An Efficient Traffic Congestion Monitoring System on Internet of Vehicles

• Main Authors: Duc-Binh Nguyen, Chyi-Ren Dow, Shiow-Fen Hwang
• Format: Article
• Language: English
• Published: Hindawi-Wiley 2018-01-01
• Series: Wireless Communications and Mobile Computing
• Online Access: http://dx.doi.org/10.1155/2018/9136813

Existing intelligent transport systems (ITS) do not fully consider and resolve accuracy, instantaneity, and compatibility challenges while resolving traffic congestion in Internet of Vehicles (IoV) environments. This paper proposes a traffic congestion monitoring system, which includes data collection, segmented structure establishment, traffic-flow modelling, local segment traffic congestion prediction, and origin-destination traffic congestion service for drivers. Macroscopic model-based traffic-flow factors were formalized on the basis of the analysis results. Fuzzy rules-based local segment traffic congestion prediction was performed to determine the traffic congestion state. To enhance prediction efficiency, this paper presents a verification process for minimizing false predictions which is based on the Rankine-Hugoniot condition and an origin-destination traffic congestion service is also provided. To verify the feasibility of the proposed system, a prototype was implemented. The experimental results demonstrate that the proposed scheme can effectively monitor traffic congestion in terms of accuracy and system response time.