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

合作研究：RAPID：应对 COVID-19 带来的交通便利性和公共卫生挑战

• 批准号: 2029950
• 负责人: Abhishek Dubey
• 金额: $ 5.49万
• 依托单位: Vanderbilt University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029950&HistoricalAwards=false
• 关键词: Collaborative; Research; RAPID; Addressing; Transit

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.

COVID-19 大流行不仅扰乱了数百万人的生活，还给公共交通机构带来了严峻的运营和调度挑战。各机构正在努力通过减少资源、改变乘客模式、由于社交距离导致的车辆容量限制以及由于司机缺​​勤而减少的服务来维持交通可达性。一些交通机构也开始帮助当地食品银行向避难所运送食物，如果没有进行最佳规划，这将进一步加剧可用资源的紧张。与此同时，情景信息的缺乏给乘客带来了挑战，他们需要了解车辆上有哪些座位以确保足够的距离。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交通机构合作，快速开发综合交通运营优化算法，该算法将主动调度和分配车辆到交通和货运行程，同时考虑到迫切的车辆维护要求（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在考虑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，帮助面临类似挑战的其他交通机构。拟议研究的智力价值在于综合设计和评估在非典型容量限制下优化固定线路和按需交通（包括辅助交通）的运营，这需要最大限度地提高交通便利性，同时最大限度地减少接触。优化的挑战是由于非典型的出行时间和出行需求分布而产生的不确定性，由于场景的变化，这两者都需要重新在线学习。虽然可以像之前的工作那样单独优化这些交通模式，但集成优化可以带来更好的结果。然而，这是很困难的，因为这些问题的解决空间非常大。该方法基于快速组合和比较原则性决策理论方法的有效性，例如蒙特卡罗树搜索、使用整数规划和特定问题启发式的最佳行程分配以及按需交通的需求聚合。为了开发针对不同出行需求的模型，该研究使用新颖的神经网络架构，通过运输车辆内已安装的摄像头实时估计使用情况和座位模式。这将使交通机构即使在实行免费运营时也能获得出行需求，以尽量减少与司机的接触。研究人员与合作伙伴交通机构合作，将能够在这个充满挑战的时期让社区更容易获得这些服务。 该项目与智能和互联社区计划直接相关，因为它证明了将技术和社会研究与社区的大力参与相结合对于提高交通系统因流行病和其他危机而恢复的能力的重要性。该奖项反映了 NSF 的法定使命，并被认为是值得的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来获得支持。

项目成果

Predicting Public Transportation Load to Estimate the Probability of Social Distancing Violations

• 发表时间: 2020
• 作者: J. Martínez;Ayan Mukhopadhyay;Afiya Ayman;Michael Wilbur;Philip Pugliese;Dan;Freudberg;Jonathan M. Gilligan;Aron Laszka;A. Dubey

Transit-Gym: A Simulation and Evaluation Engine for Analysis of Bus Transit Systems

Transit-Gym：用于分析公交系统的模拟和评估引擎

• 发表时间: 2021
• 期刊: 7th IEEE International Conference on Smart Computing (SMARTCOMP 2021
• 作者: Sun, Ruixiao;Neema, Himanshu;Chen, Yuche;Ugirumurera, Juliette;Severino, Joseph;Pugliese, Philip;Laszka, Aron;Dubey, Abhishek
• 通讯作者: Dubey, Abhishek

Impact of COVID-19 on Public Transit Accessibility and Ridership

COVID-19 对公共交通可达性和乘客量的影响

• DOI: 10.1177/03611981231160531
• 发表时间: 2023
• 期刊: Transportation Research Record: Journal of the Transportation Research Board
• 作者: Wilbur, Michael;Ayman, Afiya;Sivagnanam, Amutheezan;Ouyang, Anna;Poon, Vincent;Kabir, Riyan;Vadali, Abhiram;Pugliese, Philip;Freudberg, Daniel;Laszka, Aron
• 通讯作者: Laszka, Aron

Efficient Data Management for Intelligent Urban Mobility Systems

智能城市交通系统的高效数据管理

• 发表时间: 2021
• 期刊: Proceedings of the Workshop on AI for Urban Mobility at the 35th AAAI Conference on Artificial Intelligence (AAAI-21
• 作者: Wilbur, Michael;Pugliese, Philip;Laszka, Aron;Dubey, Abhishek
• 通讯作者: Dubey, Abhishek