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A network level connectivity robustness measure for connected vehicle environments
Institution:1. Department of Computer Science, East China University of Political Science and Law, Shanghai 201620, China;2. Department of Computer Science, University College London (UCL), London WC1E 6BT, UK;1. Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-infrastructure Systems, Changsha University of Science & Technology, Changsha 410114, Hunan, China;2. University of Thessaloniki, Greece;3. Energy Steering Panel, European Academies Science Advisory Council (EASAC), United Kingdom;4. Department of Civil Infrastructure and Environmental Engineering, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates
Abstract:This study introduces a new CONnectivity ROBustness model (CONROB) to assess vehicle-to-vehicle communication in connected vehicle (CV) environments. CONROB is based on Newton’s universal law of gravitation and accounts for multiple factors affecting the connectivity in CV environments such as market penetration, wireless transmission range, spatial distribution of vehicles relative to each other, the spatial propagation of the wireless signal, and traffic density. The proposed methodology for the connectivity robustness calculation in CONROB accounts for the Link Expiration Time (LET) and the Route Expiration Time (RET) that are reflected in the stability of links between each two adjacent vehicles and the expiration time of communication routes between vehicles. Using a 117 sq-km (45-square mile) network in Washington County, located west of Portland city, Oregon, a microscopic simulation model (VISSIM) was built to verify CONROB model. A total of 45 scenarios were simulated for different traffic densities generated from five different traffic demand levels, three levels of market penetration (5%, 15%, and 25%), and three transmission range values 76 (250), 152 (500), and 305 (1000) m (ft)]. The simulation results show that the overall robustness increases as the market penetration increases, given the same transmission range, and relative traffic density. Similarly, the overall connectivity robustness increases as the relative traffic density increases for the same market penetration. More so, the connectivity robustness becomes more sensitive to the relative traffic density at higher values of transmission range and market penetration. Multiple regression analysis was conducted to show the significant effect of relative traffic density, transmission range, and market penetration on the robustness measure. The results of the study provide an evidence of the ability of the model to capture the effect of the different factors on the connectivity between vehicles, which provides a viable tool for assessing CV environments.
Keywords:Intelligent transportation systems (ITS)  Vehicle-to-Vehicle (V2V)  Vehicle to Infrastructure (V2I)  Connected vehicle (CV)  Communication stability  CONnectivity ROBustness (CONROB)
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