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交通技术的采用，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社会价值观（如公平，可达性，可负担性和环境可持续性）造成生存威胁，公共交通服务在我们的社会中保护这些价值观。尽管存在这种威胁，公共交通系统的运营和规划正在从一个曾经数据匮乏的竞技场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传感数据和自动化数据源的出现和可用性，有机会执行更好的系统诊断，增强运营解决方案，并创建可操作的智能，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。一套严格的数据分析技术和创新的方法将被用来开发经验模型，网络模拟和优化模型。拟议的框架有以下模型作为构建模块：1）性能（系统供应）; 2）行为（系统需求）;和3）决策支持。重要的短期目标在本研究建议中定义。每个目标有助于一个构建块的建议框架（性能，行为和决策支持），并朝着长期目标：目标1：研究季节和时间对公共交通系统性能进行交叉比较研究公共交通系统。目标二：综合公共交通和（自主）共享移动系统中未来公共交通网络结构设计的乘客体验建模。 目标3：轨道交通运营的宏观建模，考虑到系统中的运营实践和乘客拥挤的影响。该研究提案符合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。这项研究的结果将有助于运输机构和当局提供决策支持工具，有效的城市公共交通系统。这项研究计划可以塑造交通政策，使其朝着更可持续和环境友好的原则发展。该研究将直接有助于社会价值观，如公平，包容和可持续发展在我们的社会。

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.