Reliability Assessment of Vehicle-to-Vehicle Communication Networks through Headway Distribution and Information Propagation Delay

Abstract

Several developed vehicle spaces and time headway distribution models in traffic flow theory have been widely used in the literature, reflecting the primary uncertainty in drivers’ car-following movements and explaining the traffic flow stochastic features. Moreover, effective vehicle-to-vehicle (V2V) communication is a key to decentralizing traffic information systems. Accurate vehicle headway distribution estimation will ensure reliable communication and benefit passengers’ safety and comfort. Consider several proposals for headway distribution in the literature; this paper studies the effect of space-headway distribution on information propagation delay in assessing the reliability of the V2V communication networks. We utilize the properties of reliability measurement for headway data by introducing the quasi-maximum likelihood estimator (QMLE) to measure the effects that different headway models have on estimating the parameters of headway distribution in a V2V communication network. The statistical analysis is then applied to the real Next-Generation Simulation (NGSIM) data. It validates the proposed methodology and formulations by measuring the effects of model selection on headway data and information propagation delay on the reliability of the V2V network. The results show that the effect of headway distribution when the vehicle’s transmission range is smaller than the road segmentation is not negligible, especially when cars are very distant. Based on our results, we recommend new metrics based on Kullback–Leibler divergence for model selection of headway data, thereby enhancing the reliability of the V2V network.