Performance Analysis with Traffic Accident for Cooperative Active Safety Driving in VANET/ITS

  • Authors:
  • Ben-Jye Chang;Ying-Hsin Liang;Houng-Jer Yang

  • Affiliations:
  • Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan, ROC;Department of Multimedia Animation and Application, Nan Kai University of Technology, Nantou, Taiwan, ROC;Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung, Taiwan, ROC

  • Venue:
  • Wireless Personal Communications: An International Journal
  • Year:
  • 2014

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Abstract

Recently, by using vehicle-to-vehicle and vehicle-to-infrastructure communications for VANET/ITS, the cooperative active safety driving (ASD) providing vehicular traffic information sharing among vehicles significantly prevents accidents. Clearly, the performance analysis of ASD becomes difficult because of high vehicle mobility, diverse road topologies, and high wireless interference. An inaccurate analysis of packet connectivity probability significantly affects and degrades the VANET/ITS performance. Especially, most of related studies seldom concern the impact factors of vehicular accidents for the performance analyses of VANET/ITS. Thus, this paper proposes a two-phase approach to model a distributed VANET/ITS network with considering accidents happening on roads and to analyze the connectivity probability. Phase 1 proposes a reliable packet routing and then analyzes an analytical model of packet connectivity. Moreover, the analysis is extended to the cases with and without exhibiting transportation accidents. In phase 2, by applying the analysis results of phase 1 to phase 2, an adaptive vehicle routing, namely adaptive vehicle routing (AVR), is proposed for accomplishing dynamic vehicular navigation, in which the cost of a road link is defined in terms of several critical factors: traffic density, vehicle velocity, road class, etc. Finally, the path with the least path cost is selected as the optimal vehicle routing path. Numerical results demonstrate that the analytical packet connectivity probability and packet delay are close to that of simulations. The yielded supreme features justify the analytical model. In evaluations, the proposed approach outperforms the compared approaches in packet connectivity probability, average travel time, average exhausted gasoline. However, the proposed approach may lead to a longer travel distance because it enables the navigated vehicle to avoid traversing via the roads with a higher traffic density.