发表机构
Beijing University of Technology（北京工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究如何利用人工智能理解和管理可持续智慧城市中的交通行为，通过以行为为中心的视角，研究公交到站预测等四个方向，经闭环框架整合，确定部署条件，建立从行为证据到各类交通决策的统一路径。
AI 中文摘要
城市交通系统产生异构数据，但这些数据不会自动成为可操作的管理智能。本章采用以行为为中心的人工智能视角，将出行记录和乘客生成的文本视为行为证据而非行为真相。研究了四个方向：公交到站预测、出租车出行模式发现、异常行为检测和乘客感知风险挖掘。通过闭环框架整合这些方向，确定了部署的必要条件，建立了从行为证据到运营、规划、监管和乘客服务决策的统一路径。
英文摘要
Urban transportation systems generate heterogeneous data, yet these data do not automatically become actionable management intelligence. This chapter adopts a behavior-centered perspective on artificial intelligence (AI), treating mobility records and passenger-generated text as behavioral evidence rather than behavioral truth. It examines four directions: bus arrival prediction for service reliability, taxi mobility pattern discovery for demand analysis and planning, abnormal behavior detection for accountable regulatory support, and passenger-perceived risk mining for service improvement. These directions are integrated through a closed-loop framework linking data input, behavior representation, AI inference, decision support, public value, and governance feedback. The chapter identifies data quality, privacy, fairness, interpretability, uncertainty, transferability, and human accountability as essential conditions for deployment. It thereby establishes a unified pathway from behavioral evidence to operational, planning, regulatory, and passenger-service decisions.
