Leveraging AI in Traffic Engineering to Enhance Bicycle Mobility in Urban Areas
DOI:
https://doi.org/10.70393/6a69656173.323039ARK:
https://n2t.net/ark:/40704/JIEAS.v2n6a02Disciplines:
Artificial IntelligenceSubjects:
Traffic EngineeringReferences:
12Keywords:
AI-Driven Traffic Engineering, Urban Bicycle Mobility, Intelligent Transport SystemsAbstract
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.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
