Optimizing Urban Mobility in Hangzhou: A Case Study of the City Brain’s AI-Driven Traffic Management

Abstract

This study examines Hangzhou’s City Brain as an AI-enabled traffic governance platform. Using Leong & Kumar (2023) four-dimensional ITS framework—data acquisition, connectivity, intelligence, and responsiveness, the paper evaluates operational outcomes, governance conditions, and transferability. We find that (i) average traffic efficiency improved in pilot corridors and (ii) emergency response times shortened markedly, with (iii) gains shaped by a public–private partnership that couples municipal mandates with cloud-scale analytics. However, challenges persist around data governance and public trust, interoperability with legacy ITS, and context-dependent scalability. Comparative references to Singapore and Amsterdam underscore how institutional design conditions technological payoffs. The case contributes practice-oriented insights for cities seeking reproducible, ethically governed AI in transport.