Thesis
Understanding re-scheduling decisions : behavioural modelling of commuting in disrupted networks
- Creator
- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2026
- Thesis identifier
- T18076
- Person Identifier (Local)
- 201689212
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- Modern transport systems are increasingly exposed to unexpected disruptions, oftentriggered by extreme weather events, introducing significant uncertainty into dailytravel. In response, travellers must adopt response strategies, including altering theirroute, mode of transport, and/or departure time. The complexity of individual travelbehaviour becomes particularly evident under dynamic and uncertain conditions. Inparallel, evolving work arrangements, particularly the rise of hybrid and remoteworking, have added a new dimension to travel behaviour, raising new and importantquestions about how individuals adjust their plans in this context. In making traveldecisions in these tightly constrained circumstances, time pressure frequently arises,reflecting both situational urgency and cognitive limitations. Understanding how suchtime pressure, and the perception of time pressure, influences rescheduling decisions istherefore critical. However, these contextual and cognitive factors remain inadequatelyexplored in the existing transport behaviour literature, highlighting a clear research gapthat this study seeks to address.This thesis investigates how commuters reschedule their daily work-related travelin response to unexpected transport disruptions, particularly within the evolving contextof post-pandemic hybrid and remote working practices. It examines the influence ofperceived time pressure and varying working arrangement scenarios on decisionmaking when adjusting daily plans under disruption conditions. By developingempirically grounded and behaviourally realistic models, the research seeks to uncoverkey patterns of choice behaviour in such circumstances. In parallel, the study advancessimulation capabilities by enhancing an agent-based transport simulation framework tomodel the effects of real-time information provision on multi-dimensional activity-travel rescheduling within a multi-modal network subject to disruptions. Thisframework facilitates a deeper understanding of how individual micro-levelbehavioural 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-basedsimulation model, by incorporating real-time information provision with the timedependent transport network. Multi-dimensional rescheduling options were enabled foragents to adopt for rescheduling choices across a multimodal transport system. Adecision time budget was introduced to reflect the limited window available forrescheduling decisions, thereby enhancing the behavioural realism of the simulationunder time-constrained conditions. The second part of the research focusses on thedesign and implementation of an activity-travel stated preference (SP) experiment,aimed at capturing individual behavioural responses through a series of carefullyconstructed scenarios. These scenarios varied in work arrangement contexts, reflectingdifferences in both importance and flexibility. Respondents were asked to chooseamong alternative options featuring different attribute combinations, making trade-offsunder imposed time pressure to simulate the limited decision-making time availablewhen unexpected transport disruptions occur on the day. The resulting choice data wereanalysed using a nested logit model with heteroscedastic error structures, allowing forvariations in the degree of time pressure perceived across scenarios and choice tasks tobe explicitly modelled.Findings revealed that rescheduling behaviour was shown to be highly contextdependent: individuals with more formal or group-based work commitmentsdemonstrated stricter punctuality preferences. The incorporation of heteroscedasticerror structures uncovered a non-linear relationship between perceived time pressureand decision consistency - choice behaviour was most stable under moderate timepressure but became increasingly stochastic under low or high extremes, suggesting thepresence of cognitive disengagement or rushed judgement. Additionally, remoteworking availability emerged as a significant factor shaping rescheduling decisions. Inscenarios where remote participation in the activity was permitted and widely accepted,it became a viable option, highlighting the strategic value of flexible work arrangementsin sustaining activity participation while ensuring punctuality. This research makes several novel contributions to the field of travel behaviourmodelling. It advances understanding of activity-travel rescheduling under unexpecteddisruption, particularly in the context of evolving post-pandemic work practices. Byintegrating work-related contextual variables and modelling behavioural heterogeneityacross varying levels of perceived time pressure, the study offers a more realistic andbehaviourally grounded representation of commuter decision-making.Methodologically, this research advances the discrete choice literature by applying aheteroscedastic nested logit framework that parameterises scale heterogeneity as afunction of perceived time pressure – an aspect that has been insufficiently examinedin transport behaviour studies. In addition, this research contributes an enhancedMATSim 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 transportplanning and disruption management. Understanding the influence of varying workcontext and perceived time pressure on rescheduling behaviour enables the formulationof more targeted, user-responsive policies. The demonstrated strategic role of remotework highlights the need to integrate flexible work arrangements into transport demandmanagement frameworks. Furthermore, the extended large-scale agent-basedsimulation framework provides a robust analytical tool for assessing the impacts ofnetwork disruptions on travel patterns, urban mobility, and overall transport systemefficiency. This capability offers transport professionals and policymakers a sound basisfor designing more robust and resilient transport management strategies.
- Advisor / supervisor
- Ferguson, N. S. (Neil Stuart)
- Resource Type
- DOI
- Date Created
- 2025
Relations
Items
| Thumbnail | Title | Date Uploaded | Visibility | Actions |
|---|---|---|---|---|
|
|
PDF of thesis T18076 | 2026-06-26 | Public | Download |