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

arXiv: https://arxiv.org/abs/2607.17694 · HTML: https://mlsanity.com/paper/2607.17694/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 20 Jul 2026.

## Builds on (most cited first, 8 of 8)

- [Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey](https://mlsanity.com/paper/2303.14483/) (2023)
- [Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap](https://mlsanity.com/paper/2503.21411/) (2025)
- [Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Era](https://mlsanity.com/paper/2403.14151/) (2024)
- [Towards Explainable Traffic Flow Prediction with Large Language Models](https://mlsanity.com/paper/2404.02937/) (2024)
- [Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems](https://mlsanity.com/paper/2309.11039/) (2023)
- [Finding A Taxi with Illegal Driver Substitution Activity via Behavior Modelings](https://mlsanity.com/paper/2404.11844/) (2024)
- [Importance-aware Topic Modeling for Discovering Public Transit Risk from Noisy Social Media](https://mlsanity.com/paper/2512.06293/) (2025)
- [Few Shot Semi-Supervised Learning for Abnormal Stop Detection from Sparse GPS Trajectories](https://mlsanity.com/paper/2510.12686/) (2025)

## Abstract (arXiv)

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.