Modeling and Analysis of Driving Behaviour for Heterogeneous Traffic Flow Considering Market Penetration under Capacity Constraints

Abstract

Based on analytical and simulation methods, this paper discusses the path choice behavior of mixed traffic flow with autonomous vehicles, advanced traveler information systems (ATIS) vehicles and ordinary vehicles, aiming to promote the development of autonomous vehicles. Firstly, a bi-level programming model of mixed traffic flow assignments constrained by link capacity is established to minimize travel time. Subsequently, the algorithm based on the incremental allocation method and method of successive averages is proposed to solve the model. Through a numerical example, the road network capacity under different modes is obtained, the impact of market penetration on travel time is analyzed, and the state and characteristics of single equilibrium flow and mixed equilibrium flow are explored. Analysis results show that the road network can be maximized based on saving travel time when all vehicles are autonomous, especially when the autonomous lane is adopted. The travel time can be shortened by increasing the market penetration of autonomous vehicles and ATIS vehicles, while the former is more effective. However, the popularization of autonomous vehicles cannot be realized in the short term; the market penetration of autonomous vehicles and ATIS vehicles can be set to 0.2 and 0.6, respectively, during the introduction period.

A Regional Road Network Capacity Estimation Model for Mountainous Cities Based on Auxiliary Map

“…Later, Jiang et al [16] used the traffic assignment simulation method to calculate road network capacity while accounting for the effects of parking supply and parking prices to evaluate urban road network capacity. Zhou et al [17] developed a twotier planning model for hybrid traffic flow assignment and proposed to save the time cost of travel by increasing the market penetration of driverless cars. This calculation method is simple and practical and can identify key sections of the road network.…”