Canada-UK AI 2019 : Responsible Automation for Inclusive Mobility (RAIM) : Using AI to Develop Future Transport Systems that Meet the Needs of Ageing Populations

加拿大-英国 AI 2019：包容性出行的负责任自动化 (RAIM)：利用人工智能开发满足老龄化人口需求的未来交通系统

• 批准号: 548594-2019
• 负责人: Mehran, BabakB
• 金额: $ 10.68万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749238
• 关键词: Canada; UK; AI; 2019; Responsible

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 o folder 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.

为了捕捉AI的全部社会和经济利益，新技术必须对整个人群的各种需求敏感。这意味着了解并反映个人需求的复杂性，各种看法以及可能指导与AI相互作用的约束。 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 o folder populations, resulting in improved social, economic, and health outcomes.该研究集成了多学科方法论 - 将定性观点和定量数据分析整合到AI生成的人群模拟和供应优化中。在整个项目中，对跨学科互动和学习的坚定承诺，研究人员从城市地理，人口健康，运输计划和工程以及人工智能中汲取灵感。RAIM项目将产生一系列旨在促进运输政策和计划中的变革和讨论的产量。作为一个主要目标，该项目将模拟和评估对较老人群的按需EAV系统的可行性。这就需要围绕对物理和认知约束，偏好，位置，生活方式和流动性需求的复杂相互作用的理解和预测进行进步，这与社会其他部分有很大不同。通过捕获和建模的这些需求模式，将需要通过优化按需EAV来满足这种需求的新方法。该项目将采用一种前瞻性的跨学科方法来应用AI在这些研究领域中的应用，包括使用深度学习来建模人类行为，深入的强化学习以优化EAV的供应以及生成模型来估算人口分布。研究的第二个组成部分涉及探索在两个地区范围内的eavs aging aging eavs的潜在采用。这两个感兴趣的领域 - 加拿大曼尼托巴省和英国西米德兰兹 - 面临着将老年人口随着服务问题而增加并减少对年长旅行者现有服务的赞助的综合挑战。 RAIM项目还与当地运输当局（包括当地运输机构）建立了合作伙伴关系 - 加拿大温尼伯过境以及英国西米德兰兹的运输 - 除了当地的支持小组和行业机构。这些伙伴关系将为基于新的AV机动性干预措施的可行性以及影响未来运输政策的直接途径提供见解和指导。作为这项工作的一部分，该项目将提出新的方法来评估运输基础设施投资的经济案例，通过解决改善旧人口的流动性的更广泛好处。该项目的核心是加强英国和加拿大学术社区之间的协作的承诺。 Raim为合作伙伴组织之间的跨国学习和协作提供了机会，确保共享与移动性和AI相关的挑战。 Raim此外，将通过跨学科的指导，培训和网络机会来支持下一代研究人员的发展。