Sharing the Road: Exploring transitions away from private vehicle ownership through agent-based modelling

私家车的排放是造成世界各地城市空气污染的主要因素。在英国，私人车辆拥有量在一段时间内一直保持稳定，甚至在大流行之前的几年有所增加（DfT，2022），公共交通不足、既定的土地使用和城市形态以及个人偏好等因素都有助于维持这一趋势。对灵活且易于使用的私人交通的需求对于许多人来说至关重要，特别是那些有残疾、支持要求或与工作相关的用途的人。然而，从排放的角度来看，与主动和共享出行模式相比，私人车辆是一个重要问题。在地方和国家实现净零碳排放目标的背景下，平衡出行需求和偏好，从私人模式过渡到私人模式是非常重要的。交通的未来和工作的未来仍然存在相当大的不确定性，任何对未来出行场景的考虑都必须建立在前瞻性的态度和观点之上。我们可以通过将私人车辆所有权和未来交通情景的态度变化与人口统计变量相结合来建立见解，以预测移动需求的转变。本研究的重点将考虑不同模式的共享自动驾驶汽车服务如何满足英格兰北部地区的出行需求。共享自动驾驶汽车有可能取代私人车辆使用的某些方面，但需要围绕建立这些需求的时空模式以及满足这些需求的可行性开展工作。我们设想该项目分为四个阶段。综合当前关于私人车辆使用的看法、依恋和需求以及向共享自动驾驶车辆过渡的潜力的文献。制定并开展一项调查，收集关于私营和共享模式的模式选择中缺失或了解不多的方面的数据，重点放在感兴趣的北方英格兰地区。根据行为变化理论，结合人口统计学、世界观和流动需求（如调查研究中所确定的），开发新流动服务的空间倾向模型。这部分工作将使用微观模拟来扩大对人口规模的调查反应。3.在现有的基于CoSMoNorth代理的交通模型的基础上，扩展模拟以纳入围绕采用新服务的社会过程，构建包含倾向建模的选择模型，以及共享自动驾驶汽车服务优化交付和调度的技术流程。4.与利益相关者合作，开发一系列案例研究，探索共享自动驾驶汽车服务的交付，探索围绕采用和不同服务设计参数的不同假设。该模型将解决服务评估问题，产生对旅行成本和排放变化的估计，以及对社会公平、城市设计和土地使用等方面的次级影响。博士项目将与地理学院，交通研究所和利兹数据分析研究所（利达）正在进行的基于代理的建模，城市分析和未来移动性的大量研究相结合并从中受益。

Emissions from private vehicle ownership are a major contributor to air pollution in cities worldwide. In the UK, private vehicle ownership has been stable for some time, and even increased in years prior to the pandemic (DfT, 2022), with factors such as inadequate public transport, established land use and urban forms, and personal preference all contributing to the maintenance of this trend. The need for flexible and easily accessible private transport is of fundamental importance for many people, particularly those with disabilities, support requirements, or work-related uses. However, private vehicles are a significant issue from an emissions perspective, in contrast to active and shared travel modes. And in the context of local and national targets on achieving Net Zero carbon emissions, navigating a course that balances mobility needs and preferences, with transitions away from private modes is highly pertinent.There remains considerable uncertainty around the future of transportation, and the future of work, and any consideration of future mobility scenarios must build on prospective attitudes and perspectives. We can build insights here by integrating attitudinal variations in private vehicle ownership and future transportation scenarios with demographic variables, to create predictions of mobility demand transitions. The focus of this study will consider how different models of shared autonomous vehicle service can meet travel demand within the North of England region. Shared AVs have the potential to replace some aspects of private vehicle use, but work needs to be done around establishing the spatiotemporal patterns of those demands, and the feasibility of meeting them.We envisage four stages to this project.1. Synthesise current literature around perceptions, attachments, and needs in relation to private vehicle use, and the potential for transition to shared autonomous vehicles. Develop and undertake a survey to collect data on missing or poorly understood facets of mode choice in relation to private and shared modes, with a focus on the Northern England region of interest.2. Develop a spatial propensity model for uptake of the new mobility service, based on theories of behaviour change, incorporating aspects of demographics, worldview, and mobility need (as identified within the survey study). This part of the work will use microsimulation to scale up survey responses to population scale. 3. Building on the pre-existing CoSMoNorth agent-based transportation model, extend the simulation to incorporate social processes around adoption of new services, building choice models that incorporate the propensity modelling, and technical processes on the optimised delivery and scheduling of the shared autonomous vehicle service. 4. In partnership with stakeholders, develop a set of case studies that explore the delivery of shared autonomous vehicle services, with an exploration of different assumptions around uptake and different service design parameters. The modelling will address issues of service evaluation, generating estimates of changes in travel cost and emissions, as well as secondary impacts on aspects including social equity, urban design, and land use. The PhD project will interface with and benefit from the vast array of research into agent-based modelling, urban analytics, and future mobility ongoing in the School of Geography, Institute for Transport Studies, and Leeds Institute for Data Analytics (LIDA).