Just Accepted Manuscripts
Thematic Call for papers: URBAN ARTIFICIAL INTELLIGENCE

Urban Artificial Intelligence in Mobility Infrastructure: Lessons for Just and Inclusive Cities

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  • Asma Mehan
Asma Mehan
Texas Tech University, USA
DOI: https://doi.org/10.36253/contest-16690

Published 2026-03-02

Keywords

  • urban artificial intelligence,
  • emergency response,
  • infrastructure monitoring,
  • equity,
  • sustainability

How to Cite

Mehan, A. (2026). Urban Artificial Intelligence in Mobility Infrastructure: Lessons for Just and Inclusive Cities. Contesti. Città, Territori, Progetti. https://doi.org/10.36253/contest-16690

Copyright (c) 2026 Asma Mehan

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

Artificial Intelligence is increasingly embedded in urban mobility and emergency response systems, where real-time decision-making, infrastructure coordination, and public safety converge. This article examines Urban Artificial Intelligence (Urban AI) through the domain of traffic management and emergency mobility, using these systems as a strategic entry point for analyzing broader questions of governance, equity, and resilience in AI-enabled cities. The paper develops a theoretical framework that distinguishes among cognitive, data-driven, and hybrid Urban AI models, highlighting how each approach shapes urban knowledge production, operational performance, and accountability.

This framework is grounded through three U.S.-based case studies: AI-enabled emergency vehicle preemption in Fremont, California; AI-assisted subway infrastructure monitoring in New York City; and AI-driven signal coordination for emergency routing in Seattle. Together, these cases illustrate how Urban AI systems are deployed in real-world contexts to enhance efficiency, safety, and resilience.

The analysis demonstrates that while Urban AI can significantly improve urban operations, its long-term legitimacy depends on integrating principles of equity, transparency, environmental responsibility, and participatory governance. The article concludes by arguing for integrated Urban AI models that balance technical effectiveness with democratic oversight, positioning Urban AI as a critical component of just, resilient, and inclusive urban futures.

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