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

新冠肺炎疫情不仅扰乱了数百万人的生活，还给公共交通机构带来了紧迫的运营和调度挑战。各机构正在努力维持交通可达性，资源减少，乘客模式发生变化，社会距离造成车辆容量限制，司机不可用导致服务减少。一些转运机构也开始帮助当地的食品银行向避难所运送食物，如果不进行最佳规划，这将进一步加剧可用资源的紧张。与此同时，情况信息的缺乏给骑车人带来了挑战，他们需要了解车辆上有哪些座位可用，以确保足够的距离。拟议的研究将与田纳西州查塔努加和田纳西州纳什维尔的公交机构合作，迅速开发综合公交运营优化算法，考虑到迫切的车辆维护要求(即消毒)，提供主动调度和分配车辆到公交和货运行程。这项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在考虑到社交距离限制的情况下提供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社区，以鼓励更广泛的合作努力，以帮助其他面临类似挑战的公交机构。拟议研究的智力价值在于，在非典型能力约束下，设计和评估固定线路和按需公交(包括辅助公交)的综合运营优化，这需要最大限度地利用公交通道，但最大限度地减少接触。优化的挑战是由于非典型的旅行时间和旅行需求分布而产生的不确定性，由于情景的变化，这两者都需要再次在线学习。虽然可以像以前的工作那样分别优化这些交通方式，但综合优化可以产生明显更好的结果。然而，这是困难的，因为这些问题的解空间非常大。该方法基于快速合成和比较蒙特卡罗树搜索、使用整数规划和特定问题启发式的最优出行分配以及按需公交的需求聚合等原则性决策理论方法的有效性。为了开发一个针对不同出行需求的模型，这项研究使用了新的神经网络结构，从已经安装在公交车辆上的摄像头实时估计使用情况和座位模式。这将使公交机构即使在运营免费运营的情况下也能获得出行需求，以最大限度地减少与司机的接触。与合作的运输机构合作，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这些服务。该项目与智能互联社区计划直接相关，因为它展示了将技术和社会研究与强大的社区参与相结合，以提高因流行病和其他危机而导致的交通系统弹性的重要性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.