Impact Analysis of Autonomous Vehicle Hybridization on Urban Road Network Capacity

Driverless technology has become an indispensable part of future transportation, and in order to study the impact of autonomous vehicles on road network capacity in the future and reveal the mutual influence characteristics of autonomous vehicles and ordinary vehicles, assuming that the autonomous vehicles follow the system-optimal paths and the ordinary vehicles follow the user-optimal paths, we construct a mixed-equilibrium two-layer planning model considering the road section capacity constraints, in which the ordinary vehicles aim at the minimization of the individual travel cost, and the autonomous vehicles aim at the system-optimal goal. The incremental method and MSA (Multiple Sequence Alignment) algorithm are used for traffic allocation, and the validity and feasibility of the model are verified by arithmetic examples. In this paper, the impact of autonomous vehicle market penetration and road network perception coefficient on road network capacity is considered comprehensively. The research results show that under specific network conditions, the road network capacity increases with the increase of the market penetration rate of autonomous cars, and the increase of the road network perception coefficient will cause the road network capacity to show a three-stage change of growth-stability-decline. Through the change rule of road network capacity, it can be seen that driverless cars have a significant impact on the enhancement of urban road network capacity, which in turn can be used to obtain traffic management strategies to improve traffic congestion, enhance road network capacity, and promote traffic intelligence and sustainable development of transportation.