Responsible Automation for Inclusive Mobility (RAIM): Using AI to Develop Future Transport Systems that Meet the Needs of Ageing Populations

包容性出行的负责任自动化 (RAIM)：利用人工智能开发满足人口老龄化需求的未来交通系统

• 批准号: ES/T012587/1
• 负责人: Ed Manley
• 金额: $ 64.4万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FT012587%2F1
• 关键词: Responsible; Automation; Inclusive; Mobility; RAIM

To capture the full social and economic benefits of AI, new technologies must be sensitive to the diverse needs of the whole population. This means understanding and reflecting the complexity of individual needs, the variety of perceptions, and the constraints that might guide interaction with AI. This challenge is no more relevant than in building AI systems for older populations, where the role, potential, and outstanding challenges are all highly significant. The RAIM (Responsible Automation for Inclusive Mobility) project will address how on-demand, electric autonomous vehicles (EAVs) might be integrated within public transport systems in the UK and Canada to meet the complex needs of older populations, resulting in improved social, economic, and health outcomes. The research integrates a multidisciplinary methodology - integrating qualitative perspectives and quantitative data analysis into AI-generated population simulations and supply optimisation. Throughout the project, there is a firm commitment to interdisciplinary interaction and learning, with researchers being drawn from urban geography, ageing population health, transport planning and engineering, and artificial intelligence.The RAIM project will produce a diverse set of outputs that are intended to promote change and discussion in transport policymaking and planning. As a primary goal, the project will simulate and evaluate the feasibility of an on-demand EAV system for older populations. This requires advances around the understanding and prediction of the complex interaction of physical and cognitive constraints, preferences, locations, lifestyles and mobility needs within older populations, which differs significantly from other portions of society. With these patterns of demand captured and modelled, new methods for meeting this demand through optimisation of on-demand EAVs will be required. The project will adopt a forward-looking, interdisciplinary approach to the application of AI within these research domains, including using Deep Learning to model human behaviour, Deep Reinforcement Learning to optimise the supply of EAVs, and generative modelling to estimate population distributions. A second component of the research involves exploring the potential adoption of on-demand EAVs for ageing populations within two regions of interest. The two areas of interest - Manitoba, Canada, and the West Midlands, UK - are facing the combined challenge of increasing older populations with service issues and reducing patronage on existing services for older travellers. The RAIM project has established partnerships with key local partners, including local transport authorities - Winnipeg Transit in Canada, and Transport for West Midlands in the UK - in addition to local support groups and industry bodies. These partnerships will provide insights and guidance into the feasibility of new AV-based mobility interventions, and a direct route to influencing future transport policy. As part of this work, the project will propose new approaches for assessing the economic case for transport infrastructure investment, by addressing the wider benefits of improved mobility in older populations.At the heart of the project is a commitment to enhancing collaboration between academic communities in the UK and Canada. RAIM puts in place opportunities for cross-national learning and collaboration between partner organisations, ensuring that the challenges faced in relation to ageing mobility and AI are shared. RAIM furthermore will support the development of a next generation of researchers, through interdisciplinary mentoring, training, and networking opportunities.

为了充分利用人工智能的社会和经济效益，新技术必须对整个人口的不同需求保持敏感。这意味着理解和反映个人需求的复杂性，感知的多样性，以及可能指导与人工智能交互的限制。这一挑战并不比为老年人构建人工智能系统更重要，在老年人中，角色、潜力和突出的挑战都非常重要。RAIM（包容性交通负责任自动化）项目将解决如何将按需电动自动驾驶汽车（EV）集成到英国和加拿大的公共交通系统中，以满足老年人口的复杂需求，从而改善社会，经济和健康成果。该研究整合了多学科方法-将定性观点和定量数据分析整合到人工智能生成的人口模拟和供应优化中。在整个项目过程中，我们坚定地致力于跨学科的互动和学习，研究人员来自城市地理学、老龄人口健康、交通规划和工程以及人工智能。RAIM项目将产生一系列不同的成果，旨在促进交通政策制定和规划方面的变革和讨论。作为一个主要目标，该项目将模拟和评估老年人按需EAV系统的可行性。这就需要在理解和预测老年人的身体和认知限制、偏好、地点、生活方式和流动需求之间复杂的相互作用方面取得进展，因为老年人与社会其他部分有很大不同。随着这些需求模式的捕获和建模，将需要通过优化按需EV来满足这种需求的新方法。该项目将采用前瞻性的跨学科方法在这些研究领域中应用人工智能，包括使用深度学习来模拟人类行为，深度强化学习来优化EV的供应，以及生成模型来估计人口分布。该研究的第二个组成部分涉及探索两个感兴趣区域内老龄化人口按需电动汽车的潜在采用。这两个感兴趣的地区--加拿大的马尼托巴和英国的西米德兰兹--正面临着老年人口增加和服务问题以及老年旅客现有服务的顾客减少的综合挑战。RAIM项目已与当地主要合作伙伴建立了伙伴关系，其中包括当地运输当局-加拿大的温尼伯运输局和英国的西米德兰兹运输局-以及当地支助团体和行业机构。这些合作伙伴关系将为新的基于自动驾驶的交通干预措施的可行性提供见解和指导，并为影响未来交通政策提供直接途径。作为这项工作的一部分，该项目将提出新的方法来评估交通基础设施投资的经济案例，通过解决改善老年人口流动性的更广泛利益。该项目的核心是致力于加强英国和加拿大学术界之间的合作。RAIM为合作伙伴组织之间的跨国学习和合作提供了机会，确保共同应对老龄化流动性和人工智能所面临的挑战。RAIM还将通过跨学科指导，培训和网络机会支持下一代研究人员的发展。

项目成果

Towards Inclusive Urban Planning: An Elderly-Focused Agent-Based Model of West Midlands

迈向包容性城市规划：西米德兰兹以老年人为中心的代理模型

• DOI: 10.5281/zenodo.7834910
• 发表时间: 2023
• 作者: Prédhumeau M

Towards Inclusive Urban Planning: A Senior-Focused Agent-Based Model of West Midlands

迈向包容性城市规划：西米德兰兹郡以高级代理为基础的模型

• 发表时间: 2023
• 作者: Predhumeau M

Building an Environment for Spatial Modelling of Canadian Cities Using Open Data: A Replicable Workflow Applied to Winnipeg

使用开放数据构建加拿大城市空间建模环境：应用于温尼伯的可复制工作流程

• 发表时间: 2023
• 作者: Predhumeau M

Health, out-of-home activities and digital inclusion in later life: Implications for emerging mobility services

• DOI: 10.1016/j.jth.2021.101311
• 发表时间: 2022-03-01
• 期刊: JOURNAL OF TRANSPORT & HEALTH
• 影响因子: 3.6
• 作者: Carney, Ffion;Kandt, Jens
• 通讯作者: Kandt, Jens

Who is returning to public transport for non-work trips after COVID-19? Evidence from older citizens' smart cards in the UK's second largest city region.

• DOI: 10.1016/j.jtrangeo.2023.103529
• 发表时间: 2023-02
• 期刊: JOURNAL OF TRANSPORT GEOGRAPHY
• 影响因子: 6.1
• 作者: Long, Alfie;Carney, Ffion;Kandt, Jens
• 通讯作者: Kandt, Jens