• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

A New Traffic Congestion Detection and Quantification Method Based on Comprehensive Fuzzy Assessment in VANET


Abstract

Recently, road traffic congestion is becoming a serious urban phenomenon, leading to massive adverse impacts on the ecology and economy. Therefore, solving this problem has drawn public attention throughout the world. One new promising solution is to take full advantage of vehicular ad hoc networks (VANETs). In this study, we propose a new traffic congestion detection and quantification method based on vehicle clustering and fuzzy assessment in VANET environment. To enhance real-time performance, this method collects traffic information by vehicle clustering. The average speed, road density, and average stop delay are selected as the characteristic parameters for traffic state identification. We use a comprehensive fuzzy assessment based on the three indicators to determine the road congestion condition. Simulation results show that the proposed method can precisely reflect the road condition and is more accurate and stable compared to existing algorithms.


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Cite this article

[IEEE Style]
Lanlan Rui, Yao Zhang1, Haoqiu Huang and Xuesong Qiu, "A New Traffic Congestion Detection and Quantification Method Based on Comprehensive Fuzzy Assessment in VANET," KSII Transactions on Internet and Information Systems, vol. 12, no. 1, pp. 41-60, 2018. DOI: 10.3837/tiis.2018.01.003

[ACM Style]
Rui, L., Zhang1, Y., Huang, H., and Qiu, X. 2018. A New Traffic Congestion Detection and Quantification Method Based on Comprehensive Fuzzy Assessment in VANET. KSII Transactions on Internet and Information Systems, 12, 1, (2018), 41-60. DOI: 10.3837/tiis.2018.01.003