SCC-CIVIC-PG Track A: Leveraging AI-assist Microtransit to Ameliorate Spatiotemporal Mismatch between Housing and Employment

SCC-CIVIC-PG Track A：利用人工智能辅助微交通改善住房和就业之间的时空错配

• 批准号: 2043611
• 负责人: Dongxiao Zhu
• 金额: $ 4.99万
• 依托单位: Wayne State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-15 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2043611&HistoricalAwards=false
• 关键词: SCC; CIVIC; PG; Track; Leveraging

COVID-19 disproportionately affects the low-wage workers whose spatiotemporal mobility pattens, e.g., between housing and job, have dramatically changed. Microtransit service has been recently launched in Detroit to complement the existing public transit options. Despite the initial success, a salient issue is how to effectively and efficiently utilize microtransit resources to ameliorate spatiotemporal mismatch between employment and housing for low-wage workers. With the rise of Artificial Intelligence (AI) and increasingly available smart mobility data, the vision of this research project is to create a dynamic routing prediction system based on learning the hourly mobility patterns between jobs and housing. It is designed for the stakeholders (i.e., community advocates and public transport authority) to visualize and forecast the mismatch between employment and housing, which is translated into a dynamic trip demand that can be used to design adaptive routing algorithms to optimize the allocation of microtransit resources and to enhance micromobility via minimizing the rider’s first/last mile. Currently public transportation with fixed routes and schedules are periodically tweaked and/or augmented to ameliorate the ever-changing spatial mismatch. Despite its long-term effectiveness, it is not sufficiently flexible to adapt to the hourly spatiotemporal variation of jobs-housing mobility patterns primarily from the hourly paid workers. The long-term goal of this project is to work with civic partners in the city of Detroit to (1) design, implement and deploy an AI-assist microtransit system to ameliorate the spatiotemporal mismatch between housing and employment, particularly for the low-wage workers residing in the under resourced neighborhoods; and (2) use geocoded socioeconomic data to identify the community with disparities in mobility and deploy smart mobility technology to reduce the disparities and foster thriving communities. The project’s near-term objective is to leverage and power the existing microtransit service with cutting-edge technology and select a few spatiotemporally mismatched regions in Detroit as the testbed for our smart mobility strategy.The research innovation is expected to provide immediate, low-cost yet effective public transit solutions that are expected to bring an immediate benefit to the vulnerable communities in Detroit by significantly reducing transit risk, commute time/distance and trip cost. It can be replicated to other US cities to ameliorate the spatiotemporal mismatch between housing and employment. In addition, it can provide insight for designing long-term intervention strategies to eliminate the mismatch and reduce the mobility disparities, for example, government to launch new transportation options and create jobs; and builders to develop housing in the mismatched regions.This project is in response to Track A – CIVIC Innovation Challenge - Communities and Mobility a collaboration with NSF and the Department of Energy.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.

COVID-19对低收入工人的影响尤为严重，他们的时空流动模式（如住房和工作之间的流动模式）发生了巨大变化。底特律最近推出了微型交通服务，以补充现有的公共交通选择。尽管取得了初步的成功，但一个突出的问题是如何有效和高效地利用微交通资源来改善低收入工人就业和住房之间的时空不匹配。随着人工智能（AI）的兴起和越来越多的智能移动数据的可用性，本研究项目的愿景是创建一个基于学习工作和住房之间每小时移动模式的动态路由预测系统。它是为利益相关者（即社区倡导者和公共交通当局）设计的，用于可视化和预测就业和住房之间的不匹配，这被转化为动态出行需求，可用于设计自适应路由算法，以优化微交通资源的分配，并通过最小化乘客的第一/最后一英里来增强微交通。目前，固定路线和时间表的公共交通定期调整和/或增加，以改善不断变化的空间不匹配。尽管它具有长期的有效性，但它没有足够的灵活性来适应主要来自小时工的工作-住房流动模式的小时时空变化。该项目的长期目标是与底特律市的公民合作伙伴合作：(1)设计、实施和部署人工智能辅助微交通系统，以改善住房和就业之间的时空不匹配，特别是对于居住在资源不足社区的低工资工人；(2)利用地理编码的社会经济数据识别出行差异的社区，并部署智能出行技术，以缩小差距，促进社区繁荣。该项目的近期目标是利用尖端技术为现有的微交通服务提供动力，并在底特律选择一些时空不匹配的地区作为我们智能交通战略的试验台。这项研究创新有望提供即时、低成本且有效的公共交通解决方案，通过显著降低交通风险、通勤时间/距离和出行成本，为底特律的弱势社区带来立竿见影的好处。它可以复制到美国其他城市，以改善住房和就业之间的时空不匹配。此外，它还可以为设计长期干预策略提供见解，以消除不匹配和减少流动性差距，例如，政府推出新的交通选择并创造就业机会；建筑商在不匹配的地区开发住房。这个项目是响应轨道A -公民创新挑战-社区和流动性与美国国家科学基金会和能源部合作。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。