NSF Convergence Accelerator Track H: Automating Transportation Affordances for People Living with Disabilities Using a Machine Learning Approach

社区参与和就业对健康、福祉、自决和生活质量至关重要。残疾人参与社区和就业的一个主要障碍是缺乏交通工具。在美国近25%的成年残疾人中，大约60%的人处于最佳工作年龄。因此，这些障碍会对经济产生重大影响，并严重限制社会经济向上流动。缺乏交通工具的原因之一是残疾人的出行方式与一般人群不同。例如，他们出行次数较少，倾向于使用较慢的交通工具，旅行距离较短，更多地依赖公共交通。尽管为美国残疾人制定了几十年的民权立法，但由于人行道、公共路权、公交车站和一般的连通性不佳，他们无法获得通往就业场所的安全可靠的路线。改善农村和城市社区这些系统的整合需要可靠的数据，但这些数据的创建成本高昂，而且往往无法获得。数据的缺乏不可避免地阻碍了倡导团体和地方政府理解、透明讨论和做出明智的规划决策，从而改善交通，从而改善就业机会的能力。该项目将创造一种技术，以快速生成数据，为残疾人建立一个更加综合的交通系统。该项目建立在由国家科学基金会（#2125087）和国家残疾、独立生活和康复研究研究所（#90DPCP0004）资助的两个不同且活跃的项目的融合之上。该提案旨在：1)开发一个原型，以自动化公交车站的描绘和功能评估，以整合现有的人行道质量自动化工作；2)探索建筑环境和第一/最后一英里在社区参与中发挥的作用。这项工作旨在为人机交互和计算机视觉做出贡献，特别是在城市规划和残疾研究的交叉领域。我们还通过直接与残疾人合作来改进经验计算模型，努力克服机器学习对少数民族的偏见。技术团队成员将与团队中的残疾专家和设计师进行互动，以弥合阻碍将经验定性信息（日常生活活动）转化为计算模型的学科差距，从而对建筑环境的设计进行解释和评估。这个项目将产生开源的可达性分析和可视化工具，包括第一英里/最后一英里以及人行道、公交车站和道路的质量。该项目的工作可以帮助社区和州一级的交通总体规划，并促进与非政府组织和政府组织的更多伙伴关系。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Community participation and employment is vital to health, well-being, self-determination, and quality of life. A major barrier to community participation and employment for persons with disabilities is the lack of access to transportation. Of the nearly 25% of the adult population in the United States living with a disability, roughly 60% are of prime working age. Thus, these barriers can significantly influence the economy and importantly limit upward socioeconomic mobility. One of the reasons for the lack of access to transportation is that persons with disabilities have different travel patterns compared with the general population. For instance, they tend to make fewer trips, are prone to utilizing slower means of transportation, travel shorter distances and rely more upon public transit. Despite decades of civil rights legislation for Americans with disabilities, access to safe and reliable routes to places of employment are hampered by inaccessible and poor-quality sidewalks, public rights-of-way, bus stops and general connectivity. Improving integration of these systems across rural and urban communities requires reliable data – but these data are expensive to create and often unavailable. The lack of data inevitably hampers the ability for advocacy groups and local governments to understand, transparently discuss, and make informed planning decisions that improve transportation, and thus, employment access. This project will create a technique to rapidly generate data essential to building a more integrated transportation system for people living with disabilities.This project builds on the convergence of two distinct and active projects sponsored by the National Science Foundation (#2125087) and the National Institute on Disability, Independent Living and Rehabilitation Research (#90DPCP0004). This proposal aims to: 1) develop a prototype to automate the delineation and affordance assessment of bus stops to integrate with existing efforts on sidewalk quality automation, and 2) explore the role the built environment and first/last mile play on community participation to and from places of employment and households. This work is intended to make contributions to Human Computer Interaction and Computer Vision, particularly at the intersection with urban planning and disabilities studies. We also work to overcome challenges of machine learning bias toward minorities by working directly with persons with disabilities to improve empirical computational models. Technical team members will interact with disability experts and designers on the team to bridge disciplinary gaps that hamper efforts to translate experiential qualitative information (activities of daily living) into computational models that make interpretations and evaluations about the design of the built environment. This project will result in open-source accessibility analysis and visualization tools about first/last mile and the quality of sidewalks, bus stops and roadways. The work from this project can aid in community and state-level transportation master planning and spur additional partnerships with NGOs and governmental organizations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.