Modern transport systems are increasingly exposed to unexpected disruptions, often
triggered by extreme weather events, introducing significant uncertainty into daily
travel. In response, travellers must adopt response strategies, including altering their
route, mode of transport, and/or departure time. The complexity of individual travel
behaviour becomes particularly evident under dynamic and uncertain conditions. In
parallel, evolving work arrangements, particularly the rise of hybrid and remote
working, have added a new dimension to travel behaviour, raising new and important
questions about how individuals adjust their plans in this context. In making travel
decisions in these tightly constrained circumstances, time pressure frequently arises,
reflecting both situational urgency and cognitive limitations. Understanding how such
time pressure, and the perception of time pressure, influences rescheduling decisions is
therefore critical. However, these contextual and cognitive factors remain inadequately
explored in the existing transport behaviour literature, highlighting a clear research gap
that this study seeks to address.
This thesis investigates how commuters reschedule their daily work-related travel
in response to unexpected transport disruptions, particularly within the evolving context
of post-pandemic hybrid and remote working practices. It examines the influence of
perceived time pressure and varying working arrangement scenarios on decisionmaking when adjusting daily plans under disruption conditions. By developing
empirically grounded and behaviourally realistic models, the research seeks to uncover
key patterns of choice behaviour in such circumstances. In parallel, the study advances
simulation capabilities by enhancing an agent-based transport simulation framework to
model the effects of real-time information provision on multi-dimensional activity-travel rescheduling within a multi-modal network subject to disruptions. This
framework facilitates a deeper understanding of how individual micro-level
behavioural responses aggregate to produce system-level outcomes.
To achieve the research objectives, the research is structured into two main parts.
The first part extends the MATSim within-day replanning framework, an agent-based
simulation model, by incorporating real-time information provision with the timedependent transport network. Multi-dimensional rescheduling options were enabled for
agents to adopt for rescheduling choices across a multimodal transport system. A
decision time budget was introduced to reflect the limited window available for
rescheduling decisions, thereby enhancing the behavioural realism of the simulation
under time-constrained conditions. The second part of the research focusses on the
design and implementation of an activity-travel stated preference (SP) experiment,
aimed at capturing individual behavioural responses through a series of carefully
constructed scenarios. These scenarios varied in work arrangement contexts, reflecting
differences in both importance and flexibility. Respondents were asked to choose
among alternative options featuring different attribute combinations, making trade-offs
under imposed time pressure to simulate the limited decision-making time available
when unexpected transport disruptions occur on the day. The resulting choice data were
analysed using a nested logit model with heteroscedastic error structures, allowing for
variations in the degree of time pressure perceived across scenarios and choice tasks to
be explicitly modelled.
Findings revealed that rescheduling behaviour was shown to be highly contextdependent: individuals with more formal or group-based work commitments
demonstrated stricter punctuality preferences. The incorporation of heteroscedastic
error structures uncovered a non-linear relationship between perceived time pressure
and decision consistency - choice behaviour was most stable under moderate time
pressure but became increasingly stochastic under low or high extremes, suggesting the
presence of cognitive disengagement or rushed judgement. Additionally, remote
working availability emerged as a significant factor shaping rescheduling decisions. In
scenarios where remote participation in the activity was permitted and widely accepted,
it became a viable option, highlighting the strategic value of flexible work arrangements
in sustaining activity participation while ensuring punctuality. This research makes several novel contributions to the field of travel behaviour
modelling. It advances understanding of activity-travel rescheduling under unexpected
disruption, particularly in the context of evolving post-pandemic work practices. By
integrating work-related contextual variables and modelling behavioural heterogeneity
across varying levels of perceived time pressure, the study offers a more realistic and
behaviourally grounded representation of commuter decision-making.
Methodologically, this research advances the discrete choice literature by applying a
heteroscedastic nested logit framework that parameterises scale heterogeneity as a
function of perceived time pressure – an aspect that has been insufficiently examined
in transport behaviour studies. In addition, this research contributes an enhanced
MATSim Within-day Replanning module, integrated into an agent-based framework,
capable of simulating the spatial-temporal impacts of real-time information on activity–
travel rescheduling under multi-modal network disruptions.
The findings of this thesis have important practical implications for transport
planning and disruption management. Understanding the influence of varying work
context and perceived time pressure on rescheduling behaviour enables the formulation
of more targeted, user-responsive policies. The demonstrated strategic role of remote
work highlights the need to integrate flexible work arrangements into transport demand
management frameworks. Furthermore, the extended large-scale agent-based
simulation framework provides a robust analytical tool for assessing the impacts of
network disruptions on travel patterns, urban mobility, and overall transport system
efficiency. This capability offers transport professionals and policymakers a sound basis
for designing more robust and resilient transport management strategies.
| Date of Award | 8 Jun 2026 |
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| Original language | English |
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| Awarding Institution | - University Of Strathclyde
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| Sponsors | University of Strathclyde |
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| Supervisor | Neil Ferguson (Supervisor) & Daniele Zonta (Supervisor) |
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