The Effect of Connected Vehicle Environment on Global Travel Efficiency and Its Optimal Penetration Rate

The effect of connected vehicle environment on the transportation systems and the relationship between the penetration rate of connected vehicle and its efficiency are investigated in this study. An example based on the classical two-route network is adopted in this study, in which the drivers consist of two types: informed and uninformed. The advantages and disadvantages of the connected vehicle environment are analyzed, and the concentration phenomenon is proposed and found to be mitigated when only a fraction of drivers are informed. The simulation tool embodying the characteristics of the connected vehicle environment is developed using the multiagent technology. Finally, different scenarios are simulated, such as the zero-information environment, the full-information environment, and the connected vehicle environment with various penetration rates. Moreover, simulation results of the global performance of the transportation system are compared. The results show that the connected vehicle environment can efficiently improve the performance of the transportation system, while the adverse effects due to concentration rise out from the excessive informed drivers. An optimal penetration rate of the connected vehicles is found to characterize the best performance of the system. These findings can aid in understanding the effect of the connected vehicle environment on the transportation system.

[1]  André de Palma,et al.  Risk Aversion, the Value of Information, and Traffic Equilibrium , 2012, Transp. Sci..

[2]  Kay W. Axhausen,et al.  Effects of information in road transport networks with recurrent congestion , 1995 .

[3]  Fang Zhou,et al.  Parsimonious shooting heuristic for trajectory control of connected automated traffic part I: Theoretical analysis with generalized time geography , 2015, ArXiv.

[4]  Chuan Ding,et al.  A gradient boosting logit model to investigate driver’s stop-or-run behavior at signalized intersections using high-resolution traffic data , 2016 .

[5]  Xiaopeng Li,et al.  Stop-and-go traffic analysis: Theoretical properties, environmental impacts and oscillation mitigation , 2014 .

[6]  Gloria Londono,et al.  Dissuasive Queues in the Time Dependent Traffic Assignment Problem , 2014 .

[7]  Hesham A. Rakha,et al.  Freeway Speed Harmonization , 2016, IEEE Transactions on Intelligent Vehicles.

[8]  T. VaisaghViswanathan,et al.  The effect of information uncertainty in road transportation systems , 2016, J. Comput. Sci..

[9]  Eyran J. Gisches,et al.  Pre-trip Information and Route-Choice Decisions with Stochastic Travel Conditions : Experiment , 2014 .

[10]  Xuemin Shen,et al.  Connected Vehicles: Solutions and Challenges , 2014, IEEE Internet of Things Journal.

[11]  Jia Hu,et al.  Parsimonious shooting heuristic for trajectory design of connected automated traffic part II: Computational issues and optimization , 2017 .

[12]  David M Levinson,et al.  The Value of Advanced Traveler Information Systems for Route Choice , 2003 .

[13]  Eran Ben-Elia,et al.  Which road do I take? A learning-based model of route-choice behavior with real-time information , 2010 .

[14]  M. Roorda,et al.  Impact of hourly parking pricing on travel demand , 2017 .

[15]  Amnon Rapoport,et al.  Pre-trip Information and Route-Choice Decisions with Stochastic Travel Conditions: Experiment , 2014 .

[16]  Roberta Di Pace,et al.  The impact of travel information's accuracy on route-choice , 2013 .

[17]  André de Palma,et al.  Does providing information to drivers reduce traffic congestion , 1991 .

[18]  Chao Liu,et al.  Exploring the influence of built environment on tour-based commuter mode choice: A cross-classified multilevel modeling approach , 2014 .

[19]  Yiheng Feng,et al.  Travel Time Observation in Privacy Ensured Connected Vehicle Environment Using Partial Vehicle Trajectories and Extended Tardity , 2015 .

[20]  Yunpeng Wang,et al.  Understanding commuting patterns using transit smart card data , 2017 .

[21]  Joaquín de Cea Ch.,et al.  Effect of advanced traveler information systems and road pricing in a network with non-recurrent congestion , 2009 .

[22]  Ch. Ramesh Babu,et al.  Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds , 2016 .

[23]  Andreja Habjan,et al.  Exploring Effects of Information Quality Change in Road Transport Operations , 2012, Ind. Manag. Data Syst..

[24]  Chao Liu,et al.  Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance , 2017 .

[25]  T. VaisaghViswanathan,et al.  Information impact on transportation systems , 2015, J. Comput. Sci..

[26]  Ching-Yao Chan Connected vehicles in a connected world , 2011, Proceedings of 2011 International Symposium on VLSI Technology, Systems and Applications.

[27]  Xiaolei Ma,et al.  Mining smart card data for transit riders’ travel patterns , 2013 .