Collaborative Research: RAPID: Addressing Transit Accessibility and Public Health Challenges due to COVID-19

COVID-19疫情不仅扰乱了数百万人的生活，也为公共交通机构带来了紧迫的运营和调度挑战。各机构正在努力保持交通可达性，减少资源，改变乘客模式，由于社交距离造成的车辆容量限制，以及由于司机无法使用而减少的服务。一些过境机构也开始帮助当地粮食银行向收容所运送粮食，如果没有最佳规划，现有资源将更加紧张。与此同时，缺乏情境信息也给需要了解车辆上有哪些座位以确保足够距离的乘客带来了挑战。与田纳西州查塔努加和田纳西州纳什维尔的运输机构合作，拟议的研究将快速开发综合运输运营优化算法，该算法将考虑紧急车辆维护要求（即，消毒）。该研究的一个关键组成部分是设计基于摄像头的隐私保护乘客检测方法，该方法可以帮助通勤者考虑社交距离约束，提供有关可用座位的实时信息。通过该计划开发的数据集和算法将迅速发布给研究界，以鼓励更广泛的合作努力，这将有助于面临类似挑战的其他公交机构。拟议研究的智力价值在于设计和评估固定线路和按需公交的综合运营优化（包括辅助运输），这就要求最大限度地扩大过境准入，但最大限度地减少接触。优化的挑战是由于非典型旅行时间和旅行需求分布而产生的不确定性，由于场景的变化，这两者都需要再次在线学习。虽然可以像以前的工作那样单独优化这些交通模式，但综合优化可以带来更好的结果。然而，这是困难的，因为这些问题的解决空间非常大。该方法是基于快速组成和比较的有效性原则的决策理论的方法，如蒙特卡洛树搜索，最佳行程分配使用整数规划和特定问题的算法，并按需运输的需求汇总。为了开发一个针对不同出行需求的模型，该研究使用了新型神经网络架构，通过已经安装在公交车辆上的摄像头来实时估计使用情况和座位模式。这将使过境机构能够获得旅行需求，即使他们正在运行免费业务，以尽量减少与司机的接触。与合作伙伴运输机构合作，研究人员将能够在这些具有挑战性的时期使社区更容易获得服务。 该项目与智能互联社区计划直接相关，因为它证明了技术和社会研究与社区参与的重要性，以提高交通系统对流行病和其他危机的抵御能力。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标准进行评估，被认为值得支持。

The COVID-19 pandemic has not only disrupted the lives of millions but also created exigent operational and scheduling challenges for public transit agencies. Agencies are struggling to maintain transit accessibility with reduced resources, changing ridership patterns, vehicle capacity constraints due to social distancing, and reduced services due to driver unavailability. A number of transit agencies have also begun to help the local food banks deliver food to shelters, which further strains the available resources if not planned optimally. At the same time, the lack of situational information is creating a challenge for riders who need to understand what seating is available on the vehicles to ensure sufficient distancing. In partnership with the transit agencies of Chattanooga, TN, and Nashville, TN, the proposed research will rapidly develop integrated transit operational optimization algorithms, which will provide proactive scheduling and allocation of vehicles to transit and cargo trips, considering exigent vehicle maintenance requirements (i.e., disinfection). A key component of the research is the design of privacy-preserving camera-based ridership detection methods that can help provide commuters with real-time information on available seats considering social-distancing constraints. The datasets and algorithms developed through this program will be swiftly released to the research community in order to encourage a wider collaborative effort that will help other transit agencies that face similar challenges.The intellectual merit of the proposed research lies in the design and evaluation of integrated operational optimization for both fixed-line and on-demand transit (including paratransit) under atypical capacity constraints, which requires maximizing transit access but minimizing contact. The challenge for optimization is the uncertainties that arise due to the atypical travel time and travel demand distribution, both of which need to be learned online again due to the changed scenarios. While it is possible to optimize these transit modes separately as prior work has done, integrated optimization can lead to significantly better results. However, this is difficult as the solution space of these problems is very large. The approach is based on rapidly composing and comparing the effectiveness of principled decision-theoretic approaches such as Monte Carlo tree search, optimal trip assignments using integer programming and problem-specific heuristics, and demand aggregation for on-demand transit. To develop a model for varying travel demand, the research uses novel neural network architectures to estimate usage and seating patterns in real-time from cameras that are already installed within transit vehicles. This will enable transit agencies to obtain travel demand even when they are running fare-free operations to minimize contact with drivers. Working with partner transit agencies, the researchers will be able to make the services more accessible for the community during these challenging times. This project directly relates to Smart and Connected Communities program as it demonstrates the importance of integration of technical and social research with strong community engagement in improving resilience of transit systems due to pandemics and other crises.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.