EAGER: Inferring Comprehensive Individual Traveler Information in Multi-Modal Travel Environment Using Automatic Fare Collection Data

EAGER：使用自动收费数据推断多模式出行环境中的综合个人旅行者信息

• 批准号: 1636602
• 负责人: Jee Eun Kang
• 金额: $ 15万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1636602&HistoricalAwards=false
• 关键词: EAGER; Inferring; Comprehensive; Individual; Traveler

This Smart and Connected Communities (S&CC) EArly-concept Grant for Exploratory Research (EAGER) project explores quantitative methods to infer the traits of individual traveler behavior (Origin-Destination trip information and traveler routes/mode choice preferences) from transaction-type transportation data in a multi-modal transit system. The inferential power of this project's approach relies on how the travelers revise their routing choices (recorded by transactions) in response to perturbations in the travel environment and its changing conditions. The inferred knowledge of travelers' origin-destination and preferences can be used to plan, monitor and predict the response of the travelers to operational decisions of public transit system managers and policy-makers, thereby increasing the system flexibility and operational efficiency. Such individual knowledge will be particularly useful for the planning and operations of alternative services in the events of transit system failures/closures, and also, further develop recently emerging customized transportation, including shared mobility systems and ride sharing systems, inform dynamic parking pricing, etc. As a creative educational activity effort, an on-campus test-bed will be created based on student data (student/employee card that is used across campus for various activities including bus, facilities access, dining, shopping, etc.), for various transportation informatics investigations.This research reaches into the yet untapped potential of Automatic Fare Collection data, more broadly transaction-type data, to inform Smart City and Transportation Informatics research. The project's methodological advances are general, i.e., not limited to any particular application. This research goes beyond the standard use of Automatic Fare Collection data use for identifying and understanding the statistical properties/trends, to distilling the hidden patterns of traveler agenda and behavior in multi-modal travel environment. Methodologically, the inference framework advances the Expectation Maximization (EM) paradigm that found much success in many inference tasks. The PIs suggest using Iterative EM and Selective Set EM methods, which promise to reliably infer two unknowns for each individual traveler: routing preference and Origin-Destination. If successful, the framework can start a new branch of methodological data-heavy research with repeated data, allowing for the studies of individual travel behavior at a finer granularity and presenting new opportunities to transportation policy makers.

这个智能和互联社区（S&amp；CC）探索性研究早期概念资助（EAGER）项目探索了从多式联运系统中的交易型交通数据推断个体旅行者行为特征（出发地-目的地旅行信息和旅行者路线/模式选择偏好）的定量方法。这个项目方法的推理能力依赖于旅行者如何修改他们的路线选择（通过交易记录）来响应旅行环境及其变化条件的扰动。通过对出行者始发目的地和偏好的推断，可以规划、监测和预测出行者对公共交通系统管理者和决策者的运营决策的反应，从而提高系统的灵活性和运行效率。在交通系统故障/关闭的情况下，这些个人知识将对替代服务的规划和运营特别有用，此外，还可以进一步开发最近出现的定制交通，包括共享移动系统和乘车共享系统，为动态停车定价提供信息等。作为一项创造性的教育活动，将根据学生数据（学生/员工卡，用于在校园内的各种活动，包括公共汽车、设施使用、餐饮、购物等）创建一个校园试验台，用于各种交通信息调查。这项研究深入挖掘了自动收费数据尚未开发的潜力，更广泛的交易类型数据，为智慧城市和交通信息学研究提供信息。该项目的方法进步是普遍的，即不局限于任何特定的应用。这项研究超越了自动收费数据的标准使用，用于识别和理解统计属性/趋势，提炼出多式联运环境中旅行者议程和行为的隐藏模式。在方法上，推理框架推进了期望最大化（EM）范式，该范式在许多推理任务中取得了很大成功。pi建议使用迭代EM和选择性集合EM方法，它们承诺可靠地推断每个旅行者的两个未知数：路由偏好和出发地-目的地。如果成功，该框架可以开启一个新的方法分支，即使用重复数据进行大量数据的研究，允许在更细的粒度上研究个人旅行行为，并为交通政策制定者提供新的机会。