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

COVID-19不成比例地影响了低工资工人，他们的时空流动模式，例如，住房和工作之间的关系发生了巨大的变化。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，一个突出的问题是如何有效和高效地利用微交通资源，以改善低工资工人的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，该研究项目的愿景是创建一个基于学习工作和住房之间的小时移动模式的动态路由预测系统。它是为利益相关者设计的（即，社区倡导者和公共交通管理局）来可视化和预测就业和住房之间的不匹配，这被转化为动态的出行需求，可以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移动性。目前，具有固定路线和时间表的公共交通定期调整和/或增强，以改善不断变化的空间不匹配。尽管其长期的有效性，它是不够灵活，以适应小时的时空变化的工作，住房流动模式，主要是从小时支付的工人。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的社区的低工资工人;以及（2）使用地理编码的社会经济数据来识别具有移动性差异的社区，并部署智能移动技术来减少差异并促进繁荣的社区。该项目的近期目标是利用先进技术为现有的微交通服务提供动力，并选择底特律的几个时空不匹配的地区作为我们智能移动战略的试验平台。该研究创新有望提供即时，低成本但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。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，它可以为设计长期干预战略提供见解，以消除不匹配和减少流动性差异，例如，政府推出新的交通选择和创造就业机会;和建筑商在不匹配的地区开发住房。这个项目是为了响应轨道A - CIVIC创新挑战-社区和流动性与NSF和能源部合作。该奖项反映了NSF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进行评估，被认为值得支持的搜索.

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.