Infrastructure Adaptation Planning for Autonomous Vehicles

016413612016Final ReportPDFTech ReportHighway designImplementationInfrastructureIntelligent vehiclesMixed integer programmingOptimizationTraffic lanesTransportation planningDesignDesignHighwaysPlanning and ForecastingVehicles and EquipmentSelf-driving AutomobilesUnited StatesFloridaSoutheastern Transportation Research, Innovation, Development and Education Center (STRIDE)Chen, ZhibinYin, YafengSoutheastern Transportation Research, Innovation, Development and Education Center (STRIDE)University of FloridaUniversity Transportation Centers Program (U.S.)United States. Department of Transportation. Office of the Assistant Secretary for Research and TechnologyUS Transportation CollectionThis report advocates the need for infrastructure planning to adapt to and further promote the deployment of autonomous vehicle (AV) technology. It is envisioned that in the future government agencies will dedicate certain lanes and areas of road networks to AVs only to facilitate the formulation of vehicle platoons to improve throughput and hopefully improve the performance of the whole network. This report consists of two applications, AV lanes and AV zones. A mathematical approach is first developed to optimize a time-dependent deployment plan of AV lanes on a transportation network with heterogeneous traffic stream consisting of both conventional vehicles (CVs) and AVs, so as to minimize the social cost and promote the adoption of AVs. The deployment plan indicates when, where, and how many AV lanes to be located. The report also presents a mathematical framework for the optimal design of AV zones in a general network. With the presence of AV zones, AVs may apply different routing principles outside of and within the AV zones. A novel network equilibrium model is thus firstly proposed to capture such mixed-routing behaviors. A mixed-integer bi-level programming model is then formulated to optimize the deployment plan of AV zones. Numerical examples are presented to demonstrate the performance of the proposed models