Urban Transit System Diagnosis, Monitoring, and Management Using Mobility Sensing Data

自动驾驶汽车(AV)和共享移动服务等新兴技术在交通领域的采用将为城市交通创造一个革命性的未来。然而，这一趋势可能会对公共交通系统和公共交通服务在我们社会中所保护的公平、可访问性、可负担性和环境可持续性等社会价值观造成生存威胁。尽管存在这种威胁，公共交通系统运营和规划正在从一个曾经数据匮乏的领域转变为交通规划和管理中数据最丰富的领域之一。随着公共交通系统中移动性感知数据和自动化数据源的出现和可用，有机会执行更好的系统诊断，增强运营解决方案，并创建可操作的情报，以提高公共交通服务的质量和效率。拟议研究的长期目标是为公共交通系统开发一个系统诊断框架，以监测、控制和管理服务和运营。 将利用一套严格的数据分析技术和创新方法来开发经验模型、网络模拟和优化模型。该框架以以下模型为基础：1)绩效(系统供给)；2)行为(系统需求)；3)决策支持。重要的短期目标在这项研究计划中有明确的定义。每个目标都构成了拟议框架的一个组成部分(性能、行为和决策支持)，并有助于实现长期目标： 目标1：通过对公共交通系统进行交叉比较研究，研究季节和时间对公共交通系统性能的影响。 目标2：建立乘客体验模型，为未来公交线网结构设计提供参考。 目标3：考虑运营实践和系统内乘客拥挤的影响，建立轨道交通运营的宏观模型。 这项研究建议与加拿大环境部的可持续发展战略和联邦政府关于投资公共交通的政策相一致。这项研究的结果将有助于公交机构和当局为高效的城市公共交通系统提供决策支持工具。这一研究计划可以将交通政策制定为更可持续和更环保的原则。这项研究将直接促进我们社会的公平、包容和可持续发展等社会价值观。

The adoption of emerging technologies in transportation such as autonomous vehicles (AV) and shared mobility services are poised to create a revolutionary future for urban mobility. However, this trend may create existential threat to public transportation systems and to social values, such as equity, accessibility, affordability, and environmental sustainability, that public transit services protect in our society. Despite this threat, public transportation system operations and planning are being transformed from a once data-starved arena into one of the most data-rich areas in transportation planning and management. With the emergence and availability of mobility sensing data and automated data sources in public transportation systems, there is an opportunity to perform better system diagnoses, enhance operational solutions, and create actionable intelligence to improve the quality and efficiency of public transport services. The long--term goal of the proposed research is to develop a framework for system diagnostics for public transit systems to monitor, control, and manage services and operations. A set of rigorous data analysis techniques and innovative methods will be utilized to develop empirical models, network simulations, and optimization models. The proposed framework has the following models as building blocks: 1) Performance (system supply); 2) Behaviour (system demand); and 3) Decision Support. Important short--term objectives are defined in this research proposal. Each objective contributes to one building block of the proposed framework (performance, behaviour, and decision support) and toward the long--term objective: Objective 1: Study seasonal and temporal effects on public transit system performance by conducting a cross-comparison study among public transit systems. Objective 2: Modeling of passenger experience for the future design of public transportation network structure in an integrated public transportation and (autonomous) shared mobility system. Objective 3: Macroscopic modeling of rail transit operations considering the effect of operational practices and passenger crowding in the system. This research proposal is aligned with Environment Canada's sustainable development strategies and the federal government policy on investing in public transit. The output of this research will be useful for transit agencies and authorities in providing decision support tools for efficient urban public transport systems. This research program can shape policies on transportation toward more sustainable and environment friendly principles. The research will directly contribute to social values such as equity, inclusion, and sustainability in our society.