2% scenario, for both slow
charging and fast charging. When assessing predictive power on cost-effective locations, however, we use
our models to estimate both the 2% and the 6% scenario, again for both slow charging and fast charging.
4.1 Demand predictions base models
In the base models, we model potential kWh charging demand per parking location against the three
moderating predictors; neighborhood, seasonality, and charging infrastructure presence. We develop
different models for predicting slow charging demand and predicting fast charging demand. Due to its
superior performance as proven in Section 3.5 we use GBM with predictor values based on the weighted
distance score of a point of interest category respective to a parking location within a willingness-to-walk
range. We again hold on to the GBM learning rate of 0.1 and a maximum tree depth of 4, and start
modelling with 50 trees – we will, however, assess the number of trees to avoid overfitting for each model.
4.1.1 Base model slow charging
Assessment of overfitting
When initially fitting the base model to 50 trees with a learning rate of 0.1 and maximum tree depth of 4
on slow charging demand, the model overfits quickly as can be seen in Figure 4, where the red dots
indicate the lowest MSE per cross-validation iteration, and where the bold line shows the MSE on the
training data. After comparing performance given different numbers of trees (comparing 4 to 14 trees), we
find that a model with 9 trees yields the lowest MSE. As we can deduce from Figure 5, with 9 trees almost
all cross-validation iterations do not overfit.
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Interestingly, and counter-intuitive of what one would expect, few cross-validation metrics perform better
in terms of MSE than the training set. As expected, the cross-validation MSE (2,310.96) is still larger than
the train MSE (2,226.94). It may therefore be that although the test- and training sets are randomly split,
the training set is unusually difficult to train, or some test sets are unusually easy to predict.
Figure 4. Base model slow charging 50 trees – where red dots indicate lowest MSE
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Figure 5. Base model slow charging 9 trees – where red dots indicate lowest MSE
Predictor importance
The predictor importance graph as Figure 6 shows the impact of the predictors on the response variable,
where the predictor with the highest importance on the model is scored with a scaled importance of 1.0,
and where predictors with no influence on the model are scored with 0.0. In this model, all three
moderating predictors are deemed important, with neighborhood characteristics being most important in
predicting the response. Seasonality appears to have the least influence in this model on making
predictions.
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Figure 6. Predictor importance base model slow charging
Predictive accuracy
After fitting the chosen GBM model with 9 trees, a learning rate of 0.1 and a maximum tree depth of 4 on
the holdout set, we find predictive metric results as in Table 8. The average absolute difference between
the actual kWh demand and the predicted kWh demand is high with 82.89, compared to a mean kWh
demand of 320.4 kWh. The same applies to the RMSE of 47.6. Further, with an adjusted R-squared of
2.97%, the proportion of variance in the response variable that is explained by this base model is low.
Table 8. Predictive metrics base model slow charging
4.1.2 Base model fast charging
Assessment of overfitting
When fitting the base model with a learning rate of 0.1, a maximum tree depth of 4 and 50 trees on fast
charging demand, the model overfits quickly as can be seen in Figure 7. Comparing different numbers of
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trees (4 to 14 trees), we find that a model built with 8 trees yields the lowest MSE. In Figure 8, we indeed
find that almost all cross-validation iterations do not overfit with this model.
Figure 7. Base model fast charging 50 trees - where red dots indicate lowest MSE
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Figure 8. Base model fast charging 8 trees - where red dots indicate lowest MSE
Predictor importance
Figure 9 shows the impact of predictors on the response variable. In predicting fast charging demand, the
area of a parking location is again the main determinant of its potential kWh demand. However, where in
the slow charging base model charging station presence was a greater determinant than the week in which
the car was parked, in the fast charging base model both are almost equally as predictive of kWh demand.
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Figure 9. Predictor importance base model fast charging
Predictive accuracy
After fitting the GBM model of 8 trees with a learning rate of 0.1 and a maximum tree depth of 4 on the
holdout set, we find accuracy measures as in Table 9. With an absolute difference between predicted and
actual potential kWh demand of 111.46 and a RMSE of 58.84, the fast charging base model predicts
potential kWh demand worse than the slow charging base model. This reflects in the adjusted R-squared,
where only 2.3% of the variance in the response is explained by our model.
Table 9. Predictive metrics base model fast charging
4.2 Demand predictions points of interest model
In predicting potential kWh demand given slow charging and fast charging with a model supplemented
with point of interest information, we again use a GBM model with 0.1 learning rate and a maximum tree
depth of 4. We choose to model with 100 trees, as no overfitting takes place up to that point; although
increasing the number of trees may yield more accurate predictions, due to computational efficiency we
decide not to increase the number of trees.
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To select the best model given the predictors, we use a step-wise approach to predictor selection as is
common in predictive research (Shmueli & Koppius, 2011). To do so, we start with the three moderating
variables as in the base model, and iteratively select predictors to include in the model, where after we
assess the model on absolute difference, RMSE, and adjusted R-squared on the holdout set. Based on this
approach, we built four different models; (1) including all POIs within 100 meters, (2) including all POIs
within 100 and within 200 meters, (3) including all POIs within 100, 200, and 500 meters, (4) including
all POIs within 100, 200, 500 and 1000 meters. Please note, however, that due to computational
efficiency (the 4th model took over 9 hours to compute, given a computer with 4GB memory and 1.4 GHz
processor), we only assessed these four models based on a slow charging 3.6 kW charging scenario. Please
find our findings regarding the four models as Table 10. The more variables are fitted into the model, the
better it predicts new instances; the absolute difference decreases, as well as the RMSE. Further, the
variation in the response variable explained by the model increases. Based on these outcomes, we decided
on a predictive GBM model with 0.1 learning rate, tree depth of 4, 100 trees, and predictors based on the
weighted distance of points of interest within a 100 meter, 200 meter, 500 meter, and 1000 meter
willingness-t0-walk range. Lastly, in an attempt to increase prediction on this model, we tried reducing
the data dimensions as predictor reduction may lead to higher predictive accuracy (Shmueli & Koppius,
2011). We therefore removed the predictors that provided less than 0.02 scaled importance on the
predictors. However, reducing the number of predictors increased the model’s error hence decreased its
predicting ability (with an absolute difference of 79.19, RMSE of 41.47, and an R2 of 0.26). We therefore
decided to include all predictors in the model.
Table 10. Predictive metrics model comparison
4.2.1 Points of interest model slow charging
Overfitting
As mentioned, none of the cross-validation sets overfit at a total of 100 trees, as is confirmed by Figure 10.
As with the base model, there are few cross-validation sets that predict better than the average training
set; it may again be that either the cross-validation sets contain instances that are particularly easy to
predict, or that the training set contains instances that are particularly hard to predict.
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Figure 10. Supplemented model slow charging 100 trees - where red dots indicate lowest MSE
Predictor importance
Of the 92 predictors in the model, 25 predictors have a scaled importance on the response variable of 0.02
or higher. In Figure 11 we further inspect these variables. The variable that has the largest influence on
potential kWh slow charging demand at a parking location is the weighted distance of an F&B store within
1000 meter from the parking location. Interestingly, F&B stores within a walking distance of 100 meter
also appear as one of the 25 most influential predictors, yet with a scaled importance of just above 0.02. A
leisure activity in the neighborhood also influences the potential kWh slow charging demand; where a
leisure activity within 100 meter of the parking activity is the second-most predictive point of interest,
leisure POIs within 200, 500 and 1000 meters are all within the 25 most predictive POIs. Other predictive
points of interest include the presence of an airport within 1000 meter; a touristic activity within 500 or
1000 meter; an educational facility within 500 or 1000 meter; an F&B POI within 200, 500 or 1000
meter, or a religious institution within 500 or 1000 meter. Also adding to predictive power yet less
influential are the presence of public toilets within 1000 meter; accommodation within 200 meter; a
shopping location within 500 meter; access to public transport within 500 meter; an entertainment
activity within 200 meter; or a healthcare institution within 1000 meter.
It further is remarkable to note that even with points of interest input, the base predictors neighborhood
characteristics, seasonality and charging station presence are the third, fourth, and eight-most important
predictors of potential kWh slow charging demand. It is interesting that whilst seasonality was leastMaster Thesis MSc. Business Information Management |
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predictive of kWh demand in the base slow charging model, it significantly surpasses charging station
presence in terms of influence on the response in the supplemented model.
Figure 11. Predictor importance supplemented model slow charging
Predictive accuracy
Table 11 repeats the accuracy measures of our fitted model. The supplemented model predicts potential
kWh slow charging demand better than the base model; the absolute difference between predicted and
actual kWh demand is almost 6% lower than in the base model, whilst the RMSE is 14.5% lower than in
the base model. The supplemented model also provides a notable leap in adjusted R-squared, where it
now explains 29% of the variance in the response, compared to 2.9% using the base model.
Table 11. Predictive metrics supplemented model slow charging
4.2.2 Points of interest model fast charging
Overfitting
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Fitting a model with a learning rate of 0.1, tree depth of 4, 100 trees, and all predictors of 100 meter, 200
meter, 500 meter and 1000 meter, does not lead to overfitting given potential fast charging kWh demand
prediction as seen in Figure 12. There are again few cross-validation sets that eventually produce a lower
MSE than the MSE of the average training set, which indicates that also in this model there are instances
in the cross-validation sets that are easier to predict, or instances in the test set that are harder to predict.
Figure 12. Supplemented model fast charging 100 trees - where red dots indicate lowest MSE
Predictor importance
Although we included all predictors in the model, we examine the 25 predictors with the highest influence
on the response variable in Figure 13. The first remarkable finding is the importance of the base
predictors; neighborhood characteristics of the parking location has most influence on the response,
whilst seasonality is third-most influential, and charging station presence seventh-most important. This is
especially interesting given that these three variables alone produced a relatively badly-predicting model.
Other than that, similar to the slow-charging model, the presence of an F&B store within 1000 meter, and
of an airport within 1000 meter highly influence response. Proximity to a leisure activity is also
important; however, in the fast charging model a 1000-meter range in leisure activities has the highest
predictive power, whilst in the slow charging model, a 100-meter range has most influence. Almost all
points of interest that are of influence in the slow charging model, are also of influence in the fast charging
model; a touristic activity within 500 and 1000 meter, a religious institution within 500 and 1000 meters,
an F&B point of interest within 500 and 1000 meter, an educational institution within 200 and 1000
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meter, accommodation within 200 meter, public transportation within 1000 meter, and an entertainment
facility within 200 meter. However, in the fast charging model, also presence of an office within 200
meter, of a fire station within 500 meter, and of transportation within 500 meter influences the response
variable.
Figure 13. Predictor importance supplemented model fast charging
Predictive accuracy
Table 12 provides the predictive measures of the fast charging model supplemented with points of interest
information. The fast charging model provides better predictions in terms of accuracy than the base
model. As with the slow charging model, the absolute difference between predicted and actual potential
kWh demand is 6% more accurate with the supplemented model than with the base model. Further, we
find that the RMSE is 13.5% lower with the supplemented model compared to the base model. Lastly,
inserting data on points of interest notably increases explanatory power of the model, from 2.2% in the
base model, to 27% in the supplemented model.
Table 12. Predictive metrics supplemented model fast charging
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4.2.3 Demand prediction differences
In light of our first sub-research question and first prediction, we find that BEV potential kWh charging
demand is more accurately predicted when using points of interest supplementary to information on
neighborhood characteristics, seasonality and charging infrastructure presence.
4.3 Location predictions
Based on above-developed models, we determine how these models can be used to improve decisionmaking in predicting cost-efficient charging station locations.
4.3.1 Costs and revenue
In estimating cost-efficiency of charging stations, we must make several assumptions regarding costs and
revenue involved in charging infrastructure ownership and operation. Main costs involve hardware,
installation and maintenance; for the sake of simplicity, we exclude land costs due to their high variability
between- and within-city, as well as investment interest rates, time value of money, and potential
investments – the latter as we aim to estimate a scenario in which charging stations can operate
financially independent. Please find an overview of costs per charging station type as Table 13.
Table 13. Lifetime charging station costs $
Lifetime charging station costs $
Material costs
Installation costs
Maintenance and repair yearly
Costs yearly
Total investment costs 10 years
DC fast charging AC level 2 public
14,000
2,000
27,300
4,000
1,400
200
5,530
800
55,300
8,000
Slow charging
Charging infrastructure hardware costs significantly depend on the type of charging station and the
features it provides. In 2015, Smith & Castellano (2015) estimated AC level 2 unit costs between $400 for
a basic wall charger to $6,500 for a charger with advanced features. However, hardware costs have
significantly decreased; Alexander & Gartner (2012) found that infrastructure level 2 charging hardware
costs decreased with about 50% between 2011 and 2013. We expect these costs to keep decreasing, and
therefore estimate hardware costs of a public pedestal with basic data collection features in 2020 at
$2,000, based on lower estimates of similar hardware by Smith & Castellano (2015). Installation costs
include labor costs, material costs, permits and taxes (Smith & Castellano, 2015); these costs, however,
vary widely per geographic region, mainly due to differences in labor costs. Where average US installation
costs per AC level 2 charging station total $3,000, are costs in San Diego higher with an average of $4,000
installation costs per charging station (Smith & Castellano, 2015). Maintenance costs approximate 10% of
hardware costs per year (Schroeder & Traber, 2012). Lastly, charging station life span ranges from 10 to 15
years (Schroeder & Traber, 2012) – we therefore assume a pessimistic scenario of 10 years. Total costs
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over the course of 10 years therefore add up to $8,000. Ignoring time value of money and, as mentioned,
investment interest rates and land costs, we therefore assume yearly slow charging station costs of $800.
Fast charging
Fast charging hardware costs can be estimated at $10,000 to $40,000 per unit, depending on features
and power (Smith & Castellano, 2015). Estimating hardware costs of a public DC fast charger with basic
features yet low power output of 50 kW whilst keeping decreasing hardware costs in mind, we estimate
the costs per single fast charging station to be $14,000 in 2020. Installation costs are also significantly
higher for fast chargers than for slow chargers; where fast charging installation costs in the US average to
$21,000 (Smith & Castellano, 2015), we add a margin for higher-than-average San Diego labor costs of
about 30%, and estimate installation costs at $27,300. Yearly maintenance costs are again estimated at
10% of hardware costs, and we again assume a life span of 10 years. Excluding time value of money,
investment interest rates and land costs, we predict 10-year costs of a fast charging station to be $55,300,
hence yearly costs of $5,530.
Revenue
Currently, there are four main payment structures in EV charging; no payment (free), fixed rate (e.g.
monthly), pay per charge, and pay per used resources (Madina et al., 2016). In this study, we focus on the
latter, where a customer is charged per kWh loaded. We investigate two price levels, assuming that these
price levels per kWh equal income per kWh:
Low price level: $0.40 per kWh slow charging, $0.75 per kWh fast charging
Moderate price level: $0.50 per kWh slow charging, $1.00 per kWh fast charging
4.3.2 Predictive metrics
To assess predictive power of the models in estimating cost-effective locations, we used the following
metrics. To account for situations where a charging station is not charging due to, for example, the
parking area temporarily being unoccupied, we assume in these calculations that 1/6th of the yearly
capacity of a charging station is unused. We therefore presume the yearly capacity of a slow charging
station to be 26,280 kWh, whilst the yearly capacity of a fast charging station is presumed at 365,000
kWh.
Locations predicted vs actual
The number of predicted cost-effective locations versus the number of predicted locations that, given the
actual potential kWh demand at that location over a year, are actually cost-effective. Cost-effectiveness
per parking location is attained when:
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Slow charging at $0.40
At least 2,000 kWh charged
Slow charging at $0.50
At least 1,600 kWh charged
Fast charging at $0.75
At least 7,373.33 kWh charged
Fast charging at $1.00
At least 5,530 kWh charged
Costs infrastructure
The number of predicted charging stations, which is in our prediction equal to the number of predicted
parking locations, times the costs per charging station.
Actual charged kWh
The sum of the actual potential kWh charging demand at the predicted parking locations.
Revenue charging
Actual charged kWh times the income per kWh.
Profit all stations
The revenue from charging minus the costs of infrastructure.
Profit per station
Profit from all charging stations divided by the number of predicted charging stations.
4.3.3 Cost-effective charging locations
Using above metrics, we predict cost-effective parking locations given the different scenarios, as can be
found in Table 14. In these predictions, we examine situations where charging infrastructure suppliers can
choose to place a combination of both slow charging stations and fast charging stations.
Before analyzing predictions between the base model and the supplemented model, it firstly is interesting
to note that the supplemented model predicts to place merely one charging station per predicted costeffective parking location. Upon further inspection, it appears that in the supplemented model the
maximum predicted potential kWh demand at a parking location is remarkably lower than the maximum
actual potential kWh demand at a parking location; with a maximum of 39,172 kWh compared to a
maximum of 290,484 kWh respectively in the 2% scenario with 3.6 kW chargers, the actual potential kWh
demand is over seven times bigger than predicted. It is for this reason, the apparent under-prediction of
high-kWh locations, that some parking locations are predicted to be suitable for one charging station
whilst actual potential demand could be served with more charging stations; for example, at one parking
location, the actual kWh demand could only be reached by installing six 3.6 kW charging stations, yet the
supplemented model predicts that only enough kWh will be demanded for one station. This difference
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between maximum predicted and actual potential kWh demand is even more apparent in the base model,
where it predicts a maximum of 4,639 kWh demand in a 2% scenario with 3.6 kW chargers – which is 62
times smaller than the actual predicted potential kWh demand.
When comparing the predictions of the base model to that of the supplemented model, several interesting
findings emerge. Focusing specifically on prediction of slow charging station locations within a scenario
where an infrastructure supplier places both slow- and fast charging stations, the supplemented model
constantly predicts the number of cost-effective locations better than the base model. Depending on the
scenario, of the locations predicted by the base model, 29%, 30%, 43% and 46% are predicted correctly
(for a 2% low pricing scenario, a 2% moderate pricing scenario, a 6% low pricing scenario, and a 6%
moderate pricing scenario respectively); for the supplemented model, this is 47%, 43%, 50%, and 53%
respectively. The biggest gain in performance using the supplemented model regarding this metric is
therefore in the 2% low pricing scenario.
This superiority of the supplemented model translates to almost all profit metrics; should one place slow
charging stations at locations according to both models, one will gain additional slow charging profit with
the supplemented model of $49,139 or 2.2 times more profit (2% low pricing scenario), $28,418 or 1.4
times more profit (2% moderate pricing scenario), and $42,462 or 1.5 times more profit (6% low pricing
scenario) compared to the base model. Interestingly, however, this is not the case in the 6% moderate
pricing scenario; in this scenario, the base model just outperforms the supplemented model in terms of
profit gained by $456.95 or an increase in profit of 0.3%. Upon inspection, we find that the supplemented
model in this scenario excludes some high-kWh parking locations which the base model does take into
account; it is probable that this explains the difference in actual potential kWh demand hence profit.
Other interesting outcomes emerge when examining placement of fast charging stations in a scenario
where an infrastructure provider places both slow charging and fast charging stations. As we saw, the base
model predicts fast charging remarkably worse than it predicts slow charging – and this is confirmed by
our prediction metrics. Given low pricing scenarios, the base model does not predict one single charging
location as cost-effective – therefore significantly impacting total profit. This absence of cost-effective fast
charging prediction can be accounted for by the, as mentioned before, remarkable under-prediction of
high-kWh locations. As an example, one of the two parking locations that was predicted by the base model
in the moderate pricing scenario, promised an actual potential demand of 256,558 kWh; the base model,
however, predicts potential kWh demand for this location to be 5,694 kWh – and given that in a low
pricing scenario, a fast charging station needs at least 7,373.33 kWh demand to be profitable, the base
model excludes this potential charging location from cost-effective predictions. On the other hand, the
supplemented model performs outstanding given low pricing scenarios, as 100% of all predicted charging
locations are indeed profitable. This results in great differences in profit; should one decide not to place
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fast charging stations yet only place slow charging stations given the predictions in the base model, he
misses out on $1.7 million in the 2% scenario, and on $3.7 million in the 6% scenario.
Given the moderate pricing scenario, the base model predicts fast charging relatively well with 50% of
predicted cost-effective locations actually being cost-effective – although this is slightly deceiving, as the
actual cost-effective location is one of the top-parking locations with 256,668 actual potential kWh
demand, whilst the non-cost effective location is far from cost-effective with 723 actual potential kWh
demand. The supplemented model, however, predicts better than the base model in the moderate pricing
scenario, with 86.4% of the predicted profit-making locations actually being profitable. Also, in all
moderate pricing scenarios, the supplemented model ensures exceptionally higher profits than when
using the base model; in the 2% moderate pricing scenario, the supplemented model achieves fast
charging profits of $2.3 million, which is $2.1 million or 9.6 times more than fast charging profits made
by base model predictions of $246,221. In the 6% moderate pricing scenario, fast charging profits of $5.2
million are achieved with the supplemented model, which is $4.9 million or almost 15 times more than
fast charging profits using the base model. As fast charging profits are the main source of profit in these
scenarios, total profit of both fast chargers and slow chargers combined given the supplemented model is
$2.1 million or 7.6 times more than the base model in the 2% scenario, and $4.9 or 11 times more in the
6% scenario.
The high profits of fast charging stations may be explained by their capacity relative to their costs; whilst
fast charging infrastructure costs are yearly almost 7 times more than slow charging infrastructure costs,
its capacity can cover almost 14 times the capacity of a slow charger. As we saw before, some predicted
slow charging parking locations could have served potential kWh demand with more than one slow
charger. However, as the supplemented model merely predicts one charger per location due to the underestimation of high-demand locations, we miss out on a significant portion of potential kWh charged at
these locations. As fast chargers can cover more demand, even so much that none of the fast chargers
reaches full capacity, all demand can be loaded at locations where a fast charging station is placed – hence
resulting in greater profits.
4.3.4 Charging locations given predetermined investment
To present different applications of the supplemented model, we also examine a case in which a charging
infrastructure operator aims to place 100 slow charging stations, instead of being limited to predicted
cost-effective charging locations as in Section 4.3.3. We therefore select the top 100 highest predicted
potential kWh demand locations given 3.6 kW charging stations, and calculate the metrics regarding
profitability as Table 15.
When comparing predicted cost-effective locations with the actual cost-effective locations within this
predicted top 100, the supplemented model constantly outperforms the base model. Where in the base
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model, 12%, 24%, 34% and 39% of the predicted top 100 locations are cost-effective (in a 2% low pricing
scenario; a 2% moderate pricing scenario; a 6% low pricing scenario; and a 6% moderate pricing scenario
respectively), the supplemented model performs remarkably better with 38%, 43%, 66% and 70% of
predicted top 100 locations actually being cost-effective. This also translates to the actual potential kWh
demand per scenario; in the supplemented model, the actual potential kWh demand is in each scenario
about 2.6 times higher than in the base model.
In terms of potential profit made, the optimized model is therefore also superior to the base model. In the
2% low pricing scenario, using the base model will even result in a loss of $1,819 for 100 charging stations.
The supplemented model, however, provides a profit of $126,634. In a 2% moderate pricing scenario, the
base model does not make a loss anymore, yet $160,567 or 10 times more profit can be made when using
the supplemented model. Given the 6% low pricing scenario, the supplemented model promises $362,500
or 3.5 times more profit than the base model. In the 6% moderate pricing scenario, the supplemented
model’s predictions ensure an additional profit of $453,124 or 3.3 times more than the base model.
4.3.5 Improved decision-making
Reflecting on our second sub-research question and second prediction, we find that a model
supplemented with points of interest information improves decision-making in predicting cost-effective
electric vehicle charging locations.
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Table 14. Predicted cost-effective parking locations metrics
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Table 15. Predicted top 100 parking locations metrics
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5. Discussion
In this study, we investigated the value of using information of points of interest in predicting costeffective electric vehicle charging infrastructure locations. Whilst scholars found evidence of a
relationship between points of interest and charging demand (Brooker & Qin, 2015; Cai et al., 2014; Chen,
Hall, & Kockelman, 2013; Shahraki, Cai, Turkay, & Xu, 2015; Wagner et al., 2015), predictive power of
this relationship was still unclear. We aimed to contribute to this gap in knowledge by investigating how
predicted charging demand is influenced by using a model supplemented with points of interest
information, and consequently by determining how this supplemented model could be of use in decisionmaking regarding cost-effective electric vehicle charging locations. In this discussion, we will discuss our
findings and the methodology used.
5.1 Charging demand prediction using POI information
5.1.1 Difference in demand prediction between models
We showed that a model supplemented with information on nearby points of interest predicts charging
demand more accurately than a base model that merely covers neighborhood characteristics, influence of
seasonality and charging station presence. We measured accuracy of predictions using three different
metrics; the absolute difference between predicted and actual kWh demand, the RMSE on the holdout set,
and the adjusted R-squared on the holdout set. The difference between the base model and the
supplemented model is not remarkably large yet present; the supplemented model decreases the absolute
difference in predicting slow charging or fast charging demand with 5% and 6% respectively, and
decreases RMSE in predicting slow charging or fast charging demand with 14% and 13% respectively. A
big increase is found in the adjusted R-squared hence the proportion of variance in kWh demand that is
explained by the predictors – in slow charging prediction, the adjusted R-squared of the supplemented
model is 8 times as large as that of the base model; in fast charging prediction, the adjusted R-squared is
almost 11 times as large.
5.1.2 Large difference between actual and predicted demand
Although the supplemented model predicts potential kWh demand more accurately than the base model
does, the difference between actual and predicted values by the supplemented model is still rather large,
with the absolute difference in kWh prediction for both slow charging and fast charging in a 2% scenario
being one-fourth of the average actual kWh demand per parking location, and the RSME being oneseventh of this average. This indicates that for some observations, the predicted kWh demand and the
actual kWh demand differ substantially. This shows that the supplemented model does not perform
optimally hence that there are factors other than points of interest, neighborhood characteristics, seasonal
variety and charging station availability that influence the potential kWh demand. This is also confirmed
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by the adjusted R-squared, which informs us that the proportion of variance in kWh demand that is
explained by the predictors, is 28% in slow charging prediction and 27% in fast charging prediction.
That there are other factors influencing charging demand except from points of interest, is also reported
by other scholars (Wagner et al., 2014, 2015). Considering city infrastructure, one can think of many
factors that may influence potential kWh demand, such as difficult accessibility of the city center for
vehicles, ease of parking at a certain location, or zones that are not accessible for vehicles such as parks or
pedestrian streets.
5.1.3 Slow charging more accurately predicted than fast charging demand
Both the base model and the supplemented model predict slow charging demand more accurately than
fast charging demand, given the three metrics as described above. Given our operationalization of
potential kWh charged, potential kWh charged depends on either how long a car is parked, or on its
battery level. As fast charging infrastructure loads a BEV engine quicker, the potential kWh loaded per
parking instance at a fast charging station is, on average, more dependent on the battery level of a car
than on the time a car is parked; conversely, potential kWh loaded per parking instance at a slow charging
station is on average more dependent on the time a car is parked. It seems straightforward that points of
interest, neighborhood characteristics, and seasonality have a stronger influence on time parked, and less
on the initial battery level of a car; therefore, slow charging seems to be more accurately predictable using
our models. This rationale is confirmed by the finding that presence of a charging station has a higher
influence on predicted potential kWh demand at a fast charging station than it has on predicted potential
kWh demand at a slow charging station.
On the other hand, another explanation for this finding may be that we optimized the variables in our
model based on a slow charging scenario; a different blend of variables may have been more appropriate
for modelling potential fast charging kWh demand.
5.1.4 F&B store, leisure activity and airport most influential on predictions
Both in predicting slow charging demand and fast charging demand, the presence of a food and beverage
store (such as a supermarket), of a leisure activity (such as a park), or of an airport, is most influential.
Importance of a food and beverage store is also found by Wagner et al. (2014), who report that the
presence of a bakery has an influence on charging station utilization, and by Brooker & Qin (2015), who
find that locations related to food (including buying a meal) are amongst the most popular places to
recharge. According to Wagner et al. (2015), there is indeed a relationship between parking popularity
and presence of an airport. Lastly, positive influence of leisure activities on demand is indeed found by
Brooker & Qin (2015), yet Wagner et al. (2014) find that presence of a park does not have a significant
influence on charging station utilization.
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Although finding the strength or direction of the influence of the predictors is outside of the scope of this
study, one can reasonably argue that a supermarket is preferably visited by car due to the weight of
groceries – or contrarily, that a food and beverage store is always so close to one’s residency or work, that
there is no need to go by car. Travelling to the airport is reasonably also more convenient by car due to the
weight of a suitcase. Further, one could also imagine that when a family with small children visits a park
as a weekend activity, it is more convenient to go by car.
5.1.5 Importance of neighborhood characteristics and seasonality
Interestingly, both neighborhood characteristics and seasonality have a notable influence on predicted
charging demand even when the model is supplemented with points of interest characteristics. Although
it is, again, beyond the scope of our study to make claims regarding the strength or direction of this
influence, one can imagine potential reasons for this influence; low-income neighborhoods may for
example attract less parking activity as low-income households are less likely to be able to afford a car. In
contrast to our findings, Wagner et al. (2014) do not find significant relationships between charging
station utilization and neighborhood characteristics such as distance from the center, population density
or income per person. It may, however, be that our data does not correctly represent neighborhood
characteristics; by using the neighborhood as a predictor, influences of points of interest in that
neighborhood may be incorporated hence inflate the effect that neighborhood characteristics have on the
response variable.
Both in slow charging and fast charging, seasonality measured in weeks is within the top four of
predictors influencing potential kWh demand. Scholars indeed found evidence that weather, holidays or
events influence traditional traffic volume (Agarwal et al., 2005; Datla & Sharma, 2008; Festin, 1996;
Keay & Simmonds, 2005); this also appears to extend towards an electric vehicle context.
5.1.6 Explanatory power is not necessarily related to predictive power
Explanatory power is not necessarily related to predictive power (Shmueli & Koppius, 2011), which is
confirmed by this study. We find that points of interest that were estimated by other scholars to have a
significant influence on parking behavior, such as ATMs, banks and post offices (Wagner et al., 2015), and
police stations and gyms (Wagner et al., 2014), have a negligible influence on predicting charging
demand. This finding again highlights the importance of predictive models to assess practical relevance of
phenomena (Shmueli & Koppius, 2011).
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5.2. Decision-making using POI information
5.2.1 Better prediction of cost-effective locations using POI information
We found that when supplementing the base model with points of interest information, the rate of
correctly-predicted cost-effective locations will always be higher, with 7% to 17% better predictions in our
scenarios. This shows that a model supplemented with POI information can improve decision-making
regarding cost-effective locations. Still, using the supplemented model, about half of the predicted costeffective locations are actually cost-effective – indicating, as previously mentioned, that factors influence
kWh demand that the supplemented model does not take into account. Currently high-kWh locations are
under-predicted, making that the correctly-predicted locations attract more actual kWh demand than
predicted, hence make up for the losses made on the incorrectly predicted locations. Should a charging
infrastructure provider, however, use this model to predict merely one or two ideal locations, chances are
higher that the locations are not cost-effective hence that no profit will be made. To establish this,
however, further experimentation with different prediction thresholds is required.
5.2.2 More potential profit can be made using point of interest information
Due to the higher rate of correctly-predicted cost-effective charging locations, the supplemented model
ensures that more profit can be made than when using the base model – hence the supplemented model
can again contribute to decision-making in predicting cost-effective locations. These differences in profit
are large in each scenario given predictions of cost-effective charging locations; ranging from 6 times up
to 45 times more profits that can be acquired using the supplemented model over the base model. Large
profit differences can also be found when predicting the top 100 locations for slow charging, although less
extreme; ranging from 2 times until 9 times more profit, and even turning a potential loss with the base
model into a large profit with the supplemented model.
This large increase in profits by using the supplemented model is mainly due to the model’s superior
power in predicting cost-effective fast charging locations. It is interesting to note that however we
established that fast charging demand is more difficult to predict than slow charging demand, our model
predicts fast charging locations very accurately, with 86% to 100% of predicted profitable fast charging
locations actually being profitable. It may therefore be that the supplemented model finds it easier to
predict high-demand parking locations, yet has more difficulties in predicting moderate-demand yet still
profitable parking locations.
What is important to note, however, is that the costs incorporated in this calculation merely cover costs
for infrastructure hardware, maintenance and installation. These calculations therefore exclude land
costs, investment interest rates, time value of money, signage and other visibility measures, transaction
costs, vandalism, electrical upgrades, and advertising (US Department of Energy, 2015). Further, payback
period is set at the expected lifetime of a charging station of 10 years, yet a shorter investment payback
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period may be desired. To get a full grasp of the potential profit made per location, these costs should be
taken into account.
Still, with a per-station profit of $2,700 to $4,700 for slow chargers and $108,000 to $247,000 for fast
chargers, at 2020 expected demand levels in San Diego, both slow chargers and fast chargers have the
potential to become cost-effective without additional funding – which is in line with findings by Madina et
al. (2016) and McCormack et al. (2013).
5.2.3 Yet we lose out on profit due to under-prediction
Due to the under-prediction of high-kWh locations, the supplemented model advises to place merely one
slow charging station per cost-effective charging location – whilst at some locations, up to six slow
charging stations could be deployed to cover all actual kWh demand. Therefore, some slow charging
stations will constantly operate at 100% utilization, making that a charging infrastructure provider will
lose out on profit.
However, there may be more negative effects to consider. Range anxiety is a barrier to EV adoption, which
can only be resolved through perceived availability and visibility of charging infrastructure (Carley et al.,
2013; Neubauer & Wood, 2014). Although not studied as such, one may reason that when charging
infrastructure at a popular location is always occupied, (potential) BEV drivers may lose confidence in
their possibilities for recharging – which is harmful for BEV adoption hence for the long-term prospect of
BEVs and charging infrastructure.
5.2.4 Model applications
We showed that a supplemented model can be applied in different ways; the model shows promising
results when predicting cost-effective locations when both fast and slow chargers can be placed, and when
predicting a top 100 of best locations for slow chargers. On a critical note, due to the structural underprediction of cost-effective locations, the supplemented model is not suited to solve charging demand in a
city as a whole. When predicting cost-effective locations, actual potential kWh demand at predicted
locations in a 2% moderate charging scenario only covers 9.5% of total city demand. Even if the charging
stations at these predicted locations would constantly be utilized to their full capacity, they would still be
able to cover only 30% of total city demand. The model may therefore only be used to incrementally
choose locations to place charging stations, whilst keeping track on utilization levels per placed charging
station to determine whether or not a charging location should be extended with multiple chargers.
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5.3 Assessment of methodologies used
5.3.1 Predictive power of Gradient Boosting
In context of this study, Gradient Boosting outperforms Generalized Linear Modeling on all three
recorded metrics. This suggests that GBM is, as expected, more capable to cope with non-linear
relationships between response and predictors, making it able to better predict potential kWh demand in
this context. Despite its predictive power, a drawback of GBM is its relatively computational inefficiency.
Given that our most elaborate model took over 9 hours to compute, this gives little room for
experimentation with parameters – and as Gradient Boosting is heavily influenced by parameter tuning
(Natekin & Knoll, 2013), this may impose limitations on the eventual fine-tuning hence predictive power
of the model.
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6. Conclusion
We conclude our study by presenting our stance on the research question and findings, by determining
academic and managerial implications of the findings, and by providing limitations of the study and
suggestions for future research.
6.1 The value of points of interest information
This study aimed to determine the value of using information on points of interest in predicting costeffective electric vehicle charging infrastructure locations. Charging infrastructure placement is currently
prone to a ‘chicken-and-egg’ dilemma, where customers are only willing to adopt battery electric vehicles
once plenty public charging stations are available, yet where charging infrastructure providers are
reluctant to place infrastructure until demand is known. We believe that improved insight in potential
charging demand can help overcome the reluctance of private infrastructure investors, so that charging
infrastructure can be placed a priori actual demand is known – eventually leading to higher adoption
rates of electric vehicles. As the presence of points of interest was found to have an explanatory
relationship with charging demand (Brooker & Qin, 2015; Cai et al., 2014; Chen, Hall, & Kockelman,
2013; Shahraki, Cai, Turkay, & Xu, 2015; Wagner et al., 2015), we therefore aimed to assess the practical
value of this relationship by determining whether a model supplemented with points of interest
information would indeed have value in predicting demand.
In this study, we showed that charging demand can be more accurately predicted using a model
supplemented with information on points of interest, as opposed to using a model with information on
neighborhood characteristics, seasonality, and potential charging infrastructure presence. This, in turn,
also ensures better decision-making in predicting cost-effective charging infrastructure locations, where
structurally more profit can be made when using the supplemented model as opposed to the base model.
Depending on the scenario, we showed that the supplemented model ensures 2 to 45 times more profit
than the base model does. Although slow charging demand can be more accurately predicted by the
supplemented model than fast charging demand, biggest gain can be found in fast charging prediction due
to the underperformance of the base model in predicting fast charging locations. Further, we found that
specific points of interest have a large influence on predicting potential charging demand, such as
presence of food and beverage stores, leisure activities, or an airport. At the same time, in addition to
information on points of interest, neighborhood characteristics and seasonality still have a notable impact
on predictions. This also leads us to believe that explanatory power is not necessarily related to predictive
power; several points of interest that were predicted by other scholars to have a significant effect on
charging demand, have a negligible effect on predictions in our supplemented model. We also found that
despite the supplemented model’s positive influence on predictions and profit compared to the base
model, some predictions differ substantially from actual demand – we find that especially high-demand
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locations are under-predicted in the supplemented model, thereby not reaping potential profit to the
fullest. This indicates that despite the superiority of a supplemented model over a base model, there are
other factors influencing charging demand that the supplemented model does not take into account.
Lastly, given the contradictory findings by other scholars regarding cost-effectiveness of charging stations
(Madina et al., 2016; McCormack et al., 2013; Nigro & Frades, 2015; Schroeder & Traber, 2012), we found
that in all scenarios using the supplemented model, charging stations can be profitable given our cost and
demand assumptions.
Based on our findings, we conclude that a model supplemented with information on points of interest
provides value in terms of additional potential profit when predicting cost-effective electric vehicle
charging infrastructure locations, as compared to a model with merely information on neighborhoods,
seasonality and infrastructure presence.
6.2 Academic relevance
The main contribution of this study to the current state of academic knowledge regarding the influence of
points of interest on charging demand, is the finding that points of interest information has predictive
power in estimating potential charging demand hence cost-effective locations. We found that points of
interest indeed impact charging demand, in line with findings by other scholars (Brooker & Qin, 2015; Cai
et al., 2014; Chen, Hall, & Kockelman, 2013; Shahraki, Cai, Turkay, & Xu, 2015; Wagner et al., 2015).
However, some points of interest that were deemed to have a significant impact on charging demand
given these explanatory findings, did not have notable influence on predictions. This study therefore also
shows the importance of predictive models to assess relevance of explanatory findings. Lastly, this study
shows the predictive power of non-parametric statistical methods such as Gradient Boosting, and we
therefore hope that these methods will be further utilized in future predictive studies.
6.3 Managerial relevance
The findings of this study assist policy-makers and independent charging infrastructure operators in
determining cost-effective charging infrastructure locations, before actual demand is known. Given the
stated assumptions, infrastructure providers can better predict cost-effective locations using the model
supplemented with points of interest information, as opposed to predicting cost-effective locations merely
based on neighborhood, seasonality and other infrastructure presence. The predicted potential profit
using the supplemented model given 2020 BEV penetration levels, hopefully stimulates independent
parties to invest in charging station infrastructure, so that mass adoption of BEVs will become reality.
Lastly, our approach may also be relevant to decision-making in a non-electric vehicle context. As
predicted charging demand is related to parking time, parking facility operators or parking demand
stakeholders may use our approach to predict parking demand within-city.
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6.4 Limitations and suggestions for future research
The main limitations of this study and suggestions for future research revolve around the nature of the
used data, model and methodology selection, and actual applicability of the model.
Focusing on the data used in this study, a main limitation is the fact that parking data is gathered from
electric vehicles in a car-sharing context. Parking behavior of privately-owned BEVs may differ from
shared BEVs, hence our conclusions regarding predictability of points of interest may not fully apply to a
non-shared vehicle context. Further, neighborhood characteristics in our model proved to be influential
on predicting charging demand – yet to increase our comprehension of what exact characteristics
influence the response variable, we suggest future research to incorporate neighborhood factors
separately, such as income and population density.
Other limitations of our study revolve around model selection. In light of computational efficiency, we
tested our models on trial scenarios and with limited variation in parameter selection. It may therefore be
that our models are not optimally specified hence predictive. Further, future studies may want to
experiment with increasing the number of points of interest in the model, as full inclusion of all
parameters (points of interest within a willingness-to-walk range of 100, 200, 500 and 1000 meter)
yielded best results within our context. We also found that there are other factors influencing charging
demand, which the supplemented model does not take into account. Future research can therefore try to
optimize predictive power of the model by including, for example, factors regarding city infrastructure.
Applicability of the developed model also poses limitations. Firstly, our model is based on several
assumptions regarding the number of BEVs on-the-road, public charging versus home charging activity,
infrastructure costs and potential revenue. Interpretation of the model’s findings should therefore always
be done in light of these assumptions. In this study, we further ignore factors such as price elasticity or
shifts in demand when infrastructure is placed – which are interesting parameters to include in future
research. Additionally, even though better-performing than the base model, the supplemented model
under-predicts high-demand parking locations, which negatively influences its applicability to solving a
city’s total charging demand – which can only be solved by increasing predictable power of the model
hence by implementing additional predictors. Future research may also revolve around finding the breakeven point between charging demand and infrastructure costs.
Lastly, although this study aims to develop a model which is generalizable to other cities where actual
parking activity is unknown, generalizability towards other cities should yet be proven. It may be that
predictive power of points of interest information is not one-on-one applicable to other cities due to, for
example, cultural differences and city architecture. We therefore highly suggest future studies to apply the
developed model in other contexts.
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Appendices
Appendix 1. Extracted points of interest
Appendix 1. Extract points of interest
Extracted points of interest
POI type
Accountant
Airport gate
Alcohol store
Antiques store
Arts store
Arts center
Artwork
ATM
Attraction
Bakery
Bank
Bar
Beauty store
Beverage store
Bicycle parking
Bicycle rental
Bicycle store
Books store
Boutique
Bureau de change
Café
Car parts store
Car rental
Car repair store
Car store
Car wash
Caravan site
Carpet store
Cinema
Clinic
Clothes store
College
Community center
Company
Computer store
Convenience store
Copyshop
Courthouse
Craft store
Department store
Doctor
DIY store
Dry cleaning
Electronics store
Estate agent
Fashion store
Fastfood
Fire station
Florist
Fountain
Frame store
Fuel
Furniture store
Games store
Garden
Garden center
Gift store
Government
Graveyard
Greengrocer
Hairdresser
Herbalist store
Hospital
Hostel
Hotel
Houseware store
Information
POI category
Office
Airport
F&B store
Shopping
Shopping
Entertainment
Tourism
Financial
Tourism
F&B store
Fianncial
F&B
Shopping
Shopping
Transportation
Transportation
Shopping
Shopping
Shopping
Financial
F&B
Shopping
Transportation
Shopping
Shopping
Transportation
Accommodation
Shopping
Entertainment
Healthcare
Shopping
Education
Entertainment
Office
Shopping
Shopping
Shopping
Courthouse
Shopping
Shopping
Healthcare
Shopping
Shopping
Shopping
Office
Shopping
F&B
Fire station
Shopping
Entertainment
Shopping
Transportation
Shopping
Shopping
Leisure
Shopping
Shopping
Office
Graveyard
F&B store
Shopping
Shopping
Healthcare
Accommodation
Accommodation
Shopping
Tourism
Count
2
3
8
2
2
2
14
34
15
5
26
140
6
2
11
1
10
7
1
1
79
1
8
12
1
4
1
1
3
2
40
1
3
4
1
48
3
3
1
9
2
3
5
6
3
4
157
12
3
1
1
30
5
2
1
2
17
1
3
1
17
1
17
2
49
1
7
Master Thesis MSc. Business Information Management |
POI type
Interior decoration store
Jewelry store
Kindergarten
Kiosk
Laundry store
Lawyer
Library
Mall
Marina
Massage store
Memorial
Mobile phone store
Money lender
Monument
Motel
Motorcycle store
Museum
Newsagent
Nightclub
Office
Optician
Outdoor store
Park
Parking
Parking entrance
Parking space
Pet store
Pharmacy
Photo store
Place of worship
Platform
Playground
Police
Post office
Postbox
Prison
Pub
Railway station
Restaurant
Station
School
Second hand store
Ship
Shoe store
Social facility
Sports center
Sports store
Station
Stationery store
Stop position
Supermarket
Swimming pool
Swimming sport
Taxi
Tea store
Theatre
Ticket store
Tobacco store
Toilets
Tram stop
Univeristy
Variety store
Veterinary
Video game store
Warehouse
Wine store
Zoo
POI category
Shopping
Shopping
Education
Shopping
Shopping
Office
Education
Shopping
Leisure
Shopping
Historic
Shopping
Financial
Historic
Accommodation
Shopping
Tourism
Shopping
Entertainment
Office
Shopping
Shopping
Leisure
Transportation
Transportation
Transportation
Shopping
Healthcare
Shopping
Place of worship
Public transport
Leisure
Police
Post
Post
Prison
F&B
Public transport
F&B
Public transport
Education
Shopping
Historic
Shopping
Healthcare
Sports
Shopping
Public transport
Shopping
Public transport
F&B store
Sports
Sports
Transportation
F&B store
Entertainment
Shopping
Shopping
Toilets
Public transport
Education
Shopping
Healthcare
Shopping
Shopping
F&B store
Tourism
Stéphanie Florence Visser
Count
3
7
1
3
5
3
19
1
1
1
4
12
4
2
17
2
14
1
2
2
1
1
49
9
19
3
4
13
3
274
6
6
1
11
21
2
13
4
250
77
1
1
7
2
5
5
5
29
2
5
40
2
3
2
1
12
1
1
29
1
2
4
1
1
1
1
1
71
Appendix 2. Example non-linearity predictor and response
Appendix 2. Example non-linearity predictor and response
Appendix 3. Density response variable
Appendix 3. Density response variable
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