Ecological Driving for Connected and Automated Vehicles Considering Driver Acceptance at Signalized Intersection in a Mixed Traffic Flow

This work aims to design an ecological driving strategy for connected and automated vehicles (CAVs) at an isolated signalized intersection in a mixed traffic flow of CAVs and human-driven vehicles (HVs). Actually, from existing experiments and theories, we can obtain that stochasticity of HVs plays a nontrivial role in traffic flow, including the drivers’ driving personality style and the interaction between HV and CAV. To consider the uncertainty of HVs, we propose driver acceptance to describe the interaction between HV and CAV with the increase of CAV market penetration rate (MPR). Then, to estimate the arrival time of CAV accurately, we propose an improved LWR method integrating the vehicle to V2X data and detector data. The problem is formulated as a multi-objective optimization model and solved by NSGA-II. Our study indicates that multi-objective performance benefits depend on inflow rate, the MPR, and the drivers’ acceptance towards CAVs. The results show that traffic efficiency and vehicle fuel economy can be improved with the increase of CAV MPR. Even if the MPR is only 20%, the strategy we proposed can achieve significant improvement effects. And driver acceptance has a significant impact on the strategy, and improving acceptance is beneficial for improving intersection traffic efficiency and vehicle fuel economy.