用AI分析交通行为数据，提升城市出行管理效率与服务
Artificial Intelligence for Understanding and Managing Transportation Behavior in Sustainable Smart Cities
- 将出行记录和乘客文本当作行为证据，而非真实行为
- 实现公交到站预测、打车模式发现、异常行为检测等应用
- 构建闭环框架，适合智慧城市管理者与政策制定者参考
城市交通系统产生异构数据，但这些数据不会自动转化为可操作的管理智能。本文从行为中心视角出发，将出行记录和乘客生成文本视为行为证据而非真实行为。研究涵盖四个方向：公交到站预测以提升服务可靠性，出租车出行模式挖掘用于需求分析与规划，异常行为检测支持可问责监管，乘客感知风险挖掘用于服务优化。上述方向通过一个闭环框架整合，包含数据输入、行为表征、AI推断、决策支持、公共价值和治理反馈。论文指出数据质量、隐私保护、公平性、可解释性、不确定性、可迁移性和人类问责是部署关键条件，从而建立从行为证据到运营、规划、监管和乘客服务决策的统一路径。
原文摘要 · Abstract (English)
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.
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