Leveraging AI in Traffic Engineering to Enhance Bicycle Mobility in Urban Areas

Chang Che; Junchi Tian

doi:10.70393/6a69656173.323039

OPEN ACCESS |Research Article ||1 December 2024

Leveraging AI in Traffic Engineering to Enhance Bicycle Mobility in Urban Areas

Chang Che

The George Washington University

cche57@gwmail.gwu.edu

The George Washington University, US.

Junchi Tian

The George Washington University

junchi0905@gmail.com

The George Washington University, US.

* Corresponding Author¹: Chang Che, E-Mail: cche57@gwmail.gwu.edu

Publication

Accepted Unknow ; Published 2024 December 1

Journal of Industrial Engineering and Applied Science, 2024, 2(6), 3005-6071.

Abstract

More With the rise of AI-driven technologies, urban cycling is becoming more accessible and appealing to many due to benefits such as health improvement and cost efficiency. Governments worldwide are promoting cycling as a sustainable transportation option to address environmental challenges. Ensuring seamless bicycle mobility in cities is essential to incentivize cycling. AI-powered Traffic Engineering can significantly enhance the flow of bicycle traffic in urban areas by optimizing infrastructure and safety. This article explores the benefits of cycling and the rationale for investing in AI-integrated cycling infrastructure. It provides examples of smart solutions such as AI-based vehicle-cycle segregation (including London's Cycle Superhighways), protected intersections enhanced by machine learning algorithms, and Intelligent Transport Systems (ITS) that incorporate AI for dynamic traffic management. Their implementation and impact on cyclists and overall traffic flow are analyzed, demonstrating that these advanced systems reduce accidents, boost road efficiency, and make cycling more enjoyable. Quantitative data on these improvements is also presented. In conclusion, AI-enabled Traffic Engineering solutions play a vital role in enhancing bicycle mobility and safety in urban environments.

Keywords

AI-Driven Traffic Engineering , Urban Bicycle Mobility , Intelligent Transport Systems .

Metadata

DOI:

10.70393/6a69656173.323039

ARK:

ark:/40704/JIEAS.v2n6a02

Pages: 10-15

References: 12

Disciplines: Artificial Intelligence

Subjects: Traffic Engineering

Cite This Article

APA Style

Che, C. & Tian, J. (2024). Leveraging ai in traffic engineering to enhance bicycle mobility in urban areas. Journal of Industrial Engineering and Applied Science, 2(6), 10-15. https://doi.org/10.70393/6a69656173.323039

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

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Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
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