Macroscopic Fundamental Diagram Approach to Traffic Flow With Autonomous/Connected Vehicles

US Transportation Collection2020PDFTech ReportWhalin, Robert WHu, GuojingJackson State UniversitySoutheastern Transportation Research, Innovation, Development and Education Center (STRIDE)United States. Department of Transportation. Office of the Assistant Secretary for Research and TechnologyUnited States. Department of Transportation. University Transportation Centers (UTC) ProgramSoutheastern Transportation Research, Innovation, Development and Education Center (STRIDE)United StatesAlgorithmsAutonomous vehiclesConnected vehiclesHighway capacityLane changingMacroscopic traffic flowMarket penetrationNetwork analysis (Planning)Reaction timeSimulationTraffic platooningUrban highwaysVehicle mixTraffic flowOptimizationTraffic controlConnected Autonomous vehiclesProject O269A3551747104Final Report: 9/1/2018 to 3/25/2021Connected and autonomous vehicles (CAVs) have the capability to acquire real-time information from each other while human-driven vehicles (HVs) are standalone in the vehicle roadway navigation system. The information asymmetry poses great challenges in managing and controlling vehicles in the mixed traffic. To address such challenges, a new lane-changing algorithm is proposed for CAV platoons to bypass a relatively slow HV. The simulation results indicate that, at all the CAV penetration levels, the proposed lane-changing algorithm provides significant performance improvements to the whole mixed traffic flow, in terms of outflow and travel time. In addition, how and when the benefits associated with CAVs will start to impact the performance of urban network is a question of interest for traffic operators. This study proposes an analytical capacity model for mixed urban corridors based on the concept of macroscopic fundamental diagram (MFD). The model incorporates not only the full spectrum of CAV penetration rates, but also reaction time of different vehicle types resulted from different CAV technologies. Numerical experiments verify that different reaction time settings yield disparate results. As CAV penetration rates increased, the urban street capacity may increase or decrease depending upon the reaction time settings. Finally, the concept of MFD is adopted as an index to evaluate the traffic network capacity and further appraise the quality of discrete network design problem (DNDP). This study formulates a bi-level programming model where in the lower level, traffic flows are assigned to the newly extended network subject to user equilibrium theory, and the upper level determines which links should be added to achieve the maximum network capacity. Finally, the methodology is implemented in a test network and the simulation results verify the capacity benefit of using the MFD-based method to solve the NDP under stochastic OD demands. Specifically, capacity paradox is also presented in the test results.120