Simulating platooned connected autonomous vehicle in a large scale urban scenario
Keywords:CAVs, Connected Autonomous Vehicles, micro-simulation, large-scale urban
This article is concerned with the performance evaluation of connected, autonomous vehicles (CAVs) in a realistic large-scale microsimulation scenario. In particular the question is: how much could a high diffusion of CAVs possibly change (1)~the average travel speeds (2)~the trip times of all traffic participants, including pedestrians, and (3)~the energy/fuel consumption? For this purpose, admittedly favourable assumptions are made: a 100\% diffusion of platooning-capable CAVs as substitution for private cars as well as a high maximum speed of platooned vehiclesin order to enable platoon formation. The morning rush hour scenario of the metropolitan area of Bologna, Italy has been selected for assessment. This scenario, which has been created and validated in previous works, represents an activity based demand model with travel plans for individual citizens, including all relevant transport modes. The microsimulation is performed by means of the SUMO simulator. The entire demand has been generated with the SUMOPy tool. For the platooning of CAVs, SUMO's SIMPLA module has been used, which controlls the vehicles via the interactive TRACI API.
Results show an increased speed and reduced travel time for CAV vehicles, with respect to human driven cars, in particular in the periphery and less in the center with a dense road network. However, the reason for improved speeds and travel times is predominantly the higher maximum speed allowed for vehicles trying to catch up and join a platoon. Furthermore these higher speed would also be resposible for an increase in fuel consumption of approximately 5\%.
In conclusion, CAVs alone are unlikely to reduce congestion in an urban area. To make the platooning concept work, additional technology and infrastructure is required in order to merge platoons effectively at freeways and at traffic lights. The latter could be simulated with GLOSA.
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