Exploring the rural passenger experience, information needs and decision making
during public transport disruption
Konstantinos Papangelisa, Nagendra R. Velagab, Fiona Ashmore, Somayajulu Sripadac, John D. Nelsonc,
Mark Beecroftc
School of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, CN
b
Department of Civil Engineering, IIT Bombay
c
dot.rural Digital Economy Research Hub, University of Aberdeen, UK
a
ABSTRACT
Individuals in rural areas are often provided with little or no information regarding public transport
disruptions. This has strong impact on those with limited access to alternative motorised transport. Though
an increasing number of real time passenger information (RTPI) systems are being developed their role in
supporting travellers during service disruption is poorly understood, particularly in rural areas.
In this paper, we first illustrate and categorise travel disruptions. Requirements for RTPI - particularly for
rural public transport users - are then identified for each type and stage of disruption through interviews and
focus groups with rural passengers. Patterns of passenger behaviour during travel and transport disruptions
are also classified. Further, a conceptual model of the recovery phases of disruption has been developed to
map the RTPI requirements for each recovery phase of disruption. The model has been developed and
evaluated through a series of focus groups and interviews with passengers, transport service providers, and
government agencies. Finally, the paper also discusses and suggests the necessary advances in digital
technologies for RTPI systems required to support public transport users during disruptions and thus
minimise the number of trips abandoned.
Keywords: real-time passenger information; disruption; rural transport
INTRODUCTION
Travel and transportation disruptions have been conceptualised in the literature as disruption to
infrastructure and disruption to the operation of the transport system (Van Exel and Rietveld, 2001; Cairns
et al, 2002). Disruption to infrastructure results from natural or manmade events and activities (such as preplanned road maintenance, severe weather conditions etc.), while disruption to operation results from
disturbance/problems with the transport network, vehicle and transport infrastructure. Disruptions may be
long-term or short-term; long term disruptions include major calamities such as floods, earthquakes, and
tsunamis while short-term disruptions are mainly due to operational disruptions (e.g., accidents, vehicle
breakdown).
There is a connection between travel and transport disruptions and passenger information, as the
availability of information can engender confidence about the journey and has the potential to result in high
levels of trip or activity recovery during disruptions (Ambrosino, 2004). People making journeys require
different types of information pre-journey, en-route, and post-journey, which must be delivered using
appropriate channels (Grotenhuiset et al., 2007). For instance, pre-journey activities, such as planning,
require information covering available modes of transport, routes, timetable, and cost; this is often
delivered in paper-based form, on a website, or via call centres. Immediately before and during their
journey, passengers require information relating to estimated arrival time, delays, network disruptions, and
schedule changes, which can be delivered via: at-stop variable message signs, websites, public
announcements, or mobile devices (Khattak et al., 2008). Providing RTPI just before and during journeys is

Corresponding author: [email protected]
1
extremely valuable for the passengers, as it can influence the travel behaviour, cultivate positive attitudes
towards the service provider and create positive perceptions of efficiency and security (Caulfield and
Mahoney, 2007; Zhang et al., 2008; Wang et al., 2009; Watkins et al., 2011; Beecroft and Pangbourne,
2015).
In this paper, we first illustrate and categorise travel disruptions. RTPI requirements - particularly for rural
public transport users - are then identified for each type and stage of disruption through findings from
interviews and focus groups with rural passengers. Patterns of passenger behaviour during travel
disruptions are also identified, and as part of this, self-organisation and resilience of communities is
discussed. Further, a conceptual model of the recovery phases of disruption has been developed to map the
RTPI requirements for each recovery phase of disruption. The model has been developed and evaluated
through a series of focus groups and interviews with passengers, transport service providers, and
government agencies. Finally, the paper also discusses and suggests the necessary advances in digital
technologies for RTPI systems required to support public transport users during disruptions and the
associated implications for managerial practice.
REVIEW OF RELEVANT LITERATURE
High quality information is a key enabler of a successful transport service provision (Ambrosino, 2004).
RTPI provision during transport disruptions is a major concern particularly for public transport users in
rural areas (Velaga et al., 2012). Although there is a vast literature on different issues (separately) such as:
travel behaviour, RTPI, and travel and transportation disruptions, almost no evidence has been found on
public transport RTPI requirements during disruption (one exception being Lu et al., (2011), and its effects
on passenger behaviour in rural areas.
Cairns et al. (2002) reviewed evidence from about 100 case studies of temporary or permanent transport
network disruptions across the world and identified alteration of traffic patterns and behavioural
adjustments for 60 of these. The evidence from a survey of 150 transport professionals and disaster
management experts indicated that public transport users change their attitudes towards travel and
transport, and behaviour based on duration, significance, impact and effect of disruptions (Cairns et al.,
2002). For example, a short-term disruption (e.g., transport strike or a bridge closure) may lead to changing
travel mode, choosing to visit alternative destinations, changing the journey frequency, consolidating trips
for different purposes, altering the allocation of tasks within a household to enable more efficient tripmaking, car-sharing, or avoiding possible journeys (e.g. by working from home occasionally). In case of
long term disruptions, passengers normally tend to make more permanent changes (e.g., job or home
relocation; permanent mode change). However, Cairns et al. (2002) did not discuss the impact of
information provision on these changes.
Zhu and Levinson (2010) also adopted a similar approach developed by Cairns et al (2002), and their
analysis identified that changes that occur as a result of short-term disruptions (e.g., road closure) can
become permanent. Furthermore, they identified that road network redundancy is a factor in determining
the significance of a disruption (Zhu and Levinson, 2010). The impact of disruptions upon rural passengers
compared to urban passengers is likely to be greater because passengers in rural areas usually have more
limited transport connectivity, fewer alternative routes for a given origin – destination and also public
transport frequency is low. However, very little is known about the real impact of disruptions on transport
networks as the available data about traffic patterns are usually based on aggregate counts, and have not
been assessed against predictive models of traffic behaviour (Watling et al., 2012).
Some studies have suggested that travel behaviour changes depend on the type and duration of disruption
and whether the disruption is planned or unplanned (Fujii et al., 2001; Van der Waerden et al., 2003; Lo
and Hill 2006; Van Exel and Rietveld, 2009). Van Exel and Rietveld (2001) examined the impact of 13
major public transport strikes in Europe and USA. They considered strikes as events that force passengers
to re-evaluate habits. They found that captive public transport users were most strongly impacted with 10%
to 20% of their trips cancelled. They also identified that accurate and timely information provision is one of
the critical components that affects the behaviour change of the public transport users.
Several recent studies have used hypothetical or real-life scenarios to test the responsiveness and robustness
of specific transport networks. De-Los-Santos et al. (2012) measured passenger robustness to disruptions
and developed a robustness index for two different types of disruptions. They applied this index to two
2
different scenarios with respect to the Madrid rail transit network: (1) providing alternative solutions (e.g.,
bus services) during disruption; and (2) not creating alternative solutions (meaning passengers have to wait
for the failure to be repaired). He and Liu (2012) developed a traffic flow evolution model for transport
disruptions which was evaluated following the collapse of the I-35W Mississippi River Bridge in
Minneapolis, Minnesota. Jenelius and Mattsson (2012) proposed a grid-based approach road network
vulnerability analysis of the area-covering disruptions (e.g., floods, heavy snowfall, storms and wildfires).
Hounsell et al. (2012) discussed bus Automatic Vehicle Location (AVL) systems data management and
applications; one of their applications is managing vehicle fleet during disruptions; however, their study
mainly concentrates on urban areas; taking the iBus system in London as a case study.
One study that addresses the role of RTPI during disruption was Lu et al (2011) who studied traveller route
choices and mode choice during random disruptions to the traffic system such as traffic incidents and bad
weather. It was identified that passenger route choices at both individual and system levels mainly depend
on real-time traveller information systems. Their study considered two types of passenger information: (1)
en-route real-time information on the occurrence of an incident; and (2) ex-post information on foregone
payoffs; and it was identified that the en-route RTPI is more important. It was concluded that RTPI plays a
major role in passenger decisions during disruptions.
From the literature review, the following gaps are identified:
1) The passenger recovery process and travel behaviour during disruptions are not well understood.
2) There have been no studies relating the passenger information requirements in the passenger
recovery process during disruptions.
3) No study has been found which examined the role of RTPI in the decision making process under
disruptions.
In this research, we utilise evidence gathered from interviews and focus groups with public transport users,
car users, cyclists, public transport operators, government agencies and domain experts. The evidence is
used to identify the characteristics of the recovery phases of disruption; explore the self-organisation and
resilience of the various communities of travellers; discuss passenger behaviour during disruptions; and
map the information requirements to the recovery phases. This has enabled a better understanding of
passenger motivations for behavioural responses during disruptions and provided understandings to aid
development of technologies that may improve passenger experience during disruptions.
Methodology
To contextualise the study, a series of interviews and structured focus groups were undertaken with public
transport passengers, transport operators, government agencies and members of academia. A summary of
these research activities is provided in Table 1. The geographic location of the rural area where interviews
were conducted with passengers in Scottish Borders, UK is shown in Figure 1.
No
Event type
Date
Number
1
Interviews with
rural passengers
February
2012
52 interviews
2
Focus groups
with bus and car
users, and
cyclists in all
operational
March
2012
4- focus
groups1
Area /
Organisation
Scottish
Borders, UK
Leeds, and
Aberdeen, UK
Aim/Objective
 Discuss the effects of public transport
disruptions in everyday life
 Understand information requirements
during disruption
 Understand the effects of public
transport disruptions in everyday life
 Explore the decision making process
during disruption
These focus groups were conducted as part of the EPSRC funded ‘Disruption’ project (funding reference
number: EP/J00460X/1).
1
3
 Explore behavioural adaptation during
disruption
 Explore the self-organisation and
resilience of communities
environments
3
Interviews with
domain experts
(transport and
social science)
May 2012
4- interviews
University of
Aberdeen, UK
 Evaluation of the conceptual model
regarding the phases of disruption.
 Evaluation of the conceptual model
regarding the self-organisation and
resilience of the communities.
4
Interviews and
focus groups
with transport
operators
May/June
2012
2-interviews
and
Bus operator,
Galashiels, UK
 Model evaluation
 Passenger information requirements
during disruption from operator's point
of view
 Understand information provision
strategies by operators
 Explore passengers travel behaviour
during disruptions from the operator's
point of view
Interviews with
government
agencies
June 2012
Local councils
and transport
authorities in
the study area
 Model evaluation
 Understand passenger information
requirements during disruptions from
local agencies' point of view
 Understand the passenger information
strategy from local agencies point of
view
5
2- focus
groups
2-interviews
Table 1 Details of interviews and focus groups with passengers, operators and agencies
Figure 1 Study area
4
Interviews with rural passengers
The 52 semi-structured interviews in the Scottish Borders explored the common experiences, shared culture,
and individual stories of the participants with regard to public transport and disruptions in rural areas.
These aimed to elicit the effects of public transport disruption on the everyday lives of rural passengers, and
their information requirements during disruptions. During interviews we had a script that we followed that
aimed to explore topics relating to how individuals experience disruption, how they behave during
disruption, what information they require etc. However, even though we followed the script, we were
flexible and encouraged the informants to share their experiences and their stories. Further, during the
interviews we inquired regarding into various observations we made. For example: we observed that
various individuals were checking the time and then directly using the phone to text or call. When we
approached them and inquired about their action they mentioned that they were using their phones to notify
friends and relatives about where the bus is and if the bus is running late and or early. These interviews
involved fifty-two participants (35 male, 17 female) with a mean age of 36.7 years old. They were
conducted along the First X95 bus service between Edinburgh and Carlisle (the route shown in Figure 1).
We selected this particular service because it connects two major cities and serves several areas with
various degrees of rurality. Each interview lasted approximately 18 minutes. It must be noted that the
participants were recruited based on a pre-screening interview regarding their frequency of public transport
usage, rather than randomly selected (Patton, 2002).
Focus groups
For our study we conducted 6 focus groups to explore issues raised from the observations and interviews.
These included I) the use of kinship networks during disruption II) appropriation of technologies, III) the
passenger information requirements during disruption, IV) and to explore how individuals and communities
cope with disruption.
Four focus groups were conducted at the Universities of Aberdeen and Leeds in the UK. The participants
were a mix of urban and rural bus and car users, and cyclists from the Aberdeen and West Yorkshire
County respectively. Each focus group was comprised of 8 to 11 participants with a mean age of 34 years,
and lasted approximately 90 minutes. The participants were recruited through emails and flyers. The main
discussion concentrated on the effects of disruption in everyday life, and the individuals’ adaptation and
decision-making processes during and after different types of transport disruptions.
In addition, we conducted two focus groups with bus drivers that mainly operate the X95/95 bus service in
the Scottish Borders. The focus group involved nine bus drivers that had at least four-year experience in the
area and the route. The main discussion concentrated upon their experiences on how passengers behave
during disruption, and the importance of information. The operator focus groups included different
members in the hierarchy of a public transport (bus) operator company. The objective was to capture
relevant issues at a variety of operational levels within the company. The interviewees included the
Communications Manager and the manager of a bus depot in the rural area of Galashiels, UK (see Figure
12). Both interviews lasted approximately 90 minutes. In both cases the discussion focused on disruptions,
passenger recovery, trip recovery and abandonment, passenger travel behaviour during disruption from an
operator point of view, and the operator’s information strategy.
Interviews with government agencies
These interviews comprised two government agencies: i) an individual from the Transport Subcommittee
of the Scottish Borders Council and ii) the regional area manager of a government transport agency. Both
of the interviews lasted approximately 120 minutes. In both cases the disruption model was used to
organise discussion around the impacts that disruption has upon rural passengers, the importance of RTPI,
and each agency’s future plans for a passenger information strategy.
Interviews with domain experts
The interviews with the domain experts included two academic experts in the field of travel behaviour and
disruption, from the University of Aberdeen’s Centre for Transport Research on the effects of disruption in
the everyday lives of individuals living in rural areas. All interviews lasted approximately 80 minutes, and
aimed to critique and evaluate the model.
5
Data analysis
The interviews and focus groups were transcribed verbatim and subjected to a grounded theory type
approach involving data reduction and inductive content analysis (examples of interviews can be found in
Appendix 4 on page 201, and examples of focus groups and the focus group flyer and the topic guide can
be found in Appendix 6 on page 243). The ground theory type approach is a systematic methodology,
which enables the researchers to review the data collceted, and through aparent repeated ideas, concepts or
elements meaning is assigned. The results of the ground theory type approach were analysed using an
inductive content analysis approach. The purpose of this was to:



To condense extensive and varied raw text data into a brief, summary format.
To establish clear links between the research objectives and the grounded theory type approach
findings.
To develop of a model about the underlying structure of experiences or processes which are
evident in the raw data.
Such techniques, enabled us to drew elements, categories, patterns, and relationships from the data.
The results of this process were refined based on interviews with public transport operators and government
agencies, and through inputs from the domain experts from the Center for Transport Research (CTR) of the
University of Aberdeen.
The analysis resulted in the creation of a model of the recovery phases of disruption. The model was then
used as an analytical framework against the raw data in order to output the information requirements and
the underlying travel behavior of the various phases (see Figure 2).
Figure 2 Methodology
EXPERIENCING PASSENGER DISRUPTION IN RURAL AREAS
In all the areas we studied, disruption was frequent, expected, and seen as a characteristic of the transport
system. This is vividly illustrated in the following quotations : “Whenever I’m going further than my daily
commute, I think it’s always a factor for me”, and “I just kind of accept that if I’m going anywhere outside
the Aberdeen area there’s going to be a delay – there’s going to be a disruption in my travel plans”. This
expectation that a disruption might occur results in high levels of frustration. However, our data illustrate
that this is not always the case as some disruptions are more acceptable than others. For example, man-
6
made disruptions (e.g. strikes) are less tolerable than disruptions caused by nature (e.g. heavy rain or high
winds). This is described by the following assertion: “I would say that public transportation disruption is
man-made and the other we can influence. So that’s the main problem, for me. I was very upset when I was
stuck somewhere on the beach, it was freezing cold and I couldn’t get the bus because they were striking
and I didn’t know they were”.
This quote also comes in line with our findings that each individual experiences disruption differently, as
one individual’s disruption can be another individual’s opportunity or inconvenience. This may depend on
various factors including personality and previous experience amongst others. The following two quotes
illustrate this “Some things, are just interruptions but it’s when it affects what you’ve planned to do – you
planned to have your breakfast on the train whilst doing your work because you are getting an early train,
when you can’t have your breakfast and you can’t do your work then that’s a disruption but if it’s someone
playing loud music then it’s not really affecting your plans to sit on that train and get to a destination. For
me, that would be the thing: whether it affects what my plans were for the journey”, and “ […] for example
weather things, in my home country it’s not an issue at all, so this I don’t feel as a disruption. It makes it
difficult but I don’t feel it as a disruption.”
Our findings also illustrate that individuals living in rural areas are more prepared to tackle disruptions than
their urban counterparts, and preparedness is seen as important. This is especially true for remote places.
Individuals are more likely to be prepared for disruptions in rural areas with higher chance of systemic
disruption. For example, inhabitants of the Isle of Tiree, can experience high winds during winter that make
the island inaccessible for up to two weeks, and so, they stock food and fuel for up to three weeks during
the winter.
Further, this research has identified that certain groups of individuals are more vulnerable to disruptions
than others. These can be summarised as:






Family with young children
Individuals without family or friends
Those living in the outskirts of rural hubs or in hamlets
Individuals dependent on public transport
Individuals that do not have immediate access to private transport
Tourists or Individuals who do not have knowledge of the locality
Many participants reported thatdisruption is becoming easier to cope with. This is due to new technologies
that travellers utilise to access a great variety of formal and informal information channels (e.g. social
media, websites, blogs, forums, etc) enabling them to stay up to date, and exchange information. Figure 2
illustrates an individual living in a hamlet in the Scottish Borders informing her twitter followers that the
A7 roadworks are causing delays longer than expected.
Figure 2– Correcting and relaying official source information in twitter
Nonetheless, individuals have stated that disruption can also lead to positive outcomes. These can include:
increased fitness by walking instead of taking the bus or driving, working from home, taking days off, and
getting a break from the routine. The latter is illustrated through the following quotation: “Maybe it’s not a
positive thing for our climate, but you know if you work in a large office like I was in that incident, when
something like that happens, because it’s a break from the routine and there’s a prospect that they might
need to send people home, regardless of the fact they might need to spend five hours getting there, people
do look at that as quite a positive experience, it’s like that kind of - You get a buzz.”
Behavioural responses to disruption
7
Individuals living in rural areas display a wide array of behavioural responses to disruptions ranging from
minor to major adaptations. Our study indicates that minor adaptations are more prevalent in low impact
disruptions, while major adaptations are more common in high impact disruptions. This is summarised in
Table 2.
Type of disruption
Low impact
Frequent
disruptions
Infrequent






High
impact
disruptions
Infrequent


Effects in journey
Journey usually recovered.
Adaptations are minor.
Not much time spent in planning
and decision making process.
Examples of passenger adaptation
 Switching mode
 Catching an earlier bus.
 Staying overnight with friends.
Journey usually recovered.
Adaptations range from minor to
major.
Decisions are well thought and
planned.



Keeping spare clothes at a
friend’s house
Leaving earlier or later
Avoiding social arrangements on
the day of travel
Journey recovery of abandonment
depends on purpose of journey.
Adaptations are major.



Mode change
Route change
Relocation
Frequent
Table 2 the effects in journey and examples of passenger adaptations with reference to the type of
disruptions.
In low impact disruptions, the journey is usually recovered and the change in travel behavior occurs to
facilitate that particular journey. Little time is spent in the planning and the decision making process. The
individuals will usually base their actions on local knowledge and previous experience. Examples of such
minor short term adaptations include: using local shops, staying overnight with friends, relying on family
for lifts, switching mode, working from home, leaving early or late. However, if a low impact disruption
becomes frequent it may lead to significant changes in the behaviour of an individual. During such
disruptions the individuals spend more time in the planning and decision making process and base their
actions on long-term convenience. Examples include, keeping spare clothes at a friend’s house, leaving
earlier or later, avoiding social arrangements on the day of travel etc.
High impact disruptions lead to significant changes in the behaviour of the individual. The individual
almost always plans a course of action, mainly based on previous experience and knowledge of the locality.
A significant number of participants in both our interviews and focus groups mentioned that if a high
impact disruption is infrequent they would only try to recover the journey if the purpose was important (e.g.
commuting to work, visiting a doctor). The most common examples of behavioural adaptations to high
impact infrequent disruptions are mode change, and route change. However, if a high impact disruption
occurs often (even as often as once per month) it may result in life changing events, such as buying a car or
relocating. The following quotes demonstrate this “I’ve moved – I use to live in Longtown but due to
disruption I moved to Galashiels.”, and “I used to commute with my bicycle every day. It’s only about 8
miles but it’s a really bad journey, and not in itself was a reason to buy a car, but I could not take it
anymore!”.
Making choices during disruption
The behavioural adaptations discussed in the previous section are largely influenced and shaped by various
factors. These can relate to the characteristics of the area, and can either be social, personal, or
psychological. The factors that relate to the area are the transport options and the information that
individuals have during a disruption. The social factors relate to the social norms, the roles and status of the
individual and the passengers social network. The personal factors relate to the previous experience and
various socio-economic factors. The psychological factors relate to the traits, personality, temperament and
cognitive biases of the passenger. The following figure illustrates these factors in relation to their
importance to the response to disruption as discussed by participants in interviews and focus groups.
8
Area
Social
Personal
The available transport
options
The available information
Resilience of the community
Social status
Social Norms
Passengers Social network
Socio-economic factors
(e.g. age, lifecycle stage,
occupation, economic
circumstances, lifestyle
personality)
Purpose of journey
Previous Experience
Psychological
Traits
Temperament
Personality
Response to
disruption
Cognitive Biases
Figure 3 Factors that affect the response to disruption as emerged from our analysis
The information individuals have during disruption
Individual level information during disruption may play the most important role in the decision making
process of the individuals during disruption. We have expanded upon previous research that explores the
passenger recovery process to disruption, and have identified that information is very important during the
recovery phase, in which the individual looks for preventive measures to mitigate the effects of disruptions
and recover the journey (Papangelis, 2013).
The purpose of the journey
The purpose of the journey plays an important role in the decision making process on whether or not the
individual will abandon or recover the journey. Many participants stated that if they have an important
journey to make they tend to choose modes of transport with low probability of disruption – such as car and
DRT service. Further, they mention that if they experience disruption they tend to have an alternative
arrangement. This is illustrated in the following quote ”If I have to visit the doctor I will call a taxi […] and
I will notify my relatives that I am taking a taxi in case there is any issues.”
The available transport options
The transport options that the individual is aware of plays an important role in the decision making process
during disruptions. Most of the participants in interviews and focus groups that were relying on private
motorised vehicles utilised them in case of disruptions as they were not aware of the various options
available to them. However, this was not true for the individuals who were utilising mainly public transport,
as they were more aware of the other alternatives.
The individual’s social network
The individual’s social network is critical during rural disruptions as the individual usually exploits it to
increase situational awareness and gather information on how to mitigate the effects of disruptions.
Granovetter (1973) discussed how and when the individuals who comprise one’s closest group do not
possess the information or social resources that one needs in order to conduct their daily life, weak ties can
be invaluable resources. Further, the activity of others (outside the individual’s social network) who have a
strong dependency or connectivity to the individual’s travel pattern is important in case of disruption, as
they might posses information that will help others to recover their journey.
The social status
9
The social status of an individual plays an important role mainly when choosing modes during disruptions.
A small percentage of participants in our focus groups mentioned that they would prefer taxi to bus, and if
no taxi was available they would prefer to abandon the journey rather to use a bus. However, this was not
the norm amongst the participants of the focus groups as most agreed that the mode of transport does not
matter as long as they arrive in their destination in a timely manner.
The socio-economic characteristics of the individual
Further, we have identified that various socio-economic characteristics (car usage, family status, income,
etc) play an important role in the decision making process and the recovery process of an individual.
The social norms
The social norms of the locality affect the adaptation of the individual. Our studies indicate that individuals
are less likely to use a new form of transport that is not common where they live. For example, in
Longtown, in the Scottish Borders, car sharing is commonly employed when there is public transport
disruption. However, they don’t use other modes of transport, such as bicycles as they are not the norm.
This is illustrated in the following quotation: “I would not use a bike. It would be weird [...] nobody is
using them around here except kids. I prefer to stay at home rather than seen riding a bicycle to college”.
The self-organisation/resilience of the locality
In resilience theory, communities are not resourceful, but rather have resources that can be developed,
expanded or exhausted over time. The capacity to act is not enough to develop resilience; it is the action
taken that is critical (Magis, 2010). The identification of resources by the passenger results in them
accessing those resources to create new travel arrangements. Further, proactive individual and collective
human agency is a key characteristic of resilience. Individual action and collective action occur differently
during short term and long-term disruptions: in the short term, actions are marked with individualism,
whereas in the long term, actions steadily become more collective and pro-social. This is particularly
interesting as it appears that the ability to develop collective resources and collective resilience does not
occur unless the disruption is long-term, whereas individual resources are developed in the short term. This
signifies that there is a temporal component to developing and enhancing different levels of resilience
(Heesen et al, 2010).
Previous experience
Past disruption experiences can impact future decision-making. Previous research has indicated that past
decisions influence future decisions, because when something positive results from a decision, people are
more likely to decide in a similar way, given a similar situation (Juliusson, Karlsson, & Garling, 2005). On
the other hand, people tend to avoid repeating past mistakes (Sagi, & Friedland, 2007). The findings of our
studies align with this literature, as over 80% of all the individuals involved in our studies mentioned that
they base their decisions during disruption on past successful actions. Further the participants mentioned
that if a decision based on past experiences is not successful it will not be considered in the future.
Various other traits of the individual
In addition to the aforementioned, the individual’s traits can influence decision making. Although not
explored in detail in this study, it seems that the traits, personality, temperament and cognitive biases of an
individual also play an important role in the decision making process. These seem to affect the collection
and interpretation of information, and the individual’s behaviour during disruption (e.g. individualistic or
pro-social behaviour).
COPING WITH DISRUPTION
Most of the discussions with the participants revolved around the various strategies they employ when they
encounter mobility disruption. The most common coping strategy we have observed is ‘time buffering’.
Individuals usually make an assumption that they will be late, or that something will go wrong and “build
time on one end or the other” of the journey in case that happens. The is exemplified in the following two
10
quotes: “By making that assumption I’m always building in time on one end or the other in which I can
scramble for whatever I need. As far as my day to day commute is concerned I only rely on myself. So the
only disruption is when I can’t manage to do what I need to do.”, and“I travel reasonably frequently down
to West Wales and I travel at night because I know that the traffic disruption is going to be considerably
less, it’s just planning around it.”
Further, information is deemed extremely important for shielding against disruption. During our initial
interviews when we asked the participants ‘how could you minimise disruption in your journey’ most of the
interviews answered that cars, mopeds and motorcycles are the best way coupled with a technology that
provides real-time information about disruptions, and suggests ways around them (such as in-vehicle
satellite-navigation systems). When we expanded this in the focus groups the participants mentioned that
technologies and timely, accurate and personalised (TAP) real-time information is probably the best way to
insulate against disruption. In addition, when asked to rank the reliability of public transport and car in
situations without real-time information, they ranked car higher as “it is more flexible”. However, when
presented with a mock-up of a technological solution that provides real-time information about all modes of
public transport they ranked the reliability of the public transport and the car the same as: “I will be more
confident and when something goes wrong I will find a way around it”. It should be noted that none of the
respondents owned a car, and only a handful had intermittent access to one.
Moreover, we have identified that kinship networks are also utilised as a way to protect against disruptions
(Papangelis et al, 2013). Kinship networks are composed of weak ties and strong ties. The strong ties
channels are individuals within the passenger kinship networks, which consist of family members, close
friends, work colleagues, and school peers that are considered to be as close as familial links (Ebaugh and
Curry, 2000). The weak ties are usually friends of people from their strong ties network, or other
passengers, where they have a strong dependence on the connectivity to the individuals travel patterns. The
information the passengers are seeking from these networks is usually to increase their situational
awareness and information on how to mitigate the effects of disruptions. For example, during our passenger
interviews, a participant mentioned that during the heavy snowfall in the Scottish Borders in 2010, she
reached home safely not because of information that the operator provided, but from information that the
passenger got from a friend of a friend about a local man going through her village with his snowplough. It
was explained in our interview that the same individual, picked up other individuals that he did not know
personally along the way only because they had shared common networks and ties. Figure 4 captures these
information exchanges during times of disruption among strong ties, weak ties and formal information
channels.
Figure 4 Information exchange between individuals affected by disruption and their kinship network
(Adapted from Papangelis, 2013).
Further, we observed that individuals exchange information through kinship networks through various
technologies. These include smartphone applications (e.g. whatsup, and viber), social media (e.g. twitter),
as well as emails, phone calls and text messages.
11
Overall, our findings indicate that disruption in rural areas is seen as an inherent characteristic of the
transport system. Even though it usually leads to frustration, it is often not seen as a problem if there is a
way around it. In addition, we have identified that infrequent disruptions lead to more often micro
adaptations in behaviour while frequent disruptions lead to major adaptations. However, this depends on
the individual, as certain groups are more vulnerable than others. In addition, we have identified that
individuals in rural areas utilise two main strategies to insulate themselves against disruptions, namely time
buffering and use of kinship networks. The findings align with and expand previous studies by providing
an initial insight into the rural dweller’s behavioural adaptation during disruption, and can be utilised to
inform which combination of policies and strategies can support effective contingency planning and
improved travel options for the user and the operator in rural areas during disruption.
Self-organisation and resilience of communities
There is a vast array of literature discussing and debating resilience, mainly concentrating on community
response to environmental and socio-economic change (Wilson, 2012; McManus et al. 2012). Within
governance structures across Europe and the UK, the concept of resilience is often found in strategies and
policies as part of emergency or disaster planning (see Scottish Government, 2012). However, the use of
resilience as a method for understanding passenger behaviour during transport disruption is a relatively new
method for the academic transport community. This notion of resilience is concerned with adapting to
stresses to maintain acceptable levels of function and identity. Social resilience builds on these themes to
represent the ability to withstand shocks due to external factors. Norris et al. (2008) define it as both a
reactionary and proactive process: “A process linking a set of adaptive capacities to a positive trajectory of
function and adaption after a disturbance” (p. 131). Magis (2010) further contextualises this, defining
broader community resilience as “…the existence, development, and engagement of community resources
by community members in order to thrive in an environment characterised by change, uncertainty,
unpredictability, and surprise. Members of resilient communities intentionally develop personal and
collective capacity that they engage to respond to and influence change, to sustain and renew the
community, and to develop new trajectories for the communities' future” (p. 401). This definition of
resilience has distinct ties with disruption in rural transport. The characteristics of resilience, located in
Figure 5, have been used in conjunction with our results of the interviews and focus groups to develop a
conceptual model of passenger resilience pathways, depicted in Figure 6.
Passenger resilience during a disruption varies with type, impact and duration; it is also influenced by
secondary factors including geographic area and passenger alternative transport options. By identifying
pathways to passenger resilience through a conceptual model, we aim to link current behaviour patterns
with their potential for increasing resilience in times of future disruption with the view to inform policy
matters.
Eight dimensions of community resilience identified
by Magis (2010):
Figure 5 Resources and human agency are
identified as key characteristics of the social
concept of resilience in transport.
12
The model presented in Figure 6 depicts the link between eleven resilience characteristics as emerged from
our data (which are under four different groups) for two extreme types of travel and transport disruptions:
(1) Short term and low impact disruption and (2) long term high impact disruption. The resilience
characteristics presented in the model are formulated from existing resilience literature, and consultation
and brainstorming discussion with field experts. Further, various transport disruptions are categorised based
on type and impact of a disruption. We have considered only the two extremes of disruptions to obtain an
understanding of the extent of possible behaviour patterns (i.e., short term low impact and long term high
impact); an example of such short term low impact is 'a road closure due to a minor accident' and an
example of a long term high impact disruption is 'travel disruption due to heavy continuous floods'. A
series of studies conducted in the Scottish Borders generated the passenger behaviour data and information
requirements used in this model.
The characteristics of resilience are listed first, with the concepts of strategic action and resources being
broken down into more detailed dimensions. Resources, a critical component of resilience, are broken
down into resource identification (the need and ability for individuals and groups to identify currently
available resources), resource development, and resource engagement. The drivers that affect transport
decision-making during disruptions are identified from expert studies, as specific characteristics are
believed to impact transport behaviour differently and as such play into the opportunity to enhance
resilience of passengers. Finally, we include impact, to demonstrate how specific types of disruption may
impact the development of resilient characteristics. From this we identify behavioural traits in line with
these characteristics based on the two extreme disruption occurrences, a) short-term, low impact disruption,
and b) long-term, high impact disruption.
Proactive individual and collective human agency is a key characteristic of resilience. Based on the
preliminary data, individual action and collective action occur differently during short term and long-term
disruption: in the short term, actions are marked with individualism, whereas in the long term, actions
steadily become more collective and pro-social. This is particularly interesting as it appears that the ability
to develop collective resources and collective resilience does not occur unless the disruption is long-term,
whereas individual resources are developed in the short term. This signifies that there is a temporal
component to developing and enhancing different levels of resilience.
In resilience theory, communities are not resourceful, but rather have resources that can be developed,
expanded or exhausted over time. The capacity to act is not enough to develop resilience; it is the action
taken that is critical (Magis, 2010). The identification of resources by the passenger results in them
accessing those resources to create new travel arrangements. This process is identified as more relevant in
the long-term disruption data. In the short-term, individual information provision is more relevant. Studying
the drivers for transport adaptation has provided some preliminary results, demonstrating that whilst in all
cases previous experience and knowledge are utilised, alternative options (also a resource available to rural
passengers), become more intensively analysed and utilised the longer the disruption. Demographic and
socio-economic characteristics, trust in information, and social norms also impact behaviour, but play out
differently depending on the type of disruption. This also demonstrates a need to understand the temporal
distinctions in behaviour. Understanding these characteristics in communities prior to disruption can allow
planners to have a better understanding of behaviour patterns and how to lessen the impact if disruption
occurs.
Finally, the impact of such disruption is analysed in the context of the potential to develop resilience
characteristics. In the short-term, changes made are non-drastic, rarely permanent and do not tend to result
in adapted behaviour once the recovery period is over. In the event of a long-term disruption however,
adaptations are more extreme, occasionally resulting in permanent changes to behaviour in an effort to
lessen the impact of future disruption, demonstrating the ability for disruption to contribute to the creation
and enhancement of passenger resilience.
13
Figure 6 The link between eleven resilience characteristics as emerged from our data.
14
PASSENGER RECOVERY PHASES TO DISRUPTION
Through the interviews and focus groups, we identified the passenger recovery phases to disruption from
the passenger perspective during transit. Each recovery phase of disruption has unique information
requirements and associated travel behaviour. The recovery phases identified are: i) pre-disruption, ii)
warning, iii) response, iv) impact, and v) recovery. It should be noted that the warning stage might occur
only during planned disruptions (e.g., pre-planned strikes), and not in un-planned disruptions. Figure 7
illustrates the passenger recovery phases to disruption. A description of each of these phases follows.
Figure 7 Disruption recovery phases
Pre-disruption phase
Passengers’ routines and travel habits are characterised by normality and continuity. Our findings regarding
the information requirements of the period before disruption broadly align with the findings of previous
research by Deeter (2009) and Zimmerman et al. (2011). That is, information is required/desired pre-trip, at
the boarding point and in-vehicle. This information relates to upcoming services, journey planning facilities
as well as information about upcoming or current disruptions.
In addition, our research has illustrated that the proliferation of new technologies (e.g. smartphones) has
resulted in a change in the information needs of individuals from static to dynamic and mobile, as over 50%
of the interviewees would like to receive the RTPI on their phone through various channels (e.g. App, SMS,
etc). Over 70% of these participants mentioned that this information should be directed to their needs and
not be generic. This illustrates a desire for TAP information, which is especially important during
disruption, as one interview participant stated: “information is good. We have information. The problem is
that the information we get during disruptions is rubbish; is usually out of date and generic. Getting
information in your phone would be great. However, it would have been up-to-date and directed to my
travel. I don’t want to get information about a disruption in Edinburgh or disruption about a bus I am not
on or intend to take”.
Warning phase
During this phase, the potentially affected population is notified of the pending disruption.
The warning usually states the nature of the problem, but it often fails to offer an assessment of the
consequences (e.g., extension in journey times for specific destinations). After warning about an upcoming
disruption, individuals with knowledge of the local transport network gain situational awareness and begin
to understand the effects of disruption on their journey. Individuals unfamiliar with the transport network
will often try to anticipate the effects based on imperfect knowledge and might be optimistic or pessimistic
based on their personality or past experiences.
15
Warning for an upcoming disruption, and the associated degree of notice, has the potential to significantly
affect the travel routine of the individual. For example, over 70% of participants during the passenger
interviews mentioned that when they know that there is disruption in the bus service they intend to use,
they adapt to it (e.g. by car-sharing, cycling or walking). However, advance warning for an upcoming
disruption is not common in rural areas. This is vividly illustrated by the following rural passenger quote:
“You only find out there is a disruption when the bus does not show up. There is almost no information
about disruptions, delays or problems“. Most information about disruptions in rural areas is provided to
passengers via posters on the bus and at bus stops. However, most of the time, that information is operator
centric, and does not suggest alternative travel options (e.g. services run by other (non-affiliated) operators
or community services).
The information requirements of the passengers during the warning phase changes depending on the stage
of the journey. During pre-trip, most of the participants agreed that timely and accurate information about
network status and TAP notification of upcoming disruptions on the services is critical, as this information
will direct their travel plans for the day. At the boarding point the participants discussed the need for
detailed TAP information about on-going or upcoming disruptions that will affect the passengers’ trip. This
is illustrated in the following rural passenger quote: “When you are awaiting for the bus knowing what
doesn’t work or knowing when there are issues, is very important, if you know things like this you can
adjust your journey accordingly”. Once on board a vehicle, the participants agreed that prompt information
about any disruption to the service or connecting services, and how these will affect the passengers’
journey is critical. This is important as situational awareness plays an important role in the choices of
individuals, as it is the main driver for understanding the currently on-going incident, and reacting to it
(Starbird and Palen, 2011).
Response phase
During this phase the passenger looks for preventive measures to mitigate the effects of disruptions and
recover the journey. The timeliness of a warning has the potential to alter the ability of the passenger to
respond. The extent of the success of any attempts to mitigation of the effects of disruption depends on the
previous experience that the individual has with similar types of disruptions. Usually during the response
phase, the individuals affected by disruption begin to gather as much information as possible from multiple
channels; these might include formal as well as informal sources. The formal channels may include the
vehicle driver, the operator and local media. The informal channels may include friends, relatives or other
travellers familiar with the locality. However, the value of information depends on the individual
passenger’s perceptions, experience and attitude towards the information sources. In many cases during our
interviews, individuals demonstrated a preference for information from people directly involved in the
event (e.g. from travellers on a delayed bus), rather than formal sources as “they do not provide relevant
and up-to-date information” and “most of the times they are wrong”. Two types of informal channels can
be identified: strong ties channels and weak ties channels (Figure 4). Granovetter (1973) discussed how and
when the individuals who comprise one’s closest group do not possess the information or social resources
that one needs in order to conduct their daily life, weak ties can be invaluable resources. Over 80% of the
participants agreed that they tend to utilise these networks during high impact disruptions. The information
that the passengers are seeking from these networks is usually to increase their situational awareness and
information on how to mitigate the effects of disruptions. Obviously, not all the individuals in weak and
strong ties of an individual impacted by a disruption have timely and accurate information. However, they
might have pieces of information by hearing from others, seeing and feeling through various platforms they
might have access to (social networks, websites, blogs, local media, their own network of strong and weak
social ties etc.) (Maclean and Dailey, 2001; Politis et al., 2010) or advice based on their own experience.
Figure 8 captures these information exchanges during times of disruption among strong ties, weak ties and
formal information channels. The solid lines in Figure 8 illustrate the information that the individual is
getting is from formal information channels, and strong ties, while the dotted lines show the information
that the individual is receiving is from weak ties.
16
Figure 8 Information acquisition and exchange during disruption
Overall, the information requirements of this phase were identified by the participants as: (a) TAP
information regarding alternative modes, routes and arrangements, and (b) TAP updates on current
situation, the effects of disruption on their travel, a planning facility, and prediction of how long the
disruption will last.
Impact phase
In this phase, the actual disruption takes place. Individuals may be temporarily confused during the
disruption. Our research findings have illustrated that when disruptions are of low impact, the actions of the
passengers are marked by individualism (i.e. not helping individuals outside their social sphere), and only
minor temporary travel behaviour adaptations are evident (e.g., catch an earlier bus). However, these minor
adaptations can become a longer term feature of travel choices. For example, several participants in our
interviews in Scottish Borders mentioned that they tend not to take the 10:00 Friday service, because they
know the local farmers move their sheep and horses between fields and that causes delays.
When a disruption has high impact, the actions of the passengers are marked by individualism in the earlier
stages and pro-social behaviour in the later stages (i.e. altruistically help individuals outside their social
sphere). This was discussed extensively among the participants in relation to the heavy snowfall of 2010 in
the Scottish Borders, which left all public transport immobilized for several weeks. During this time, the
passengers initially utilised their strong and weak social ties to acquire the necessary travel and transport
information. Then as the disruption evolved, from a temporary activity to more permanent, people sharing
similar routes started self-organising and coming up with creative co-operative solutions to overcome travel
issues. For example, individuals living in Longtown (a small rural village in the Scottish Borders) started a
communal car sharing scheme, where families with a car were helping other families or the elderly with
their daily commutes, shopping etc.
The information requirements of this phase as emerged from the rural passenger interviews and bus and car
users and cyclists focus groups are: information regarding alternative services, mode and routes, updates on
current situation and the effects it has on their travel, planning facility with information about disruption,
and predicting the duration of the disruption.
Recovery phase
In this phase, the disruption is over. The passenger either returns to their pre-disruption normality or
maintains the routine acquired during the disruption (or a variant of it). Our research findings illustrate that
the former has the same information requirements as the pre-disruption phase and is more prevalent in low
impact or short disruptions. The latter is more prevalent in high impact or protracted periods of disruption.
Barley (1986) describes that external events can be so intense that they cause slippages (i.e. deviations from
17
normality), to take place. When slippages persist, they become replicated patterns that people must adapt to
on a continual basis. As a result, individuals restructure their patterns of action in order to adapt to changing
circumstance. Thus, a disrupted environment, which affects people’s normal routines, can be a trigger for
people to develop new routines, or new patterns of action, to adapt to the changed environment. It should
be noted that the new routine of the individual is limited to the available alternatives. This has also been
identified in a limited extent in the transport behaviour literature (for example see Cairns, 2002; Zhu and
Levinson, 2008).
Supporting a new travel normality with TAP information can be done in two ways: a) by presupposing
what the new travel normality will be, and by supporting it in advance, and b) by designing an RTPI system
that is grounded in the needs for “loose fit”, that is designing the RTPI system so that unexpected uses of
the system can be accommodated (Fisher, 2011).
Overall, the information requirements of the new normality are characterised by the same need for accurate,
timely and personalised information in various phases of the journey. Table 3 summarises the information
requirements and the travel behaviour of each recovery phase.
18
Disruption
phase
Predisruption
Characteristics
Information requirements
Period before a disruption
occurs.
Warning
The potentially affected
population is notified of
the pending disruption.
Response
Preventive measures are
taken to mitigate the
potential effects or lessen
the effects of disruption
for the current and
subsequent journeys.
The actual disruption
takes place.
Impact
Recovery
Normal travel behaviour
resumes.
OR
New travel
emerges.
Travel behaviour
 Pre-trip, in vehicle and at the boarding point, timely and
accurate information about the status of the network, Timely
Accurate and Personalised information about the upcoming
services, planning facility as well as information about
upcoming or current disruptions.
 Pre-trip, and at the point of travel TAP notification regarding
planned and ongoing disruptions.
 In-vehicle information about own vehicle disruption,
connecting services disruptions, and how these will affect the
passengers’ journey is critical.
 Information is required during pre-trip, at the boarding point,
and in vehicle.
 TAP provision of information regarding alternative
modes/routes/arrangements.
 TAP updates on current situation and the effects of RTPI
information on their travel.
 Planning facility with TAP information about disruption.
 Prediction of how long the disruption will last.
 Travel behaviour is characterised by normality and
continuity.
 TAP information similar to pre-disruption phase.
 TAP information similar to pre-disruption phase tailored to
maintain the new travel behaviour or encourage particular
options.
 In low impact, short disruptions the passenger returns to
their old travel behaviour.
 In high impact or long disruptions passengers may
maintain the adaptation after the disruption.
 Once warned the passenger begins to understand the
effects of disruption on their journey.
 Looking for (trusted) channels of TAP information
 The passengers look for ways to mitigate the potential
effects or lessen the effects of disruption through the use
of personal networks.
 The actions of the individuals are marked by
individualism in low impact disruptions and pro social
behaviour in high impact.
 During low impact disruption, small travel adaptations in
the individuals' routine are prevalent.
 During high impact disruption, the individuals adapt their
routine significantly.
behaviour
Table 3 The information requirements and the travel behaviour of each disruption phase
19
DISCUSSION AND CONCLUSION
In this paper we discussed findings of a study that aimed to explore rural disruptions from a passenger
and community perspective. In detail we identified patterns of passenger behaviours during travel and
transport disruptions; explored the community resilience in rural areas; developed a conceptual model of
the recovery phases of disruption; and identified information requirements relevant for each recovery
phase. Our results indicate that the current provision of information in rural areas regarding disruption
does not meet passengers' needs, and that passengers are looking for TAP information during pre-trip, at
boarding point and on-trip information during the various recovery phases of disruption.
The conceptual model of the phases of disruption, the associated passenger information requirements and
the broad travel behaviour of each phase has been evaluated through a two-stage process. The first stage
included reflection upon the interviews and focus groups. This stage shaped and our choices during the
development of the conceptual model. The second phase included interviews with three domain experts.
This phase refined our model through critique (see Table 1 for more information). The evaluation
indicated that the model offers a framework for the understanding of disruption, the associated
information required, and the effects it has on travel behaviour. Also there is a need for better
understanding of disruption and its effects and a structured approach such as that used in developing the
conceptual model. The evidence identified from the rural public transport users' RTPI requirements
during disruptions shows that a range of information is required for each recovery phase of disruption.
For example: (1) during the pre-disruption phase, passengers need information about the severity and
effect of disruptions on their travel and transport; and (2) in the case of the warning and response phase,
passengers like to have TAP information on the current status of the transport and alternative transport
options to hand. Provision of RTPI during disruptions has the potential to significantly affect the
passenger comfort level, anxiety level and their travel behaviour. Through our research, it was also
identified that normally in rural areas information about disruptions is displayed through posters on buses
and at stops; and this information is only about pre-planned disruption and is operator dependent.
In order to provide detailed TAP information, certain transport technology and infrastructure (e.g.,
Automatic Vehicle Location (AVL) systems on buses and variable message signs in-vehicle and at stops)
is required. The deployment of such transport infrastructure is generally limited to urban areas and cities;
rural areas generally suffer a lack of such transport infrastructure (Nalevanko and Henry, 2001). This
might be because of: (1) fewer passengers; therefore no encouragement to operators to provide current
transport information; (2) rural areas being sparsely populated, making it difficult to collect travel/traffic
information from the system; (3) the widespread use of request stops by the passengers; (4) and the higher
cost associated with developing, deploying and maintaining these technologies in a rural environment
(Velaga et al., 2012).
Recent advances in mobile communications and digital technologies may offer the prospect of addressing
these gaps in provision of TAP RTPI during disruptions. Examples of such advances include broadband
internet access coverage, smart mobile phones and personal computer ownership. RTPI can be
disseminated to the end users through their phone through various channels (e.g. App, SMS) (Velaga et
al., 2012a). Further, collaborative working of different public transport service providers and
development of a system to integrate information from different modes of transport with other open data
(e.g., weather reports, road works) could further enhance the RTPI provision during disruptions.
Moreover, development of a formal system that allows linking a passenger who is waiting for a public
transport in a remote area with a co-passenger who has already commenced their journey and who has
knowledge of the current situation and disruption could take the system to further level. Capturing,
disseminating and linking with other data is a complex information exchange but may be feasible through
social media, as individuals are more likely produce TAP information directed to personal ties, and
trusting information coming from personal ties. However, there are some barriers to bringing together
transport and technology solutions to enhance RTPI provision during disruptions particularly in remote
areas; this includes signal coverage and network availability, and the skills required to use the latest
digital systems. This leads to notions of ‘rural digital divide’ with various factors such as geographical
area, gender, age, income, and race (Velaga et al., 2012).
20
In terms of managerial practice this study provides an initial stepping stone towards understanding the
interplay between disruption and RTPI systems and informing the necessary technological advances that
will support the rural dwellers in trip recovery during mobility disruption. Given the presence of
‘resilience’ as a concept in government vernacular particularly in the emergency and disaster management
sectors, this method is ideally suited to provide transport service planners with a guide to develop services
better suited to passenger behaviour and resilience.
The interplay between technologies, disruption and communities in rural areas is an under-researched
area. The findings of this work relate to both the latest reorientation of the industry towards the
passengers’ needs, and the new wave of research exploring social media and mobility disruptions. Future
research should concentrate in utilising alternative methodologies to address the limitations of this work.
For example, questionnaires could be used in multiple research areas to further explore the passenger
recovery phases to disruption and community resilience against the provision of real time information in
the various communities. In respect to the later it is recommended that work should expand the current
findings. In particular, explore the interplay between the use of kinship networks, the passenger recovery
phases to disruption, and community resilience during disruption.
7 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 acknowledge the RCUK research grant EP/J000604/2.
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