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

使用移动传感数据进行城市交通系统诊断、监控和管理

• 批准号: RGPIN-2020-03944
• 负责人: Saidi, Saeid
• 金额: $ 1.89万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716733
• 关键词: Urban; Transit; System; Diagnosis; Monitoring

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.

自动驾驶汽车（AV）和共享出行服务等新兴交通技术的采用，将为城市出行创造革命性的未来。然而，这种趋势可能会对公共交通系统和社会价值造成生存威胁，比如公共交通服务在我们的社会中所保护的公平、可达性、可负担性和环境可持续性。尽管存在这种威胁，但公共交通系统的运营和规划正在从一个曾经缺乏数据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传感数据和自动化数据源的出现和可用性，有机会进行更好的系统诊断，增强操作解决方案，并创建可操作的智能，以提高公共交通服务的质量和效率。这项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。