Stanford Database Library of American Journal of Applied Science and Technology

Vol. 5 No. 04 (2025)
Articles

Artificial Intelligence and Sustainable Urban Transportation: Integrating Accessibility, Efficiency, and Decision-Oriented Planning

  • Alexandra J. Whitman
Alexandra J. Whitman
Department of Civil and Transportation Engineering, Northeastern Metropolitan University, Boston, MA, USA

Published 2025-04-30

Keywords

  • Artificial Intelligence,
  • Urban Transportation,
  • Accessibility,
  • Predictive Modeling

How to Cite

Alexandra J. Whitman. (2025). Artificial Intelligence and Sustainable Urban Transportation: Integrating Accessibility, Efficiency, and Decision-Oriented Planning. Stanford Database Library of American Journal of Applied Science and Technology, 5(04), 102–105. Retrieved from https://oscarpubhouse.com/index.php/sdlajast/article/view/22

Copyright (c) 2025 Alexandra J. Whitman

Creative Commons License

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

Abstract

The rapid evolution of urban transportation systems presents both unprecedented opportunities and complex challenges, particularly in the domains of efficiency, accessibility, and sustainability. This research article examines the integration of artificial intelligence (AI) applications within urban transportation planning, emphasizing their potential to optimize traffic management, enhance predictive modeling, and facilitate evidence-based decision-making. Drawing upon a wide spectrum of literature encompassing traditional transportation performance metrics (Ewing, 1995; Schrank & Lomax, 2007), accessibility frameworks (Hansen, 1959; Wachs & Kumagai, 1973), and AI-enabled solutions (Abduljabbar et al., 2019; Vasudevan et al., 2020), this study elucidates how AI can address persistent inefficiencies and equity gaps. Key methodologies include descriptive system modeling, machine learning applications for traffic and travel time prediction, and evaluation of equity-driven infrastructure investment strategies (Parate et al., 2025). Results indicate that AI can substantially improve route optimization, predictive accuracy, and resource allocation while highlighting significant challenges related to public perception, data privacy, and systemic robustness (Gross, 2022; Qian et al., 2024). The discussion emphasizes theoretical implications for urban accessibility, decision-oriented planning, and regulatory adaptation, highlighting pathways for future research in AI-driven sustainable transportation. The article concludes with recommendations for integrating AI frameworks into urban mobility planning to enhance both operational efficiency and social equity.

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