The research of traffic density extraction method under vehicular ad hoc network environment. Issue 1 (29th August 2019)

• Record Type: Journal Article
• Authors: Wu, Zhizhou; Zhang, Yiming; Tan, Guishan; Hu, Jia
• Is Part Of: Journal of intelligent and connected vehicles. Volume 2:Issue 1(2019)
• Page Start: 25
• Page End: 32
• Publication Date: 2019-08-29
• Subjects: Traffic density -- VANET simulation -- Vehicular ad hoc network; Intelligent transportation systems -- Periodicals; Automobiles -- Safety measures -- Periodicals; Autonomous vehicles -- Periodicals; Motor vehicles -- Technological innovations -- Periodicals; 629.2042
• DOI: 10.1108/JICV-01-2019-0003
• Languages: English
• ISSNs: 2399-9802

Abstract

Abstract : Purpose: Traffic density is one of the most important parameters to consider in the traffic operation field. Owing to limited data sources, traditional methods cannot extract traffic density directly. In the vehicular ad hoc network (VANET) environment, the vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) interaction technologies create better conditions for collecting the whole time-space and refined traffic data, which provides a new approach to solving this problem. Design/methodology/approach: On that basis, a real-time traffic density extraction method has been proposed, including lane density, segment density and network density. Meanwhile, using SUMO and OMNet++ as traffic simulator and network simulator, respectively, the Veins framework as middleware and the two-way coupling VANET simulation platform was constructed. Findings: Based on the simulation platform, a simulated intersection in Shanghai was developed to investigate the adaptability of the model. Originality/value: Most research studies use separate simulation methods, importing trace data obtained by using from the simulation software to the communication simulation software. In this paper, the tight coupling simulation method is applied. Using real-time data and history data, the research focuses on the establishment and validation of the traffic density extraction model.