Predicting Travel Patterns Under Disruption and Change

预测颠覆和变化下的出行模式

• 批准号: 2887424
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2887424
• 关键词: Predicting; Travel; Patterns; Under; Disruption

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.

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。这部分是由于这些事件的临时性质----有些关闭是提前很久就计划和宣布的，而另一些则是完全出乎意料的事件----以及关闭的影响、信息的可靠性和现有的最佳替代办法的不确定性。提高我们对不同情景下的行为的理解，并开发一个强大的行为模型，将有利于交通部门寻求促进行为改变以改善服务。该项目将对中断后旅行行为的变化进行分析，并开发新的基于代理人的模型，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。除了促进知识交流外，合作的目的将是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服务团队中花费一些时间。预计该项目将分为三个主要阶段。第1阶段-破坏下的行为分析在第一阶段，我们将回顾之前的研究文献，以更新和扩展破坏分类法（例如Zhu和Levinson 2010，Marsden等人。，2020），以及行为改变，考虑到先前信息的作用（例如，消息传递活动、媒体报道）、旅行模式、位置、事件时间和其它相关因素，对旅行模式选择的改变（例如，Schaefer等人，2021）、路线选择（例如Marra和Corman，2023）和出发时间（例如货车Exel等人，2009，Rahimi等人，2019）。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，并评估缓解干预措施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下去，从而确定哪些因素有助于对行为进行长期（“粘性”）调整。这一初步分析将揭示关于对不同类型事件的反应如何不同的新信息，它还将为随后的建模阶段提供信息。第二阶段-对中断下的行为进行建模通过对先前中断事件的分析，我们将考虑如何建模和预测未来中断下的行为。这一阶段的工作将建立在最新的计算模型方法和出行行为和行为变化的定性理论以及新的流动数据来源的基础上，以推导出不同类型中断事件下的出行行为预测模型。我们预计，这些模型将反映选择的不确定性，这些方案固有的方面，区分他们从“正常”条件下的旅行行为。这种模型有希望作出新的贡献的研究和practice.Stage 3 -代理为基础的模型和情景explorationThe新的行为模型将被集成在一个代理为基础的模型（ABM），建立使用MATSim建模框架。研究人员将受益于西米德兰兹地区现有的MATSim交通模型，但预计为了确保这些模型的顺利整合，需要进一步校准，验证和开发模型。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。对情景的探索将仔细考虑它们在评估中的作用-制定描述不同中断事件影响的整个运输系统的措施。这些评估措施将纳入影响的各个方面，包括社会经济公平和空气质量影响。