IDILIM: incident detection included linear management using connected autonomous vehicles

Ilgin Gokasar (), Alperen Timurogullari (), Sarp Semih Ozkan () and Muhammet Deveci ()
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Ilgin Gokasar: Bogazici University
Alperen Timurogullari: Bogazici University
Sarp Semih Ozkan: Bogazici University
Muhammet Deveci: National Defence University

Annals of Operations Research, 2024, vol. 339, issue 1, No 33, 889-908

Abstract: Abstract Autonomous vehicle advancements and communication technologies such as V2V, V2I, and V2X have enabled the development of connected and autonomous vehicles. Because CAVs are directly effective in traffic, their application in traffic management and incident management appears promising. They can immediately begin regulating traffic and acting as sensors due to their connectivity to the infrastructure. This research proposes Incident Detection Included Linear Management (IDILIM), a CAV-based incident management algorithm that regulates CAV and traffic speeds based on dynamic and predicted shockwave speeds. The SUMO simulations are carried out on a 10.4-km-long, three-lane facility with 21 sensors every 500 m. In the scenarios, three traffic demands, eleven CAV penetration rates, and varying incident locations, duration, and lanes are used. A total of 20 simulation seeds are used in each scenario. The proposed algorithm necessitates the use of a reliable traffic prediction model. Convolutional Neural Networks, a deep learning algorithm with high estimation accuracy, are used in the prediction model. IDILIM uses the highly accurate traffic prediction output of the Pix-to-Pix model as input at 3-min intervals. Shockwave speed is calculated using model outputs and fed to CAVs. To compare with IDILIM, variable speed limits (VSL) are also modeled. When compared to uncontrolled base scenarios, IDILIM reduced density values greater than 35 veh/km in the critical region by 89.32%. In the same scenario, VSL management decreased by only 52.43%.

Keywords: Incident detection; Real-time traffic management; Connected autonomous vehicles; Deep learning; Convolutional Neural Networks; Conditional Generative Adversarial Networks (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-023-05280-y

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