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

这个智能互联社区（S CC）早期概念探索性研究资助（EAGER）项目探索定量方法，从多模式交通系统中的交易型交通数据中推断个人旅行者行为的特征（起点-目的地旅行信息和旅行者路线/模式选择偏好）。这个项目的方法的推理能力依赖于旅行者如何修改他们的路线选择（由交易记录），以响应旅行环境的扰动及其不断变化的条件。通过对出行者的出发地-目的地和出行偏好的推断，可以规划、监控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响应，从而提高系统的灵活性和运营效率。这种个人知识对于在交通系统故障/关闭的情况下规划和运营替代服务特别有用，并且还进一步发展最近出现的定制交通，包括共享移动系统和乘车共享系统，告知动态停车定价等。将根据学生数据（学生/员工卡，用于校园内的各种活动，包括巴士，设施访问，餐饮，购物等）创建校内测试台，这项研究深入到自动收费数据尚未开发的潜力，更广泛的交易型数据，为智慧城市和交通信息学研究提供信息。该项目在方法上的进步是普遍的，即，不限于任何特定的应用。本研究超越了自动票价收集数据用于识别和理解统计属性/趋势的标准使用，以提取多模式旅行环境中旅行者议程和行为的隐藏模式。在方法论上，推理框架推进了期望最大化（EM）范式，在许多推理任务中取得了很大的成功。PI建议使用迭代EM和选择集EM方法，这些方法可以可靠地推断每个旅行者的两个未知数：路线偏好和起点-目的地。如果成功的话，该框架可以开始一个新的分支的方法学数据重研究重复的数据，允许在一个更细的粒度的个人出行行为的研究，并提出新的机会，交通政策制定者。

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.