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

私家车排放的废气是全球城市空气污染的主要原因。在英国，私人车辆拥有量已经稳定了一段时间，甚至在大流行之前的几年里有所增加（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保持这一趋势。对许多人，特别是残疾人、需要支助或使用与工作有关的工具的人来说，灵活和方便的私人交通工具的需求至关重要。然而，与主动和共享出行模式相比，从排放的角度来看，私家车是一个重大问题。在实现净零碳排放的地方和国家目标的背景下，找到一条平衡移动需求和偏好的道路，从私人模式过渡是非常重要的。交通和工作的未来仍然存在相当大的不确定性，任何对未来移动场景的考虑都必须建立在前瞻性的态度和观点之上。我们可以通过将私家车拥有量的态度变化和未来交通情景与人口变量相结合，来建立洞察力，从而预测出行需求的转变。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家车使用的某些方面，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进行工作。我们设想这个项目分四个阶段进行。综合当前文献中与私家车使用相关的感知、依恋和需求，以及向共享自动驾驶汽车过渡的可能性。开发和开展一项调查，收集与私人和共享模式相关的模式选择缺失或理解不足的方面的数据，重点关注英格兰北部地区的兴趣。在行为改变理论的基础上，结合人口统计学、世界观和（在调查研究中确定的）移动需求等方面，为采用新的移动服务开发一个空间倾向模型。这部分工作将使用微观模拟将调查反应扩大到人口规模。3. 在已有的cosmonnorth基于代理的交通模型的基础上，扩展模拟，将采用新服务的社会过程纳入其中，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的优化交付和调度的技术过程。4. 与利益相关者合作，开发一组案例研究，探索共享自动驾驶汽车服务的交付，探索围绕摄取和不同服务设计参数的不同假设。该模型将处理服务评价、对旅行费用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要影响等问题。该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（LIDA）正在进行的关于基于代理的建模、城市分析和未来交通的大量研究相结合，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).