The Effects of Mobile Real Time Information On Rural Passengers
Konstantinos Papangelisa*, John D Nelsonb, Somayajulu Sripadab, Mark
Beecroftb
of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool
University, CN
bdot.rural Digital Economy Research Hub, University of Aberdeen, UK
aSchool
Abstract
Mobile real time passenger information (RTPI) systems are becoming ubiquitous in public
transport and a plethora of studies have explored the effects they have on passengers.
However, these studies mostly focus on urban areas and largely ignore rural dwellers.
In this paper we present results of a study that looks into the effects that mobile RTPI has on
passengers in rural areas. The results indicate that the participants primarily used the mobile
RTPI system to gain situation and geospatial awareness and to adapt their travel behaviour in
disrupted circumstances. Further, we have identified that mobile RTPI significantly affects
the everyday public transport travel of individuals. The outcomes of this study provide an
initial understanding of the effects of a mobile RTPI system on rural users.
Keywords: Real Time Passenger Information Systems, Effects of Real Time
Information, Rural Transport.
Introduction
Mobile real time passenger information (RTPI) systems are becoming ubiquitous in
public transport. The transport community has studied them extensively from various
perspectives, and a plethora of studies have explored the effects they have on passengers (e.g.
increased willingness to pay, improved perceived waiting time, and improved satisfaction and
image). However, these studies mostly focus on urban areas and largely ignore rural
dwellers. In this paper we present results of a study that looks into the effects that mobile
RTPI has on passengers in rural areas. The study involved: i) the creation of a mindmap of the
possible effects of information to individuals; ii) the creation of a technology probe; and iii) a
before-and-after intervention study in the Scottish Borders exploring the possible effects of
real time information utilising a mobile RTPI system developed as part of a wider project
known as the Informed Rural Passenger (IRP) 1 2 . The results indicate that real time
information has the potential to significantly affect the everyday public transport journeys of
rural passengers. More specifically, the participants of our study reported that the RTPI
affected their perceived control over their journey, reduced their waiting time and increased
their willingness to pay for the service. They also reported that the technology made the
service easier to use, improved perceptions towards the service, and had an impact on their
decision making. Overall, the results of the study provided evidence that real time
information has the potential to significantly affect the journeys of rural travellers.
Review of relevant literature
The availability of real time information for travellers is becoming more and more
widespread. Passenger attitudes towards RTPI systems are generally very positive. Several
1 http://www.gettherebus.com
2
http://www.dotrural.ac.uk/irp/
* Corresponding author. Email: [email protected]
studies have been conducted on various RTPI systems (mobile, at stop, web, etc.) and almost
all report that RTPI significantly affects the passengers. Unfortunately, the literature does not
usually provide sufficiently complete information about methods employed in studies
conducted to allow an overall valid investigative framework. Therefore, it becomes apparent
that not all of the possible effects of RTPI are taken into account in some previous studies.
Based on the literature review we have identified that the principal effects of RTPI on
passengers are the following: reduced perceived waiting time, willingness to pay, adjusted
travel behaviour, positive psychological effects, mode choice, customer satisfaction and
image.
Reduced perceived waiting time
Several studies have reported reduced perceived waiting time as a positive effect of
RTPI (e.g. Dziekan and Sedin 2005; Watkins et al, 2011). For example, Watkins et al (2011)
showed that OneBusAway (a real time information system deployed in Seattle) has decreased
the perceptions of wait time for 91% of its users. The perception of time is usually estimated
by asking passengers at stops and stations to estimate how long they have been waiting.
Willingness to pay
Several studies have illustrated an increased willingness to pay after the deployment
of RTPI systems. For example, Wardman et al (2001) report that passengers value real-time
information at interchange terminals as equal to 1.4 min in-vehicle-time. This measure can be
recalculated into monetary willingness-to-pay (about 5 British pence per journey). However,
this is subjective to the type of system as findings from Dziekan and Kottenhoff, 2007;
Schweiger, 2003) (2004) illustrate that not all systems are valued equally and not all travellers
are willing to pay for such a service. The main argument here is that the traveller expects that
the public transport provider should supply this information free of charge. Nevertheless, an
increased willingness-to-pay is a possible effect of RTPI. It is worth noting that different
evaluation methods trigger different results. This does not necessarily mean that the different
studies contradict each other. It is more likely that respondents have answered questions that
are qualitatively different. Willingness to pay is usually measured using stated preference
experiments (Louviere et al, 2000).
Adjusted travel behaviour
The term adjusted behaviour aims to congregate observations regarding the
adaptation of the behaviour of individuals in RTPI enabled travel environments. Possible
measurement techniques include behavioural observation, interviews and questionnaires. The
following three adjusting strategies are the most prevalent in the literature: reduction of disutilisation of waiting time, decisions leading to more efficient travelling (e.g. taking an
alternative bus from a different stop that arrives close to a destination), and other adjusting
strategies (e.g. letting a crowded bus pass by, adjusting walking speed) (Dziekan and Sedin,
2005).
Positive psychological effects
Positive psychological effects can be studied by asking questions relating to the
feelings and experiences of the individual. The most common methods used are interviews,
questionnaires and focus groups. Positive psychological effects relate mainly to perceived
control, feelings of security, reduced uncertainty, and increased ease-of-use (e.g. Schweiger,
2003).
Mode choice
Mode choice effects occur if the attractiveness of one mode increases. However,
Dziekan and Sedin (2005) illustrated that other factors, such as travel habit or attitudes
towards a mode of transport are more important. Furthermore, Mishalani et al. (2006) found
that only a very limited number of passengers thought that RTPI would change significantly
their way of travelling. Effect on mode choice is usually observed in stated preference studies.
Customer satisfaction and Image
Overall higher customer satisfaction has been observed in various studies. Customer
satisfaction is influenced by a multitude of factors including: social norms, attitudes toward
the technology etc. The evaluation process may be cognitive, affective, personal or social.
Customer satisfaction is usually measured through interviews, questionnaires and focus
groups. Further, RTPI improves the attractiveness of public transport (Dziekan and Sedin,
2005). Similar to customer satisfaction, image is a complex construct that is influenced by
many factors. It is usually measured through various quantitative and qualitative methods
such as interviews, questionnaires and focus groups.
In this research the outcomes of the literature review informed the creation of a
mindmap that describes the interrelations of the possible effect of RTPI.This was used to
guide the thematic areas of the questions included in a before and after study using a mobile
RTPI system. It should be noted that due to lack of RTPI systems in rural areas, most of the
studies that were taken into account when designing the mindmap took place in urban areas.
This does not present a problem in an exploratory setting such as ours as the literature
indicates that the possible effects of RTPI are the same and in some cases are exhibited more
strongly in rural areas (Lehtonen and Kulmala 2002). Figure 1 illustrates the mindmap
developed as part of the study.
Figure 1. Mindmap of possible effects of real time information as emerged from the literature
review
Methodology
The methodology for the research involved two main activities, the co-designing and
development of a technology probe, and a study that aims to explore the effects of real time
information on rural passengers. To the best of our knowledge there have not been any studies
exploring the use of rural mobile RTPI systems or implications for the design of such
systems.
Co-designing the technology probe
For the design of the technology probe study we employed two co-design sessions
with two different groups of rural passengers. The first was conducted on the Isle of Tiree in
the Inner Hebrides of Scotland and the second in the Scottish Borders. Each session lasted an
average of two hours and thirty minutes and had 15 participants (Isle of Tiree 8 female and 7
male, Scottish Borders 9 female and 6 male) with an average age of 25 years old. Most of the
participants were familiar with journey planning technologies and all had used them in the
past. The participants were recruited through flyers and emails. All the participants in the codesign sessions were dependent on public transport, and both groups had individuals that
were familiar and less familiar with the area, and the available services.
After the initial discussion we asked the participants to generate approximately 10
stories based on their experiences, and use these to identify potential functions for a
smartphone application that they believed would help them address the issues in their stories.
Both groups came up with multiple functions ranging from social media extensions to
multiple visualisations. Then we asked them to design in groups various screens of an app
illustrating the functions. We also asked them to provide a written description of the function
and how it would be used. After this exercise, we brought the groups together and asked them
to discuss and compare their designs. Through this, a final design emerged for each co-design
session. Both co-design sessions produced a similar set of functions and designs. We treated
the outcomes of the two co-design sessions as design exemplars and based on them we
designed the initial version of the technology probe study.
The testing of the technology involved laboratory testing as well as testing in the
field. The laboratory testing involved an extensive stress test of the back end services over a
period of 3 days, as well as riding buses while using the probe to test the front end
(responsiveness, map matching, visual glitches etc). The field testing took place on a bus
route in Aberdeen, Scotland, and was conducted as part of the IRP project. The testing in the
field involved eight participants, with a mean age of 29 years old. The aim of the study was
to test: a) the usability of the design; b) the suitability of the technology probe; c) the frontend
and backend services, and d) the information sources we were using (timetables). After the
field testing we conducted a focus group with the participants. During the focus groups we
discussed design issues, the participants’ experiences using crowd-sourcing to capture and
disseminate public transport information, as well as issues/difficulties they encountered
during the study. Figure 2 illustrates a screenshot of the technology probe in action during
the main study.
The design of the technology probe aims to provide RTPI that is open to
interpretation by the users. This intends to enable the user to appropriate the technology probe
for use in real world settings depending on their needs and orient the research to “what
happens on the ground” drawing out of design principles or recommendations based on users
natural reactions (Baillie, 2001).
Examining the effects of RTPI on rural passengers
The study took place along the A7 corridor in the Scottish Borders, which is mainly
served by one bus service. The service operates between Edinburgh and Carlisle via the town
of Hawick, covers a distance of approximately 100 miles, and passes through areas ranging
from urban to remote rural. The service mainly serves two types of passengers: (a) travellers
that use the route from Carlisle to Edinburgh as a cheap alternative to the train service; and
(b) locals that use the service for short trips for various purposes (education, commuting,
shopping, entertainment, etc.). There are three main bus stations along the route; Edinburgh,
Galashiels and Carlisle. For the study we focused on the rural town of Galashiels (population
22,500) as it has the only rural bus station along the route, which acts as a main transport hub
for many buses from rural areas around the Scottish Borders.
As part of the research we conducted a before and after intervention study examining
the possible effects of RTPI based on the mindmap as emerged from the literature review
(Figure 1). Before and after intervention studies aim to explore whether an intervention has
affected a sample population. The pre-tests involve measuring the variables before the
introduction of the intervention in order for the researcher to establish baseline values. The
post-tests measure the same variables as the pre-tests after the group has experienced the
intervention. For this study we utilised a non-experimental before-after design approach to
examine the effects of real time information on rural passengers. In order to recruit
participants for our study we focused on increasing awareness regarding our research in the
area and building rapport with the community. We did that by: i) putting up posters in all the
main bus stations and bus stops along the route, education establishments, and public bulletin
boards; and ii) setting up a stand in various bus stops and handing flyers presenting the
technology probe to the various passengers, and discussing informally with them issues they
have regarding their travels. From the available pool of candidatres we chose 15 individuals
depending on: i) their frequency of usage of public transport; ii) their familiarity with the
area, iii) their purpose of travel, and iv) their broad socio-economic characteristics. Table 1
illustrates the number of participant journeys throughout the week during our study.
Table 1. The number of journeys undertaken by the study participants.
One can argue that the number of participants we recruited for the study prohibits us
from meaningful conclusions. However, for an exploratory setting (such as this) with the
objective to investigate and describe phenomena as consciously experienced, the literature
indicates that a small number (5 to 25) of participants is adequate to derive meaningful
conclusions, especially when the participants have homogeneous broad socio-economic
characteristics and experience similar situations (Bertaux, 1981; Guest, 2006)
As soon as the shortlisting of the potential participants finished we started the preintervention interviews. The interviews took place in the Student Union of the Scottish
Borders College and involved 15 participants (7 male, 8 female) with average age of 24.3,
and lasted approximately forty-five minutes. They started with general questions about their
journeys and experience during disruptions and continued with questions structured around
the six possible effects of real time information as emerged from the mindmap of possible
RTPI effects. After the end of each interview we presented the technology probe to the
participants, showed them how to use it, and installed it in their phones.
The intervention period started a day after the last interview and lasted 18 days
because we wanted to give enough time to the participants to become familiar with the
technology probe and make it part of their everyday travel. In order to monitor how much
each participant uses the probe in their everyday journey we used a daily score system based
on the number of data point observations they provided via the location provision function of
the technology probe. We used the biweekly average of the data point observation to send
reminders via text messages to the participants with low numbers of observations. This helped
to keep the participants actively engaged with the study. The post intervention questions took
place again at the Student Union of the Scottish Borders College two days after the
intervention period ended. They included the same questions in the pre-intervention
interviews, and lasted on average an hour and a half.
Figure 2. The technology probe in use during our study in the Scottish Borders
During the intervention study we actively observed six of the participants in their
everyday travel for seven days. Three of the participants were frequent (2 male, 1 female) and
three were infrequent (3 female) users of the bus service. The average age of participants we
observed was 23.6, and each observation lasted approximately three hours. The observations
involved following the participants door-to-door from various points of origin to various
points of destination. We conducted at least 5 observations per participant over a period of
two weeks. Examples of observations include: i) how the participants use (or don’t use) the
probe in their everyday life; ii) how they use the probe when there is available RTPI, and how
they use it when there is not available RTPI; and iii) how the information that the probe
provides affects their travel decisions.
It should be noted that the study involved relatively young passengers with high
technological literacy, as such the results may differ for passengers in other rural areas, older
passengers, or for passengers with lower technological literacy. This is also illustrated by the
literature. For example, Gault et al (2014), found that passengers over 60 prefer text
messaging rather than smartphone applications for receiving public transport passenger
information.
Data analysis procedures
The analysis procedures involve content analysis on the before-and-after intervention
interviews. Initially we conducted open coding where we looked at the data and wrote down
ideas, concepts, and comments about them. Then, the analysis was split in two distinct
processes.
The first process aimed to explore the effects of RTPI on rural passengers. It
involved: i) transcription of the data verbatim; ii) sorting the data in clusters against the
mindmap of possible effects of RTPI; iii) separating the data from the context by noting the
most salient statements of each participant for each possible effect of RTPI; and iv)
conducting interrogative hypothesis testing through negative case testing. The second process
aimed to explore how rural dwellers use mobile RTPI systems. It involved axial coding,
which looked into: i) how, under what context and what causal conditions individuals use the
system; and ii) the actions, interactions and consequences that occur through the use of the
system. At this stage of the analysis relevant theoretical perspectives were introduced (such as
the information required and the recovery phases during disruption) to further explore the
issues at hand.
In order to ensure reliability we used 4 independent coders to examine our analytic
interpretations during our analysis. We calculated the inter-coder reliability of the coders
based on the Cohen’s Kappa index and then calculated the Kappa coefficient across the coder
pairs using average P(e). This helped us to ensure that: i) our claims and assertions were not
derived from a misreading of the data; and ii) that our data has been documented adequately.
The agreement between the independent coders was measured by the Cohen’s κ index
(Cohen, 1990). The K(m) K m =0.748 (with 95% confidence intervals .104 to .232) and
p<.0005. According to Landis and Koch (1977) a kappa that is > 0.7 but <0.8
represents good strength of agreement and the confidence interval indicates that the coding is
not random and is reliable, and is in line with previously published intercoder reliability
estimates obtained from coding similar constructs in previous studies. Furthermore, the
marginal distributions of coding did not indicate prevalence or bias problems, suggesting that
Cohen’s kappa was an appropriate index of inter-coder reliability.
Effects of RTPI on rural passengers
Upon finalisation of the technology probe we initiated the study in the Scottish
Borders. The effects of real time information on rural passengers as emerged from the beforeafter intervention study are summarised in Table 2 and discussed below.
Potential effects of RTPI on rural
bus passengers*
Perceived Control
Perceived waiting time
Willingness to pay
Bus service easier to use
Perceptions towards the service
Mode choice
Impact on decision making
Frequent users
8/8 (100%)
5/8 (63%)
6/8 (75%)
8/8 (100%)
6/8 (75%)
0/8 (0%)
6/8 (75%)
Infrequent
users
7/7 (100%)
6/7 (86%)
7/7 (100%)
6/7 (75%)
6/7 (75%)
1/7 (14%)
7/7 (100%)
Table 2. The effects of RTPI on rural bus passengers
Total
15/15 (100%)
15/15 (100%)
13/15 (86%)
14/15 (93%)
12/15 (80%)
1/15 (6%)
14/15 (93%)
* Users were asked to explain how the RTPI system improved their travel experience in
relation to each of the parameters below.
Perceived control
Increased control relates to the passengers’ perceptions of having power over their
journey. It has been routinely clustered in the literature under the positive psychological
effects category. Various terms are used to demonstrate this effect in the literature such as
“stress reduction, “increased reliability” and “reduced uncertainty”.
The participants, both frequent (8/8), and infrequent (7/7) felt that they were not in
control of their journeys. This is illustrated by the following quote from a frequent passenger
“I am not sure. There might be delays or they (sic) might not be. Usually around this time of
year the weather is good so I don’t think they [sic] will be any issues with the service [...]
However, I still go to the bus stop 40 minutes early just to be on the safe side”. Infrequent
passengers not familiar with the route and the service have even lower levels of control. This
is vividly illustrated by the following quote by one of the participants in our study who had
just started using the bus service to commute to university twice per week “I only feel certain
when I take the bus from the Edinburgh station […] after that I feel lost […] I don’t know
where I am! […] On my way back is even worse! I don’t know if the bus is coming, when the
bus is coming, when I will arrive and even if I will arrive!” When further probed it became
apparent that frequent users feel uncertain and lack control due to previous experiences, as do
infrequent users due to the unknown and hearsay. The former is illustrated by the following
quote “I don’t feel in control at all! Actually, I feel super uncertain […] I have clothes in 3
different places, in Galashiels, in Hawick and in Melrose. I don’t think they fit me anymore!
[laughs] […] I haven’t used them in a couple of years […] However, I still keep them in my
friends’ houses just in case, as a few years ago I got stuck for two days in my friend’s house
in Melrose […] the bus was not running due to bad weather.” The following participant
assertion illustrates the latter “I only use the bus for 3 weeks. I never had a delay or any other
issue […] I don’t feel confident at all when using the [bus] […] actually I feel very stressed
[…] as I heard that it usually runs late and that some people got stuck at the uni and could
not go back home! I don’t want that to happen to me.”
Furthermore, the lack of control affects their perception of reliability of the service as
they consider it to be a “necessary evil” and if they “had alternative they would (sic) stopped
using the service once and for all”. This is exemplified by the following quotation by a
participant that had to relocate from Edinburgh to Galashiels due to issues he had with the
service: “The [service] is the worst bus I have ever used in my entire life. It is expensive. It is
dirty. And it is never on time. I start failing classes at uni because I would go in late due to
the bus running late or early or not running at all! I can’t drive due to a disability […] but if I
could I would buy a car in an instant […]”.
Post-intervention both frequent (8/8) and infrequent (7/7) participants when asked the
same questions unanimously replied that their overall perception of control has improved.
This is illustrated in the following two quotes. The first quote is from a participant that uses
the bus multiple times a day to commute to work and to the University. “The app makes my
life so much easier. It makes me much more confident to use the bus […] and I can see where
it is […] I don’t have to wait for the bus in the bus stop half an hour early […] I can wait at
my home or go grab a coffee”. The second quote is from a participant that uses the bus two or
three times a week. “I feel much more in control now. I know where to get on and off […] I
mean even if no real time information is available I can still see where the bus is according to
the timetable […] It has made taking the [bus] a much more enjoyable experience”. Through
this and the previous two quotes it becomes apparent that the means of communicating
information is as important as the information itself.
Furthermore, infrequent users became more confident to use the bus service. For
example, two of the participants wanted to explore the Scottish Borders, but they were finding
it difficult to do so with the bus as they were not confident about the reliability of the service.
They mentioned that the technology probe made them much more confident as they “could
not only see where the bus was but also the route and the bus stops and plan accordingly our
journeys”. The resulting increase of perceived control by both frequent and infrequent
travellers was intensified when RTPI was available to them. This is shown by the following
quote “The app has made me feel more in control […] even when there is no real time
information is very reassuring to see the bus moving down the road […] you can trust it.
However, what is even better is when someone else is in the bus and the T turns into an R3
then you are 100% sure that the bus is where it says it is”. In addition, when the participants
where queried about how the increase in perceived control affected their journeys almost all
unanimously said that the increase in control helps them plan their day and journeys better.
For example, a frequent passenger mentioned “I can see where the bus is and decide whether
I will grab this one or the next one or whether I have time to grab a brew or not“, and an
infrequent user mentioned “I can now go about my day and make plans without thinking so
much about the bus journey to the uni or back. I can just check where the bus is and make
plans accordingly […] and if it is a busy time there will almost always be someone else
travelling with the bus and providing real time information to us. This makes planning even
better than just seeing the T on the map”. In addition, the increase in perceived control
improved the perceptions of service reliability for both frequent and infrequent users even
though the service did not change. This is vividly demonstrated by the following quotation
from an infrequent traveller “The buses are running on time. You can see it in the app. They
are almost never late or maybe I am always on time […] I haven’t missed a bus in two weeks
now”.
In summary, the effects we observed during the pre-intervention interviews are: i)
low levels of perceived control; ii) increased levels of stress; iii) increased feelings of
uncertainty; and iv) decreased perceptions of service reliability. Similar effects have been
reported in the literature when there are low levels of information available to the passengers
regarding their journeys, or the perceived levels of information quality is low (Dziekan and
Kottenhoff, 2007; Schweiger, 2003). These were affected significantly after the introduction
of the intervention. In particular, we have seen that information has increased perceived
control of the passengers and enabled them to plan their journeys better. This resulted in
reduced feelings of uncertainty, lowered their stress levels associated with planning or
executing journeys, and increased the perceived service reliability.
Perceived waiting time
The perception of waiting time has been investigated pre and post-intervention by
asking the participants to estimate how long they have been waiting for the bus. These
estimations were compared to determine if the perceived waiting time of the participants has
changed through the intervention. The term has been clustered in the literature under the
broad adjusted travel behaviour category.
Pre-intervention the participants mentioned an average waiting time of 12 minutes
(min 5, max 19), while post intervention they mentioned an average waiting time of 5 minutes
(min 4, max 6). This decrease should be taken as an indication of a downwards trend in
perceived waiting time rather than exact estimation. This is due to the inability of the
3
Where T = timetable and R = real time, as displayed by the mobile RTPI system.
individuals to estimate time in such a fine detail. When the participants where asked what
affects their waiting time, most mentioned the potential delays based on the weather, or
previous experiences. These are demonstrated with the following quote from a frequent
passenger.: “I wait 10 minutes more or less […] I mean it depends on the weather as if it
rains or snows I try to go earlier to make sure I don’t miss the bus”.
Overall, the introduction of the intervention has illustrated that participants wait less
time when provided with information regarding the bus location. This aligns with evidence
from previous studies that public transport information decreases the perceived waiting time
in both urban and rural settings (e.g. Watkins, 2011).
Willingness to pay
In a transport context willingness to pay refers to the amount of money a person is
willing to pay to be provided with RTPI. For the purpose of the study we asked participants
about their willingness to pay pre and post-intervention for mobile RTPI.
Pre-intervention 7 out of 15 (4/7 infrequent and 3/8 frequent) of the participants
mentioned that they would be willing to pay up to £2 one off payment for a smartphone
application that provided RTPI as they see it as an extra to the bus service. This is illustrated
in the following quote from a frequent passenger: “It would be good to have real time
passenger information through an app. Aye. I don’t think its necessary but I would buy the
app if it was cheap […] I don’t know how much these things cost. I only bought games for my
phone […] I would not pay more than a pound for it.” Similarly other participants mentioned
that the public transport provider should provide this information free of charge. The
following quote from a frequent passenger shows this: “I can’t believe that they want to
charge us more money. The fares are already sky high. They charge so much money and they
want us to pay more for an app? They should provide it for free […] XXXXX buses in
Edinburgh is doing it why can’t XXXXX do it? I need it with all the delays and that […] I
think that they don’t care enough for the Borders.”
Post-intervention there was a radical change in the stance of most individuals
regarding their willingness to pay for mobile RTPI. 13 out of 15 (7/7 infrequent and 6/8
frequent) participants mentioned they would be willing to pay for the app. The following
quote demonstrates this: “I know I said that I would not pay for the app but I was wrong. It
made my journeys with the [bus] so much easier […] I don’t care if XXXX gives it for free or
not”. Further, when they were probed how much they will be willing to pay, the average of £2
that was mentioned pre-intervention was increased to £5 post-intervention. This can be
attributed to the participants identifying mobile real time passenger information as an
important missing element that improves their journeys. This is vividly demonstrated by the
following two quotes from frequent and infrequent passengers respectively: “I can’t believe I
used to travel without this app. It makes my life so much easier […] I now can’t imagine
myself using the bus without it […] I would be willing to pay up to 5 pounds for it. It is the
most I ever thought about spending for an app” and “It is a really useful piece of technology.
I used to never use my phone while travelling. Now I use it much more often. It is also great
that I can see the timetable information when there is no one on the bus. I want to continue
using it. If you guys started selling it I would be happy to pay 4-5 pounds for it no questions
asked”.
In brief, most of the participants in our study reported that they would be willing to
pay up to £5 as a one off payment (excluding data charges) for mobile, real-time information,
as they perceived it to be a useful missing element from their journeys. However, it should be
mentioned that the amount of money is indicative of the value they give to information and
not the actual amount of money they would be willing to pay for mobile, real-time
information, as individuals usually over-estimate the hypothetical amount they would be
willing to pay versus the actual amount they would pay. Our findings align with the literature,
which shows that participants value real-time information and are usually willing to pay up
for it.
Bus service easier to use
Ease of use relates to the effort required from the passenger to use the bus service. It
has been clustered in the literature under the positive psychological effects category.
Wardman et al (2001) names three types of effort: physical, cognitive and affective. Physical
effort concerns the physical activity of a journey. Cognitive effort relates to information
gathering and processing information for route planning, navigation, progress monitoring and
error correction. Affective effort is the emotional energy expended on a journey in dealing
with uncertainty regarding safe and comfortable travel and timely arrival at intermediate and
final destination. In this study we explored all the three types of effort.
Pre-intervention the frequent users (8/8) reported low levels of physical, and
cognitive effort. This can be summarised in the following quote “I have used the bus for so
long that I don’t have to think when using it […] I know where to get on and off […] and if
something goes wrong I know where I am and my alternatives”. However they reported high
levels of affective effort, as they were uncertain about their journeys “Usually my journey is
smooth but in the back of my head I am always worried whether I will arrive on time or not
[…] I had way too many bad experiences of getting stuck in places I am not familiar with”.
However, the infrequent (6/7) users mentioned high levels of physical, cognitive and affective
effort. For example, they mentioned that they don’t know where to get on or off, or if they are
approaching their destination. The following quote demonstrate this “I don’t really like using
the bus to travel to new places as I don’t know where to get off. Especially if I have to go to a
village. Most have tiny bus stops or not bus stops at all. So if I ring the bus to stop too late or
too early I can be miles away from where I want to go”. Further the infrequent users
mentioned that they can only “guestimate” the timetable for the smaller bus stops, as printed
information material does not have them. “I really want to explore the little villages of the
Scottish Borders but I can’t really plan my journeys there as the timetable information at the
bus station only provides information for the bigger bus stops and not for the small villages. I
don’t want to guestimate wrong as buses are not frequent […] and I don’t want to spend the
night there”.
Post intervention almost all frequent users (7/8) reported no change in their levels of
physical and cognitive effort. However, they reported a reduced affective effort as the
information the technology probe provides improves their levels of certainty. “It is always
good seeing the ticker getting close to you. You know that the bus is where [it] is supposed to
be and that makes you more comfortable and relaxed”. All infrequent users mentioned that
the information improves their physical, cognitive and affective effort. The technology probe
enabled them to: i) know where to get off and on; ii) know that they are approaching their
destination; iii) find the timetables for the smaller stops; and iv) plan their journey better. This
was mainly achieved through improving their geospatial and situational awareness by
showing the bus location, the route and the bus stops on a map. This is illustrated in the
following three quotes “being able to see the bus really helps. Actually it helped me find a bus
stop closer to my house that I did not know it existed. I saw the dot on the map, and one
morning that I had extra time decided to go there and flag the bus down instead of go to the
bus station. The bus stopped! I was so surprised! I started using it daily!”, “I can now see on
the map if I am running late and text or call my parents to let them know so they can pick me
up on time”, and “I can see where the bus is on the map […] that makes me feel much more
comfortable. I can take the bus without worrying any more”.
In summary, providing information though our technology probe increased the ease
of use of the bus service for both frequent and infrequent users. This aligns with the findings
of the literature that RTPI improves the levels of physical, cognitive and affective effort.
Perceptions towards the service
Passenger perceptions towards service generally relate to: i) the attitude of the
passenger; ii) the levels of satisfaction with the service; and iii) the perceived image of the
service. Attitude is a predisposition that is expressed towards favour or disfavour. Its
evaluation process can be based on cognitive and affective processes, personal behaviour or
social influences. Satisfaction is the fulfilment of one’s wishes, expectations or needs, the
evaluation process of satisfaction is based on past experiences. Image relates to the
attractiveness of the service, and is usually evaluated through a combination of anecdotes and
satisfaction with the service. For the purposes of this analysis we consider passengers’
perceptions as a unified concept and not as its individual constituents. The term has been
usually clustered in the literature under the higher customer satisfaction category.
Pre-intervention most of the frequent (6/8) and infrequent (6/7) passengers mentioned
that overall they are unhappy with the service. This is demonstrated in the following quote
from a frequent passenger that uses the study bus service more than ten times a week to
commute to school and work “[service] is a necessary evil. If I had an alternative I would
take it in a heartbeat […] I don’t have one though so I am stuck with it. I am really unhappy.
I have even formally complained but never heard back”. When further probed the participants
mentioned that this strong disfavour stems from various issues, with the most prominent
being, the price of the tickets, the overall reliability of the bus service, and the lack of
information. This is demonstrated in the following two quotes. The first one is from an
infrequent user who has only recently started using the bus “How can you be happy? The bus
is always late or early […] and when you complain they say that the timetable you have is
wrong. How can it be wrong? They gave it to me a week ago! […] It doesn’t even have my
bus stop so I have to guess what time the bus will come. I can’t believe that I pay 36 pounds
per week and I don’t have a solid service”. The second quote is from a frequent user that has
used the bus daily for several years “It used to be really bad. I remember that some years ago
they had coaches instead of buses and those were horrible. Not that now is better but at least
you travel more comfortable. That does not mean that I am happy […] it is expensive. My
biggest issue is XXXXX not informing the passenger of delays or disruptions. They should do
something about it. Send text messages or something”.
Post-intervention most frequent (6/8) and infrequent (6/7) users mentioned higher
levels of happiness with the service. This is demonstrated in the following quote: “using the
smartphone app made me more confident about my journey and that made me less grumpy in
my everyday life”. Furthermore, they mentioned that they feel that the service has been
improved and that the performance of the service is better even though there haven’t been any
actual improvements in the service. The following quote illustrate this “I am sure that the
XXXXX knows that you are doing the study and make the buses running on time. Two weeks
now and I had no delay! This [pointing at the mobile] really improves the bus service“. This
overall improvement in satisfaction can be attributed to the technology probe dealing with the
two main concerns of the passengers – reliability and control. This becomes clear in the
following passenger quotation from an infrequent passenger: “I feel more in control of my
journeys with the bus. I know when the next bus is coming and how long it will take to get
here. I feel very confident. […] and that makes me happy. So happy that people start noticing
[laughs]”.Overall,our results indicate that RTPI can improve perceptions towards the bus
service. This comes in line with the literature as multiple authors have demonstrated that
RTPI can improve the passengers’ attitudes, satisfaction and image of public transport.
Mode choice
Mode choice in regards to RTPI systems relates to the effect the information has on
the number of trips of existing users or if information attracts new customers. Evidence
whether RTPI systems increase patronage is mixed (Paulley et al., 2006). Some studies stated
an increase of between 5% and 10% in ridership after the introduction of a new RTPI system
(e.g. Lehtonen and Kulmala, 2002; Schweiger, 2003). However, it has been speculated that
most of them overestimate the impact of RTPI mode choice, mainly due to the combined
measures they utilise, which makes it difficult to correlate an increase in the number of public
transport users to the information provided. In this study we inquired about mode choice
extensively. However, we observed increased ridership in only one of our participants who
had just moved to Scotland from Asia and wanted to explore the Scottish Borders, but was
unable to do so due to lack of information. This lack of data to explore mode choice can be
due to: i) the rural nature of our work as there are only two bus services catering for the route
we were focusing on; ii) to the length of our study; or iii) the participants we were examining.
Further research could address these issues and further explore mobile RTPI and mode
change in rural areas.
Impact on decision making (utilization of wait time, more efficient traveling, and other
adjusting strategies).
The human being is very adaptive to travel conditions (Loukopoulos, 2005). The
literature indicates than when provided with real time information passengers utilise their
time better, travel more efficiently and adjust their travel in various other ways. These are
usually examined through observations and indirect and direct questioning during interview.
In this study we examined these three adaptations through six participant observations
and during the before-and-after interviews. Pre-interview we asked the passengers what they
do when they wait for the bus, discussed their travel patterns, and how they adjust their travel
if necessary. Most of the passengers mentioned that they were waiting for the bus in the bus
stop, playing with their phones and mainly paying attention to the road to flag the bus down.
Both frequent and infrequent passengers described fairly stable habitual travel patterns.
Furthermore, the frequent passengers demonstrated a better awareness of alternatives than the
infrequent passengers. This contrasting knowledge is demonstrated in the following two
quotes. The first quote is from a frequent passenger that uses the bus daily to commute to
Herriot-Watt University (HWU) and to work from Selkirk to Galashiels “If the bus doesn’t
come, I can walk to another bus stop or get another service. It is not that complicated once
you know the services, and the times”. The second quote is from an infrequent passenger that
uses the route twice a week to commute to HWU ”If I lose the bus I have to wait. I know that
there are other buses that go to (and) from Edinburgh to HWU however I am not sure where
they stop or when they run! If I get stuck I am stuck!”
Post intervention both the frequent and infrequent participants mentioned that by
being able to see the bus stops, the bus and the route on the map they were able to utilise their
wait time better. For example, participants mentioned that they were able to do light shopping
or wait for the bus somewhere else (e.g. a nearby coffee shop) and only go to the bus stop
when the bus was approaching. Further, when we inquired about their travel patterns they
described fairly different patterns than pre-intervention. For example,we observed that they
now utilise alternative services more often because of the information from the technology
probe. In addition, infrequent passengers started showing a better awareness of alternatives.
For example, they were able to identify and utilise alternative services that would take them
close to their destination if their main service was disrupted or delayed. Moreover, we noted
two further adaptations during the intervention period. These included an adaptation of
walking speed of the passengers according to the information they received, and other
adjusting strategies, such as letting a crowded bus go and getting an alternative. When during
the interviews the participants were probed about these they mentioned that they were doing
them without realising that they were utilising information from the probe.
Overall, during the pre- and post-intervention interviews and participants’
observations we have discovered that information provided through the technology probe
adjusted the travel behaviour of the participants. Namely, it improved the utilisation of
waiting time of the participants, improved the efficiency of their travelling, and enabled them
to find alternatives.
Conclusions
This paper has discussed findings of a before-and-after intervention study that utilised
a mobile RTPI technology probe to explore the effects of RTPI on rural public transport
users. Further to this, we explored how rural passengers use the system and identified several
implications for the design of a rural mobile RTPI system. To the best of our knowledge
there have not been any previous studies exploring the use of rural mobile RTPI systems or
implications for the design of such systems.
Our results indicate that mobile RTPI can significantly affect the journeys of rural
passengers. More specifically, in our study, we have observed that mobile RTPI has
positively affected the perceived control that the passengers have over their journey, the
perceived waiting time, their willingness to pay for the information, it made the service easier
to use, it improved the perceptions towards the service and impacted their decision making.
Also, we have identified that the participants used the information provided through the
technology probe to gain situation and geospatial awareness, and to adapt their travel
behaviour in disrupted circumstances.
The findings of this study are of relevance to both the transport and the humancomputer interaction academic communities as well as transport operators and policy makers
since understanding the effects of mobile public transport RTPI systems in rural areas, and
the effects the design of the technology has on them, can result in: i) improved understanding
of the benefits and issues surrounding RTPI in rural areas; and ii) the development of mobile
RTPI systems in rural areas which focus on the needs of users.
Acknowledgements
The research described here is supported by the award made by the RCUK Digital Economy
programme to the dot.rural Digital Economy Research Hub; award reference: EP/G066051/1/. Further,
we would like to thank both anonymous reviewers for their insightful comments on the paper.
References
Bertaux, D. 1981. From the Life-History Approach to the Transformation of Sociological
Practice. In Biography and Society: The Life History Approach in the Social Sciences (pp.
29–45).
Baillie, L. and D. Benyon. 2007. Investigating Ubiquitous Computing in the Home. In
Proceedings of 1st Equator Workshop on Ubiquitous Computing in Domestic
Cohen, J. 1990. Things I Have Learned (So Far) American Psychologist, 45, 1304– 1312.
doi:10.1037/0003-066X.45.12.1304 Environments; Http://www.mrl.nott.ac.uk/
axc/equator_workshop/Baillie.pdf (accessed August 20, 2015).
Dziekan, K. and K. Kottenhoff 2007. Dynamic at-stop real-time information displays for
public transport: effects on customers. Transportation Research Part A: Policy and Practice,
41(6), 489–501. doi:10.1016/j.tra.2006.11.006
Dziekan, K. and S. Sedin, 2005. Customer reactions to the implementation of a trunk bus
network in Stockholm. Paper presented at the 56th UITP World Congress, 5–9th June, Rome,
Italy. URN: urn:nbn:se:kth:diva-8226
Ferri B., K Watkins, and A. Borning. 2012. Onebusaway. Proceedings of the 28th
international conference on Human factors in computing systems - CHI '10 (2010): n. pag.
Web. 23 Aug. 2015.
Gault P., Corsar D., Edwards P., and J.D. Nelson. 2014. Providing older adults with realtime passenger information via SMS. MobileHCI '14 Workshop: Re-imagining commonly
used mobile interfaces for older adults , Toronto , Canada , 23/09/14 - 26/09/14.
Guest, G. 2006. How Many Interviews Are Enough?: An Experiment With Data Saturation
And Variability. Field Methods 18.1 (2006): 59-82. Web. 23 Aug. 2015.
Hinds, P. 2003. Workplace Studies: Recovering Work Practice and Informing System Design,
Computer Supported Cooperative Work (CSCW) 12, no. 1 (February 2003): 123–25.
doi:10.1023/A:1022496821600.
Landis, J. R., and G.G. Koch. 1977. The measurement of observer agreement for categorical
data. Biometrics, 33, 159–174. doi:10.2307/2529310
Lehtonen, M. and R. Kulmala 2002. Benefits Of Pilot Implementation Of Public Transport
Signal Priorities And Real-Time Passenger Information. Transportation Research Record:
Journal of the Transportation Research Board 1799 (2002): 18-25.
Loukopoulos, P. 2005. Future urban sustainable mobility - Implementing and understanding
the impacts of policies designed to reduce private automobile usage. Retrieved from:
https://gupea.ub.gu.se/bitstream/2077/44/1/Loukopoulos_05.pdf
Louviere, J.J., Hensher D.A., and J.D. Swait. 2000. Stated Choice Methods, An Analysis and
Application. University Press, Cambridge.
Mishalani, R.G., Mccord M.M., and J. Wirtz. 2006. Passenger Wait Time Perceptions at Bus
Stops : Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information.
Journal of Public Transportation, 9, 2, 89–106.
Paulley, N., Balcombe RMackett ., R., Titheridge H., Preston J., Wardman M., and P. White.
(2006). The demand for public transport: The effects of fares, quality of service, income and
car ownership. Transport Policy, 13, 295–306. doi:10.1016/j.tranpol.2005.12.004
Schweiger, C. 2003. Real-Time Bus Arrival Information Systems. Retrieved June 11, 2014,
from http://onlinepubs.trb.org/onlinepubs/tcrp/tcrp_syn_48.pdf
Wardman, M., J. Hine, and S.G. Stradling. 2001. Report for the Scottish Executive by the
Institute of Transport Studies at the University of Leeds and the Transport Research Institute
at Napier University- Interchange and Travel Choice, vol. 1. Scottish Executive Central
Research
Unit,
Edinburgh.
Retrieved
from:
http://www.gov.scot/Resource/Doc/156530/0042033.pdf
Watkins K. E., Ferris B., Borning A., Rutherford G.S., and D. Layton. 2011. Where Is My
Bus? Impact of mobile real-time information on the perceived and actual wait time of transit
riders. Transportation Research Part A: Policy and Practice 45.8 (2011): 839-848.