The rapid growth of autonomous vehicles (AVs) and traffic strategies promises to transform urban mobility, necessitating a comprehensive investigation of their impacts on traffic. This study assessed the effects of different traffic control strategies on a network with an autonomous vehicles shuttle and varying levels of AV penetration. The simulation of Urban Mobility (SUMO) software was utilized as an open-source and flexible traffic microsimulation, creating a realistic simulation environment. Three traffic control strategies for shuttle movements were examined in a section of Turin, Italy: mixed traffic, dynamic lane, and separated lane strategies within the network. Each strategy was evaluated across five AV penetration levels.

    The simulation results demonstrated that increasing the AV integration leads to reduced travel times and greater road efficiency. Both the separated lane and dynamic lane strategies improve travel times for the shuttle but have negative effects on other vehicles. However, the dynamic lane offers improvement for the shuttle while causing a lower negative impact on other vehicles compared to the separated lane strategy. Dynamic lanes offer a flexible and effective solution for modern traffic management, helping cities improve their mobility and reduce congestion. The findings highlight the importance of developing various control strategies and AV-compatible infrastructure to achieve traffic efficiency, reduced congestion, and an updated urban mobility framework.


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    Title :

    Evaluating Traffic Control Strategies for Autonomous Shuttle in Different AV Penetration, Using SUMO Traffic Simulation


    Additional title:

    Lect.Notes Social.Inform.



    Conference:

    International Conference on Intelligent Transport Systems ; 2024 ; Pisa, Italy December 05, 2024 - December 06, 2024



    Publication date :

    2025-04-03


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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