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

2019冠状病毒病大流行不仅扰乱了数百万人的生活，也给公共交通机构带来了紧迫的运营和调度挑战。在资源减少、乘客模式改变、社交距离造成的车辆容量限制以及缺乏司机导致的服务减少的情况下，各机构正在努力维持交通可达性。一些运输机构也开始帮助当地的食物银行向避难所运送食物，如果没有最佳规划，这将进一步使现有资源紧张。与此同时，缺乏情况信息给乘客带来了挑战，他们需要了解车辆上有哪些座位，以确保足够的距离。与田纳西州查塔努加和田纳西州纳什维尔的交通机构合作，拟议的研究将迅速开发综合交通运营优化算法，考虑到紧急车辆维护要求（即消毒），该算法将主动安排和分配车辆用于运输和货物旅行。该研究的一个关键组成部分是设计基于隐私保护摄像头的乘客检测方法，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界，以鼓励更广泛的合作努力，帮助其他面临类似挑战的交通机构。本研究的思想价值在于设计和评估非典型容量约束下的固定线路和按需交通（包括辅助交通）的综合运营优化，该优化要求最大限度地增加交通访问而最小化接触。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，由于场景的变化，这两者都需要再次在线学习。虽然可以像之前的工作那样分别优化这些传输模式，但综合优化可以带来更好的结果。然而，这是困难的，因为这些问题的解空间非常大。该方法基于快速组合和比较原则决策理论方法的有效性，如蒙特卡洛树搜索，使用整数规划和问题特定启发式的最优行程分配，以及按需运输的需求聚合。为了开发一个不同出行需求的模型，该研究使用了新颖的神经网络架构，从已经安装在交通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免费运营的情况下也能获得出行需求，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充满挑战的时期使社区更容易获得这些服务。该项目与“智能和互联社区”项目直接相关，因为它证明了将技术和社会研究与社区的积极参与相结合对于提高交通系统应对流行病和其他危机的能力的重要性。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。

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.