BIKEPRINT – IN DEPTH ANALYSIS OF CYCLIST BEHAVIOUR AND
CYCLE NETWORK PERFORMANCE USING EXTENSIVE GPS-TRACK
DATA
Dirk Bussche
DAT.Mobility and NHTV University of Applied Sciences
Paul van de Coevering
NHTV University of Applied Sciences
1.
INTRODUCTION: TRANSLATING GPS DATA INTO POLICY ENHANCING
INFORMATION
Bicycle use is on the rise in most western cities. For reasons of sustainability,
liveability, health and accessibility many local governments support and try to
strengthen this trend. Governments have invested in infrastructure and
promotion campaigns – with different degrees of success. To make these
investments more effective, thorough insight into bicycle behaviour and
network performance is needed in addition to the currently available traffic
counts and qualitative information. While this kind of quantitative information
about network performance has been available for car traffic for some time,
developments for bicycle traffic has been slow.
To reduce this knowledge gap, we developed an analysis framework which is
based on extensive GPS-tracking. The EU-sponsored1) BikePRINT2)
application, provides policy information about network performances such as:
heat maps, delays at junctions, route choice (and detours), average speeds
and the number of cyclists per day. These kinds of indicators were already
available for car traffic. However, naively applying these car-based indicators
to bicycle traffic creates paradoxical results. For instance, travel delays for car
traffic are often calculated by comparing the free flow travel times with the
actual travel times. As car speed is to a large extent determined by
infrastructure and speed restrictions, this provides a good indication of travel
time delays. However, the maximum speeds of cyclists are heterogeneous.
For instance, higher cycling speeds are often detected in peak hours –
presumably because people are in a hurry. Furthermore, while routing of car
trips is quite predictable with modern traffic assignment tools, we are just
beginning to understand the route choice decision-making process of cyclists.
These route choices may tell us more about the importance of design
characteristics such as the attractiveness of certain road segments.
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To assess what policy information, maps and analyses were needed we have
conducted interviews with policy-makers. In addition we have carried out
stated preference research on mode choice behaviour for short distances.
Furthermore, distance decay functions have been derived from traffic surveys.
This paper describes the main modules of BikePRINT and the underlying
assumptions. The next section describes the first core module that provides
information about the current network performances. Section three describes
the module that is used to assess the effects of future infrastructure scenarios.
The last section provides an outlook regarding the further development of the
BikePRINT framework.
2.
NETWORK PERFORMANCE AND BICYCLING ACCESSIBILITY
2.1 Main cycle network
Many cities define a core cycle network with higher standards of infrastructure
and maintenance (for instance early winter service). This is efficient only if
most cyclists make use of this network instead of other routes.
Comprehensive information about the distribution of bicycle traffic over the
network is often not available since cycle counts are usually performed on a
number of specific locations on the main cycle network only. GPS-tracks are
well-suited to identify important
routes outside the main cycle
network.
If the distribution of bicycle traffic
deviates from the intended core
bicycle network, policy makers
can try to identify the rationale
behind these differences and
improve the core cycle network,
or they may consider
incorporating the often used
routes in their core cycle
network.
Figure 1: Example of bicycle use outside of main cycle network
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2.2 Cycle congestion
With increased bicycle use, infrastructure capacity will not always be sufficient
and cycle congestion may occur at certain locations. For car traffic, the ratio of
peak hour speed and free flow (=night) speed is a good indicator for
congestion. For cycle
traffic this indicator
would not make any
sense. Figure 2 shows
that notably at night
people cycle slowly;
they may not feel
rushed to reach their
destination or may be
tired.
Figure 2: Speed of inner-city car (blue) and bicycle (green) trips per hour of day
To increase our understanding of bicycle travel speeds and the influence of
particular network elements, we have analysed the speed profile of single
trips. We came to the conclusion that speed variance is more important than
absolute speed. Figure 3, shows the speed profile of a cyclist who cycles
about 3 km with a nearly constant speed of 10 km/h. Traditionally, we would
mark such a track ‘red’ in the map to indicate slow speed. However the
question is if this lower speed is related to obstacles in the network or if this is
the result of someone who prefers to cycle at a lower speed or has restricted
capabilities.
Fig 3: slow cyclist
Fig. 4: average cyclist
Fig. 5: 20 km/h, high variance
Figure 4 shows a speed profile of a cyclist who cycles with a relatively
constant speed of 20 km/h for about 2,5 km. Traditionally, we would mark this
as ‘yellow’ or ‘green’ for medium/high speed. But what if the tracks in figure 3
and figure 4 are on the same bicycle path? Is it sufficient to calculate the
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average speed of both cyclists? This would be the case if we were interested
in the actual use of that path. But as our main interest is in determining the
network performance, another approach is needed.
Figure 5 displays the speed profile of a cyclist on a (different) bicycle path who
presumably wanted to cycle 30 km/h. This is indicated by the fact that he often
accelerates to 30 but is delayed repeatedly to speeds around 10 km/h.
If we aim to provide indicators for network performance, we can use the
cyclists as “human sensors” to show us where significant delays are present.
In figures 3 and 4, it seems that cyclists can cycle their desired speed,
whereas in figure 5, there is a clear delay in the sense that the cyclist cannot
cycle at its desired speed. These delays could be attributed to the moments
the cyclist gets a text message on his phone or has to check the map. But if
such a pattern is repeatedly observed across individuals, we can assume that
there is something on the bicycle network that causes these delays. To
examine the nature of these delays and provide potential solutions, policy
makers can examine these specific links.
Our aim is to find a relative speed definition that fulfils these requirements:

independent of sensor (type of cyclist); we check this by splitting the
whole dataset into the 50% fastest cyclists and 50% slowest cyclists.
The network segment level values should be as similar possible for
both datasets;

values have to be interpretable from a content view;

values on a string of segments have to sum up to the total travel time on that
route.
To achieve that goal, we tested different indicators and identified the relative
difference between actual travel time and desired travel time (derived from
local percentile of speeds and distance) as the best relative speed indicator
(figure 6).
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Figure 6: Bicycle network delays in the city of Breda, Netherlands
2.3 Selected link and routes
Once a certain network element with considerable delays has been identified,
measures should be considered to reduce the underlying problems. If a
solution on the bottleneck itself is impossible or expensive, one can consider
offering alternative routes. But then, the planner has to know the origindestination relations of users of the bottleneck. BikePRINT provides such a
map (figure 7).
Figure 7: selected link: all routes via selected link underpass
The figure shows that most cyclists of a railway underpass in the Dutch
municipality of Breda have a destination in the eastern part of the city centre.
If this tunnel would be a bottleneck it may require large investments to
increase its capacity. Instead, cyclists could also use another underpass
further east. This would require a cycle connection north of the railway but this
may be much cheaper compared to increasing the capacity of the underpass.
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2.4 Informal routes
GPS-tracks also record informal routes that are not part of the “official”
network and that are automatically identified when connecting the tracks to
the network (mapmatching), in our case OpenStreetMap. Except map- and
GPS-errors which are processed elsewhere, we find


informal trails;
crossing of parking spaces, pedestrian areas or garage yards;

crossing of lane separation lines on locations where this is not
appropriate.
Figure 8: Informal cycleway
If the policy maker identifies such informal connections, he or she can:


Officially facilitate the informal path. Figure 8 shows a dead end road
(Cluysenaerstraat) which is deliberately not connected with the crossing
because the car traffic would be difficult to handle. But there is no reason to
block cyclists too.
Block the informal path. In some cases there can be reasons not to facilitate
such a track, for instance because of safety or property status.
In such cases, one can actively block the track. Facilitating an alternative
connection should be considered in this case as the informal track shows a
need for this connection.
2.5 Waiting times on crossings
Many cities monitor waiting times for cars at controlled intersections. Much
less attention is given to waiting times for bicycle traffic. If available, cities
mostly use the average waiting time for traffic lights, derived from signalprogram. This traditional approach does not incorporate:
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
crossings without traffic signals;

breaking earlier, after seeing a red signal;


time to accelerate to desired speed again;
delays by other cyclists already waiting at the crossing.
BikePRINT calculates the waiting time in three steps. First, the realised, empirical
passage time between 50 meters before and after a crossing is determined
based on GPS tracking. In the second step a fictive passage time is calculated
for this same stretch of infrastructure, assuming the cyclist could cycle at his
desired speed. In the third step, the waiting time is calculated by taking the
difference between both passage times. Figure 9 shows the resulting map. The
colour of the circles represents the amount of delay while the size represents the
number of cyclists that are affected.
Figure 9: Delays on crossings. Colour: delay, Thickness: number of cyclists
This map enables planners to identify significant delays in the network and to
monitor their development over time. In many cases, red crossings with very
small circles are irrelevant: very few cyclists experience these problems.
Exceptions to this rule are crossings where the delay is the very reason for
cyclists to avoid it.
2.6 Detours and potential cyclists
When the quality of a certain part of the cycle network is very low, most
cyclists will probably avoid these routes. In a simple demand approach, these
routes will not be improved on short notice because of the low number of
cyclists. BikePRINT identifies these routes by comparing the actual observed
cyclists on each segment with the fictive number of cyclists we would have if
everyone would cycle the shortest route (figure 10).
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Figure 10: Avoided and preferred routes, identified by detour analysis
Network segments where more actual cyclists are observed than would be
expected from the shortest routes are coloured blue and interpreted as routes
that are attractive enough that cyclists are willing to take a detour to use them.
Conversely, the red coloured segment can be interpreted as “less attractive”:
cyclists appear to take detours in order to avoid these routes.
2.7 Isochrones
Extensive GPS tracking enables the derivation of empirical travel times
between nodes over the cycle network. Sets of geographical areas, for
instance zip-codes, neighbourhoods transportation model zones, can be used
to calculate travel times’ matrices. These matrices can be shown on a map by
clicking on one specific area (figure 11). The map will show the travel times
from the selected area to the surrounding areas.
Figure 11: Isochrones
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To make these isochrones meaningful, we also add synthetically calculated
isochrones (circles) as typical travel times for the given distance. Yellow
areas outside the yellow circle indicate few delays. Orange areas inside the
yellow circle indicate longer than expected travel times. Planners can use this
information to assess cycling travel times to important destinations such as
city centres, schools and shopping areas.
2.8 Potential bicycle accessibility
The map of potential bicycle accessibility shows per area the accumulative
number of destinations which are currently within cycling distance (figure 12).
Higher levels of accessibility indicate that people have many destinations
within reach. BikePRINT is able to distinguish between different types of
destinations including workplaces, retail stores, schools and residents. These
maps can be used for decisions regarding the location of new urban
developments such as retail facilities, schools and housing. For example,
areas with a relatively high number of residents within cycling distance may be
interesting for the development of small scale retail facilities, enabling more
bicycle-oriented urban developments.
Figure 12: Potential bicycle accessibility
The measures of travel time between origins and destinations are derived
from the empirical GPS data. This ensures an accurate measurement of the
potential bicycle accessibility based on the current bicycle network
performance and speeds. BikePRINT uses the potential accessibility measure
to estimate changes in accessibility. This measure ensures that destinations
at greater distances have a diminishing influence on the overall accessibility. It
can be described by the following equation:
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[Equation 1]
Thus, the potential accessibility of origin zone i (Ai) depends on the population
the number of destinations in the other zones (Dj) which are weighted by their
distance to zone i by F(Cij). The term F(Cij) is the distance decay function.
Different forms of distance decay function are available and the appropriate
function depends on the nature of the data. In this case, the log-logistic S
curve provided the best fit to the data. The curve is represented by:
[Equation 2]
The parameters MAX, a and b were modelled to provide the best fit to the
dataset. The travel data were derived from the Dutch National Travel Survey
(NTS) (CBS, 2015). Several decay functions were estimated for different
transport modes and trip purposes (table 1).
Transport modes
Bicycle
Car
Public transport
Walking
Trip purposes
Home-work
Home-business
Business trips
Home-school
Home-shopping
Social-recreational
Other
Table 1. Transport modes and trip purposes
To ensure that the estimates of all distance decay curves are based on a
sufficient number of observations, the NTS data from the years 2010 – 2014
are combined into one dataset which comprises more than 585.000
observations (trips). In our estimations X represents the travel time in minutes.
Observations of travel time from the NTS data were clustered in 5 minute time
intervals. The midpoint of each time interval (e.g. 2,5 and 7,5 minutes)
represents the observation of travel time. The dependent variable is the
percentage of trips in these separate travel time intervals. This is computed by
dividing the number of trips in each interval by the total number of trips by a
given mode for a given purpose. The decay functions have been corrected for
known issues in the NTS data and slightly adapted to maximize usability.
Figure 13 shows the overall distance decay functions for commuting trips for
bicycling and the other transport modes (on the left) and for bicycle trips for
different trip purposes (on the right).
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Figure 13: distance decay functions BikePRINT
3.
CHANGES IN CYCLE BEHAVIOUR IN RESPONSE TO POLICY
MEASURES
3.1 Policy support for future investments
The second core module of BikePRINT provides policy support for future
investments in bicycle infrastructure. The effects of infrastructure investments
and better network performances can be estimated by simulating fewer delays
on junctions, higher specific cycling speeds (overall network or link specific)
and the construction of additional infrastructure. The baseline situation is
based on empirical data such as travel times and delays derived from GPS
tracking encompassing the whole bicycle network. The potential effects of
infrastructure investments are shown by:
1.
2.
3.
Interactive maps, which enable visual comparisons of the baseline
situation and the new situation after infrastructural changes.
The estimated changes in potential bicycle accessibility for different trip
purposes.
The number of extra bicycle trips and overall travel time gains for
cyclists.
The interactive map in figure 14 shows the effects of a new high speed cycle
route in the Netherlands. BikePRINT users can add new infrastructure
developments to the network which enables the comparison of a multitude of
scenarios. On the left side of the map, current travel times are displayed from
their origin to all destinations. The right side shows the changes in travel time
due to the new bicycle infrastructure. As anticipated, the map shows that
travel time gains are larger for longer distances.
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Figure 14: Interactive map scenario module
In addition to the interactive map, BikePRINT provides graphs that display the
changes in overall potential bicycle accessibility, the potential number of
additional bicycling trips and overall travel time gains for cyclists (figure 15).
This graph also includes the length of the new bicycle routes. The rationale
behind this is that more extensive infrastructure investments (i.e. longer
routes) will, ceteris paribus, exert stronger effects on bicycle use. Inclusion of
the length provides an indication of the development costs although it is
acknowledged that bridges, viaducts and the like significantly influence the
total construction costs.
Figure 15: Typical output of scenario module
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3.2
Changes in potential bicycle accessibility
The changes in the overall bicycle potential accessibility are measured by the
number of different type of destinations within reach. By default, these
destinations include: workplaces, retail stores, schools and residents.
Depending on the available spatial data, the number and type of destination
types can be extended (or decreased).
Calculation of the changes in potential bicycle accessibility is based on
equation 1. The difference is that the potential accessibility for each zone is
weighted by the number of persons living in origin zone I (Pi). This ensures
that origin zones with a larger population have more weight in the calculation
of changes in the potential accessibility compared to zones with smaller
populations. This leads to the following equation (for each destination type):
[Equation 3]
The total weighted potential accessibility per person in the study area is
calculated by adding up the weighted potential accessibility of all zones and
dividing this by the total number of persons in the study area. Or:
[Equation 4]
This bicycle accessibility is calculated for the baseline situation and for the
other scenarios; in figure 15 the difference between both is presented. Hence,
the figures for the potential accessibility can be interpreted as the average
number of destinations (per subtype) that is within bicycle distance of the
residents of the whole study area.
3.3 Extra cyclists and travel time gains
In addition to changes in potential bicycle accessibility the ambition was to
provide estimates for the number of extra cyclists and the overall travel time
gains for cyclists after improvements in the bicycle infrastructure. Here we
were confronted with a dilemma. On the one hand, BikePRINT is designed as a
quick scan which limits the amount of time for data processing and model
complexity. On the other hand, a precise and accurate estimate of these
effects would ask for a much more complex multimodal traffic model. We
decided to keep the modelling structure straightforward and provide global but
robust estimations of effects. This provides sufficient accuracy to enable the
comparison of effects of different scenarios.
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To estimate the number of extra cyclists and travel time gains a distribution of
cyclists over the pairs of origins and destinations is required. This distribution
is based on a single (production) constrained gravity model:
Tij = k (Oi Dj) / F(cij) [Equation 5]
where Tij is the number of trips from origin i to destination j, Oi is the number
of trips originating from zone i and Dj is the number of trips terminating at
destination j. The scalar, k, is simply the ratio of the maximum possible trips
(total production) to the calculated number of trips using the unconstrained
gravity model. The function F(Cij) is estimated to allow the model results to
match the earlier mentioned distance decay function.
The production and attraction factors are derived from the NTS dataset (20102014), described earlier in this section. Depending on available data sources,
these factors can be calculated for different trip purposes (e.g. commuting,
shopping, attending school/education, visiting friends and family). The
production factors are calculated by dividing the total number of trips for a
certain purpose by the size of the related subpopulation in the study area. For
instance, the production factor for commuting is calculated by dividing the total
number of commuting trips by the size of the working population. The
attraction factor for shopping is calculated by dividing the total number of
shopping trips by the total number of jobs related to this branch.
Based on these factors, the overall production and attraction of all zones is
calculated for all travel modes together. In the second step of the modelling
process, the share of bicycle trips in overall estimations for production and
attraction is determined. In reality, the share of bicycle trips is determined by a
multitude of factors including population characteristics, travel related attitudes
and built environment characteristics such as densities and the availability and
the quality of bicycle infrastructure (Bohte, 2010; Ogilvie et al., 2011).
However, data availability often limits the number of determinants that can be
used. Therefore, the relative share of bicycle trips is based on the level of
urbanisation at the four-digit postal-code area level (overall density measure
including inhabitants and work, schools, retail shops, leisure, etc). The bicycle
shares are for each also derived from the NTS 2010-2014 dataset by
calculating the production and attraction specifically for the bicycle. The
overall production and attraction of all zones is multiplied by the share of
bicycle trips for the corresponding level of urbanisation. This results in the
(unimodal) production and attraction of bicycle trips for each zone. Even
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though this single determinant seems to provide a relatively crude estimation
of the bicycle share, it should be noted that many other determinants such as
attitudes and population characteristics are also related to the level of
urbanisation. To a certain extent, the level of urbanisation also acts as a proxy
for these determinants.
Next, the gravity model is used to estimate the origins and destinations of the
trips (Tij) per purpose. This results in a origin-destination (OD-)matrix that links
each bicycle trip from an origin to a specific destination zone. This matrix is
used to calculate the overall travel time gains for the existing bicycle trips. For
each OD pair, the differences in travel time are multiplied by the number of
bicycle trips. The total travel time gains are calculated by adding up the travel
time gains on all OD pairs. These travel times are incorporated in the graph in
figure 15.
Note that we do not consider potential distribution effects in this calculation. In
other words, we assume that cyclists will not change their destinations due to
the improvements in the bicycle infrastructure.
However, a certain number of travellers may decide to switch travel mode
following the bicycle infrastructure improvements, resulting in a higher number
of bicycle trips on the related links. As a full-blown multimodal traffic model is
not possible in this quick scan framework, these potential mode shifts are
estimated by applying ‘competition curves’. The bicycle competition curve
describes the share of bicycle trips in the total amount of travel depending on
the travel time. Figure 16 shows the overall competition curve and specific
curves for trips with education and commuting purposes. The competitive
position of the bicycle decreases as time intervals become larger.
Furthermore, comparison of the individual curves shows that they significantly
differ per trip purpose. In line with the calculation of the production and
attraction, these curves are estimated for different levels of urbanisation at the
four-digit postal-code area level.
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Figure 16: Bicycle competition curve for different trip purposes
The potential number of modal shift is calculated for each OD pair by dividing
the current number of bicycle trips by the bicycle share from the competition
curve that is related to the specific travel time on that link. Next, this outcome
is multiplied by the (higher) bicycle share that is related to the new (shorter)
travel time on this link after infrastructure improvements. This result provides
an indication for the number of bicycle trips in the scenario situation. The
resulting modal shift is calculated by subtracting the number of bicycle trips in
the baseline situation from the number of trips in the scenario situation.
Finally, these results for each OD pair are added up to provide the total
number of additional bicycle trips.
4.
CONCLUSIONS
Extensive GPS tracking of cyclists can give thorough insight into travel
behaviour and bicycle network performance. Information such as preferred
(and avoided) routes, speeds, delays and accessibility can make bicycle
planning and policy more targeted and efficient. When developing BikePRINT,
we learned that many current road network performance Indicators make no
sense for cyclists. To reduce this knowledge gap, dedicated indicators for
cycling have been developed.
Although extensive GPS tracking provides a rich additional source of
information, it is neither intended nor suitable to replace traffic models or
count data. We consider both sources of information as complementary. GPS
tracking will provide detailed insight into cycle behaviour and route choices but
they are less suited to estimate the total number of cyclists due to the limited
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sample. Therefore, we aim to combine both data sources in the near future
which would provide better estimates of the distribution of cyclists over the
entire network. Nevertheless, we hope that the current version of BikePRINT
already contributes to better bicycle planning and policy and will be used to
increase cycle use in many cities.
REFERENCES
Bohte, W. (2010) Residential Self-selection and travel: The relationship
between travel-related attitudes, BE characteristics and TB, Doctoral
dissertation, Sustainable Urban Areas 35 (Delft: IOS Press).
CBS (2015), Onderzoek Verplaatsingen in Nederland (2013)
Onderzoeksbeschrijving OViN 2010–2014. Data Archiving and Networked
Services (DANS). Accessed 1st June 2015 http://www.cbs.nl/nlNL/menu/themas/verkeervervoer/methoden/dataverzameling/korteonderzoeks
beschrijvingen/ovinbeschrijving-art.htm.
Ogilvie D, Bull, F., Cooper, A., Rutter, H., Adams, E., Brand, C., Ghali, K.,
Jones, T., Mutrie, N., Powell, J., Preston, J., Sahlqvist , S. and Song Y. (2011)
Evaluating the travel, physical activity and carbon impacts of a ‘natural
experiment’ in the provision of new walking and cycling infrastructure:
methods for the core module of the iConnect study, BMJ Open, 2012,
2:e000694.
1)
BikePRINT is part of the Nisto project, which received EU Interreg IV B
funding
2)
www.bikeprint.eu
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