ew 23(1):

Research Article

The Use of Artificial Intelligence to Optimize the Routing of Vehicles and Reduce Traffic Congestion in Urban Areas

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  • @ARTICLE{10.4108/ew.4613,
        author={Srishti Dikshit and Areeba Atiq and Mohammad Shahid and Vinay Dwivedi and Aarushi Thusu},
        title={The Use of Artificial Intelligence to Optimize the Routing of Vehicles and Reduce Traffic Congestion in Urban Areas},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={EW},
        year={2023},
        month={12},
        keywords={Artificial Intelligence, Vehicle Routing, Traffic Congestion, Urban Mobility, Sustainability, Machine Learning, Optimization, Transportation Efficiency},
        doi={10.4108/ew.4613}
    }
    
  • Srishti Dikshit
    Areeba Atiq
    Mohammad Shahid
    Vinay Dwivedi
    Aarushi Thusu
    Year: 2023
    The Use of Artificial Intelligence to Optimize the Routing of Vehicles and Reduce Traffic Congestion in Urban Areas
    EW
    EAI
    DOI: 10.4108/ew.4613
Srishti Dikshit1,*, Areeba Atiq1, Mohammad Shahid1, Vinay Dwivedi2, Aarushi Thusu1
  • 1: Noida Institute of Engineering and Technology
  • 2: Galgotias University
*Contact email: srishti.dikshit@niet.co.in

Abstract

The swift urbanization of cities has given rise to an unparalleled surge in vehicular traffic, leading to substantial congestion, heightened pollution, and a diminished quality of life. This investigation explores the capacity of artificial intelligence (AI) to transform urban mobility by optimizing vehicle routing and alleviating traffic congestion. The objective is to create AI-powered solutions that augment transportation efficiency, diminish travel times, and mitigate environmental repercussions. This paper thoroughly scrutinizes existing AI algorithms, vehicle routing, and traffic management techniques. The study integrates real-time traffic data, road network characteristics, and individual travel patterns to formulate intelligent routing strategies. The proposed AI system adjusts to dynamic traffic conditions through machine learning and optimization algorithms, pinpointing optimal routes and redistributing traffic flows to minimize congestion hotspots. To assess the effectiveness of the AI-driven approach, extensive simulations and case studies are conducted in representative urban areas. Performance metrics, including travel time reduction, fuel consumption, and emissions reduction, are employed to quantify the impact of the proposed system on traffic congestion and environmental sustainability. Furthermore, the study evaluates the scalability, feasibility, and economic viability of implementing AI-based traffic management solutions on a larger scale. The outcomes of this research provide valuable insights into the potential advantages of AI in reshaping urban mobility. By optimizing vehicle routing and diminishing traffic congestion, the proposed AI-driven system has the potential to elevate overall transportation efficiency, reduce energy consumption, and contribute to a healthier urban environment. The findings carry substantial implications for policymakers, urban planners, and transportation authorities seeking innovative solutions to tackle the challenges of contemporary urbanization while promoting sustainable development.