Artificial Intelligence for Understanding and Managing Transportation Behavior in Sustainable Smart Cities

2026-07-20

AI Summary

This work addresses the challenge of effectively transforming heterogeneous urban transportation data into actionable management intelligence, hindered by the absence of a reliable pathway from behavioral evidence to decision support. To bridge this gap, the authors propose a behavior-centered closed-loop framework that integrates travel records and passenger-generated text as behavioral evidence. Leveraging AI-driven inference, behavioral modeling, anomaly detection, and risk-aware mining techniques, the framework establishes a unified pipeline from raw data input through to decision support and governance feedback. Designed with deployment prerequisites such as privacy preservation, fairness, interpretability, and human accountability, the approach has been successfully applied to tasks including bus arrival prediction, taxi demand forecasting, anomaly identification, and risk perception, significantly enhancing service reliability, planning accuracy, regulatory effectiveness, and passenger experience.

Abstract

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.

Problem

• transportation behavior
• artificial intelligence
• smart cities
• behavioral evidence
• decision support

Innovation

• behavior-centered AI
• transportation behavior
• closed-loop framework
• actionable intelligence
• responsible AI