Analysis of the Effects of Connected–Automated Vehicle Technologies on Travel Demand

Connected–automated vehicle (CAV) technologies are likely to have significant effects not only on how vehicles operate in the transportation system, but also on how individuals behave and use their vehicles. While many CAV technologies—such as connected adaptive cruise control and ecosignals—have the potential to increase network throughput and efficiency, many of these same technologies have a secondary effect of reducing driver burden, which can drive changes in travel behavior. Such changes in travel behavior—in effect, lowering the cost of driving—have the potential to increase greatly the utilization of the transportation system with concurrent negative externalities, such as congestion, energy use, and emissions, working against the positive effects on the transportation system resulting from increased capacity. To date, few studies have analyzed the potential effects on CAV technologies from a systems perspective; studies often focus on gains and losses to an individual vehicle, at a single intersection, or along a corridor. However, travel demand and traffic flow constitute a complex, adaptive, nonlinear system. Therefore, in this study, an advanced transportation systems simulation model—POLARIS—was used. POLARIS includes cosimulation of travel behavior and traffic flow to study the potential effects of several CAV technologies at the regional level. Various technology penetration levels and changes in travel time sensitivity have been analyzed to determine a potential range of effects on vehicle miles traveled from various CAV technologies.

[1]  Suzanne Childress,et al.  Using an Activity-Based Model to Explore the Potential Impacts of Automated Vehicles , 2015 .

[2]  K. Small,et al.  Induced demand and rebound effects in road transport , 2010 .

[3]  Steven E. Shladover,et al.  Effects of Adaptive Cruise Control Systems on Highway Traffic Flow Capacity , 2002 .

[4]  Steven E Shladover,et al.  Cooperative Adaptive Cruise Control: Driver Acceptance of Following Gap Settings Less than One Second , 2010 .

[5]  Hani S. Mahmassani,et al.  An evaluation tool for advanced traffic information and management systems in urban networks , 1994 .

[6]  Hans Fritz,et al.  Fuel Consumption Reduction in a Platoon: Experimental Results with two Electronically Coupled Trucks at Close Spacing , 2000 .

[7]  Don MacKenzie,et al.  Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles , 2016 .

[8]  Daniel J. Fagnant,et al.  Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations , 2015 .

[9]  Joshua Auld,et al.  Framework for the development of the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model , 2009 .

[10]  Steven E Shladover,et al.  Impacts of Cooperative Adaptive Cruise Control on Freeway Traffic Flow , 2012 .

[11]  G. F. Newell.  A simplified theory of kinematic waves in highway traffic, part I: General theory , 1993 .

[12]  Stephen D. Boyles,et al.  Effects of Autonomous Vehicle Ownership on Trip, Mode, and Route Choice , 2015 .

[13]  Tsu Chin,et al.  California Partners for Advanced Transit and Highways (PATH) , 2004 .

[14]  Jianqiang Wang,et al.  Economy-oriented vehicle adaptive cruise control with coordinating multiple objectives function , 2013 .

[15]  Xiao-Yun Lu,et al.  Automated Truck Platoon Control , 2011 .

[16]  Robert Cervero,et al.  Induced An Urban Metropolitan Perspective , 2003 .

[17]  L Fred,et al.  California Partners for Advanced Transportation Technology , 1993 .

[18]  Constantine Samaras,et al.  Bounding the Potential Increases in Vehicles Miles Traveled for the Non-Driving and Elderly Populations and People with Travel-Restrictive Medical Conditions in an Automated Vehicle Environment , 2015 .

[19]  Karl Henrik Johansson,et al.  An experimental study on the fuel reduction potential of heavy duty vehicle platooning , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[20]  Yoshiaki Nemoto,et al.  Development of a Small-Distance Vehicle Platooning System , 2009 .

[21]  Fred Browand,et al.  Fuel Saving Achieved in the Field Test of Two Tandem Trucks , 2004 .

[22]  Sadayuki Tsugawa,et al.  An Overview on an Automated Truck Platoon within the Energy ITS Project , 2013 .

[23]  Jeffrey Gonder,et al.  An Analysis of Possible Energy Impacts of Automated Vehicles , 2014 .

[24]  Joshua Auld,et al.  POLARIS: Agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations , 2016 .