SWSCAV: Real-time traffic management using connected autonomous vehicles

Ilgin Gokasar; Alperen Timurogullari; Muhammet Deveci; Harish Garg

doi:10.1016/j.isatra.2022.06.025

SWSCAV: Real-time traffic management using connected autonomous vehicles

ISA Trans. 2023 Jan:132:24-38. doi: 10.1016/j.isatra.2022.06.025. Epub 2022 Jun 21.

Authors

Ilgin Gokasar¹, Alperen Timurogullari², Muhammet Deveci³, Harish Garg⁴

Affiliations

¹ Department of Civil Engineering, Bogazici University, 34342 Bebek, Istanbul, Turkey. Electronic address: ilgin.gokasar@boun.edu.tr.
² Department of Civil Engineering, Bogazici University, 34342 Bebek, Istanbul, Turkey. Electronic address: timurogullarialperen@gmail.com.
³ Royal School of Mines, Imperial College London, London SW7 2AZ, UK; Department of Industrial Engineering, Turkish Naval Academy, National Defence University, 34940 Tuzla, Istanbul, Turkey. Electronic address: muhammetdeveci@gmail.com.
⁴ School of Mathematics, Thapar Institute of Engineering & Technology, Deemed University, Patiala 147004, Punjab, India. Electronic address: harish.garg@thapar.edu.

PMID: 35791970
DOI: 10.1016/j.isatra.2022.06.025

Abstract

Traffic management methods aim to increase the infrastructure's capacity to lower congestion levels. Using vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything (V2X) connectivity technologies, connected autonomous vehicles (CAVs) have the potential to operate as actuators for traffic control. In this study, a CAV-based alternative approach for traffic management is proposed (SWSCAV), and its performance is compared to that of lane control signals (LCS) and variable speed limits (VSL), which are also traffic management systems. When a shockwave is detected due to an incident, the CAVs on the road slow until they reach the speed of the observed shockwave (SWS), according to this proposed procedure. Thus, the incoming traffic flow towards the incident is slowed, preventing the queue behind from extending. In a simulation of the urban mobility (SUMO) environment, the suggested method is evaluated for 4800 scenarios on a three-lane highway by varying the market penetration rate of CAVs in traffic flow, the control distances, the incident lane, and the duration. The proposed method reduces the incidence of density values of over 38 veh/km/lane and 28 veh/km/lane in the vicinity of the incident region by 12.68 and 8.15 percent, respectively. Even at low CAV market penetration rates, the suggested method reduces traffic density throughout the network and in the location of the incident site by twice as much as the LCS system application.

Keywords: Connected autonomous vehicles; LCS; Shockwave speed; Traffic management; VSL.