COALITION: CAVs-enabled Probabilistic Offloading of Congested Lanes for Reduced Urban Traffic Congestion

The number of vehicles in developed countries has grown more rapidly than available road capacity, resulting in increased congestion, air pollution, and more accidents. A recent UN report predicts that the increasing size of cities and levels of population mobility will mean 2.9 billion vehicles on...

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Vydané v:IEEE Vehicular Technology Conference s. 1 - 7
Hlavní autori: Djahel, Soufiene, Hadjadj-Aoul, Yassine, Pincemin, Renan, Wu, Celimuge
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.01.2023
Predmet:
Cognitive Radio
Probabilistic logic
Road Traffic Congestion
Roads
Simulation
Smart Cities
Smart Transportation
Sociology
Transportation
Urban areas
Vehicular and wireless technologies
ISSN:2577-2465
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Popis
Shrnutí:The number of vehicles in developed countries has grown more rapidly than available road capacity, resulting in increased congestion, air pollution, and more accidents. A recent UN report predicts that the increasing size of cities and levels of population mobility will mean 2.9 billion vehicles on the road in cities alone by 2050. To mitigate the consequences of this increase without dramatically increasing the number of built roads, novel methods to better utilise existing road capacity are required. To that end, this paper introduces COALITION, a cognitive radio-enabled probabilistic offloading of congested lanes, as an innovative solution to efficiently handle traffic congestion in urban areas. This solution builds upon and improves the performance of our previous work, named CRITIC, and makes use of Electric Connected and Autonomous Vehicles (ECAVs) features to maximize the usage of road capacity through opportunistic exploitation of under-utilized reserved lanes while fostering the use of electric vehicles to support carbon neutral transportation objectives. Simulation results have proven the effectiveness of COALITION and its potential impact in real-world scenarios.
ISSN:2577-2465
DOI:10.1109/VTC2023-Fall60731.2023.10333813