Influence of connected and autonomous vehicles on traffic flow stability and throughput

The introduction of connected and autonomous vehicles will bring changes to the highway driving environment. Connected vehicle technology provides real-time information about the surrounding traffic condition and the traffic management center’s decisions. Such information is expected to improve drivers’ efficiency, response, and comfort while enhancing safety and mobility. Connected vehicle technology can also further increase efficiency and reliability of autonomous vehicles, though these vehicles could be operated solely with their on-board sensors, without communication. While several studies have examined the possible effects of connected and autonomous vehicles on the driving environment, most of the modeling approaches in the literature do not distinguish between connectivity and automation, leaving many questions unanswered regarding the implications of different contemplated deployment scenarios. There is need for a comprehensive acceleration framework that distinguishes between these two technologies while modeling the new connected environment. This study presents a framework that utilizes different models with technology-appropriate assumptions to simulate different vehicle types with distinct communication capabilities. The stability analysis of the resulting traffic stream behavior using this framework is presented for different market penetration rates of connected and autonomous vehicles. The analysis reveals that connected and autonomous vehicles can improve string stability. Moreover, automation is found to be more effective in preventing shockwave formation and propagation under the model’s assumptions. In addition to stability, the effects of these technologies on throughput are explored, suggesting substantial potential throughput increases under certain penetration scenarios.

[1]  Jian Sun,et al.  Simulation Framework for Vehicle Platooning and Car-following Behaviors Under Connected-vehicle Environment , 2013 .

[2]  Alireza Talebpour,et al.  MODELING ACCELERATION BEHAVIOR IN A CONNECTED ENVIRONMENT , 2015 .

[3]  P. Varaiya,et al.  Sketch of an IVHS systems architecture , 1991, Vehicle Navigation and Information Systems Conference, 1991.

[4]  David Jarrett,et al.  Stability analysis of the classical car-following model , 1997 .

[5]  R. B. Potts,et al.  Car-Following Theory of Steady-State Traffic Flow , 1959 .

[6]  Samer H. Hamdar Modeling driver behavior as a stochastic hazard-based risk-taking process , 2009 .

[7]  Hani S. Mahmassani,et al.  Multiregime Sequential Risk-Taking Model of Car-Following Behavior , 2011 .

[8]  P. G. Gipps,et al.  A behavioural car-following model for computer simulation , 1981 .

[9]  Harilaos N. Koutsopoulos,et al.  A microscopic traffic simulator for evaluation of dynamic traffic management systems , 1996 .

[10]  Petros A. Ioannou,et al.  Traffic Density Control for Automated Highway Systems , 1997, Autom..

[11]  Robert A Ferlis The Dream of the Automated Highway , 2007 .

[12]  Katie Larsen McClarty,et al.  Life in the Fast Lane , 2015 .

[13]  Maarten Steinbuch,et al.  String-Stable CACC Design and Experimental Validation: A Frequency-Domain Approach , 2010, IEEE Transactions on Vehicular Technology.

[14]  Meng Wang,et al.  Modelling supported driving as an optimal control cycle: Framework and model characteristics , 2013 .

[15]  E. Montroll,et al.  Traffic Dynamics: Studies in Car Following , 1958 .

[16]  A. Tentner,et al.  Simulation of vehicle traffic on an automated highway system , 1998 .

[17]  Hani S. Mahmassani,et al.  Life in the Fast Lane , 2009 .

[18]  Martin Treiber,et al.  Traffic Flow Dynamics: Data, Models and Simulation , 2012 .

[19]  Dirk Helbing,et al.  Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[20]  A. Tversky,et al.  Prospect theory: analysis of decision under risk , 1979 .

[21]  Dirk Helbing,et al.  Influence of Reaction Times and Anticipation on Stability of Vehicular Traffic Flow , 2007 .

[22]  Pravin Varaiya,et al.  The Automated Highway System: A Transportation Technology for the 21st Century , 1996 .

[23]  Christopher D. Wickens,et al.  A model for types and levels of human interaction with automation , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[24]  Pravin Varaiya,et al.  A Theory of Traffic Flow in Automated Highway Systems , 1996 .

[25]  Helbing,et al.  Congested traffic states in empirical observations and microscopic simulations , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[26]  Robert Herman,et al.  Traffic Dynamics: Analysis of Stability in Car Following , 1959 .

[27]  R. E. Wilson,et al.  Car-following models: fifty years of linear stability analysis – a mathematical perspective , 2011 .

[28]  Meng Wang,et al.  Rolling horizon control framework for driver assistance systems. Part II: Cooperative sensing and cooperative control , 2014 .

[29]  Petros A. Ioannou,et al.  Analysis of traffic flow with mixed manual and semiautomated vehicles , 2003, IEEE Trans. Intell. Transp. Syst..

[30]  Bart van Arem,et al.  The Impact of Cooperative Adaptive Cruise Control on Traffic-Flow Characteristics , 2006, IEEE Transactions on Intelligent Transportation Systems.

[31]  Samer H. Hamdar,et al.  Modeling Driver Behavior as Sequential Risk-Taking Task , 2008 .

[32]  Steven A. Shafer,et al.  A computational model of driving for autonomous vehicles , 1993 .

[33]  Petros A. Ioannou,et al.  Throttle and Brake Control Systems for Automatic Vehicle following , 1994, J. Intell. Transp. Syst..

[34]  A. Tversky,et al.  Prospect Theory : An Analysis of Decision under Risk Author ( s ) : , 2007 .

[35]  R. Happee,et al.  Automated Driving: Human-Factors Issues and Design Solutions , 2012 .