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, 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.
- Advisor / supervisor
- Ferguson, N. S. (Neil Stuart)
- Resource Type
- DOI
- Date Created
- 2025
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