Predicting Travel Patterns Under Disruption and Change

中断、事件、事故和关闭是城市交通系统的日常特征。然而，我们对人们在每种情况下如何改变旅行行为的理解却知之甚少。这在一定程度上是由于这些事件的临时性质--一些关闭是提前计划和广告的，而另一些则是完全意想不到的事件--以及围绕关闭的影响、信息的可靠性和最佳可用替代方案的不确定性。改善我们对不同情景下的行为的理解，并发展一个强大的行为模式，将有助于交通当局寻求推动行为变化以改善服务。该项目将对中断后出行行为的变化进行分析，并开发新的基于代理的模型，以预测未来中断的结果。该项目将由西米德兰兹郡交通局(TfWM)的同事共同监督。合作的目的除了促进知识交流外，还将为建模提供现实世界的应用和背景。预计研究人员将在Data Insight Service团队中花费一些时间进行安置。预计该项目将有三个主要阶段。阶段1--中断下的行为分析在第一阶段，我们将回顾先前的研究文献，以更新和扩展中断的分类(例如，朱和莱文森2010，Marsden等人，2020)，并考虑先前信息(例如，消息传递活动、媒体报道)、旅行方式、位置、事件时间和其他相关因素在旅行方式选择(例如，Schaefer等人，2021)、路线选择(例如，Marra和Corman，2023)、和出发时间(例如van Exel等人)2009年，Rahimi等人，2019年)。有了这个框架，我们将使用移动数据源来分析中断事件下需求的总体变化，并评估缓解干预对调整需求的影响程度。我们还将考虑服务中断的影响如何随着时间的推移而持续，确定哪些服务中断会导致对行为的长期(粘性)调整。这一初步分析将揭示关于不同类型事件的反应如何变化的新信息，它还将为随后的建模阶段提供信息。阶段2-对中断情况下的行为进行建模通过对先前中断事件的分析，我们将考虑如何对未来中断情况下的行为进行建模和预测。这一阶段的工作将建立在出行行为和行为变化的最新计算建模方法和定性理论以及新的机动性数据来源的基础上，以得出不同类型干扰事件下的出行行为预测模型。我们预计，这些模型将反映这些情景固有的选择不确定性的方面，这些不确定性将它们与“正常”条件下的出行行为区分开来。这类模型有望为研究和实践做出新的贡献。阶段3-基于主体的模型和情景探索新的行为模型将被集成到基于主体的模型(ABM)中，该模型使用MATSim建模框架建立。研究人员将受益于西米德兰兹地区现有的MATSim运输模型，但预计为了确保这些模型的顺利整合，将需要对该模型进行进一步的校准、验证和开发。一旦完全开发，ABM将允许测试与TfWM同事共同开发的不同未来场景。对情景的探讨将仔细考虑其在评估中的作用--制定描述不同中断事件影响的整个运输系统的措施。这些评估措施将包括影响的各个方面，包括社会经济公平和空气质量影响。

Disruptions, events, incidents, and closures are daily characteristics of urban transport systems. Yet, our understanding of how people change their travel behaviours in each situation is poorly understood. This is partly due to the ad hoc nature of these events - some closures being planned and advertised well in advance, while others being completely unexpected incidents - as well as the uncertainty surrounding the impact of closures, reliability of information, and the best available alternative. Improving our understanding of behaviour under different scenarios, and development of a robust model of behaviour, would be beneficial to transport authorities seeking to promote behaviour change to improve services.This project will undertake analyses of changes in travel behaviour following disruption and develop new agent-based models that predict the outcomes of future disruptions. The project will co-supervised by colleagues at Transport for West Midlands (TfWM). The aim of the collaboration will be to provide real-world application and context for the modelling, in addition to promoting knowledge exchange. It is anticipated the researcher will spend some time spent in placement with the Data Insight Service Team. It is envisaged there will be three main stages to the project.Stage 1 - Behavioural analysis under disruptionIn this first stage, we will review prior research literature to update and extend taxonomies of disruption (e.g. Zhu and Levinson 2010, Marsden et al., 2020), and behaviour change, considering the role of prior information (e.g., messaging campaign, media reports), travel mode, location, time of event, and other relevant factors, on changes in travel mode choice (e.g. Schaefer et al., 2021), route choice (e.g. Marra and Corman, 2023), and departure time (e.g. van Exel, et al. 2009, Rahimi et al, 2019) . With this framework in place, we will use mobility data sources to analyse aggregate changes in demand under disruption incidents and assess the extent to which mitigating interventions had on adjusting demand. We will also consider how the impacts of service disruptions persist through time, establishing which lend themselves to longer-term ('sticky') adjustments to behaviour. This initial analysis will undercover new information on how responses vary in reaction to different types of events, it will also inform the subsequent modelling stages. Stage 2 - Modelling behaviour under disruptionInformed by analysis of prior disruption events, we will then consider how to model and predict behaviours future under disruption. This stage of work will build on the latest computational modelling approaches and qualitative theories of travel behaviour and behaviour change, and new mobility data sources, to derive predictive models of travel behaviour under different types of disruption event. We anticipate that these models will reflect facets of choice uncertainty, inherent to these scenarios, that differentiate them from travel behaviours under 'normal' conditions. Such models have the promise of making novel contributions to research and practice.Stage 3 - Agent-based model and scenario explorationThe new behavioural model will be integrated within an agent-based model (ABM), built using the MATSim modelling framework. The researcher will benefit from an existing MATSim transport model of the West Midlands region, but it is anticipated that to ensure smooth integration of these models, further calibration, validation, and development of the model will be required. Once fully developed, the ABM will allow the testing of different future scenarios, co-developed with TfWM colleagues. The exploration of scenarios will carefully consider their role in appraisal - producing measures of the entire transport system that describe the impacts of different disruption events. These appraisal measures will incorporate dimensions of impact including socioeconomic equity and air quality impacts.