Impact of Penetrations of Connected and Automated Vehicles on Lane Utilization Ratio

Xiaoyuan Wang,Hanqing Wang,Hui Xiang,Guowen He,Shijie Liu,Huili Shi,Yang Zhang

doi:10.3390/su14010474

Xiaoyuan Wang, Hanqing Wang + Show 5 more

Open Access

https://doi.org/10.3390/su14010474

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Abstract

Lane Utilization Ratio (LUR), affected by lane selection behavior directly, represents the traffic distribution on different lanes of road section for a single direction. The research on LUR, especially under Penetration Conditions of Connected and Automated Vehicles (PCCAV), is not comprehensive enough. Considering the difficulty in the conduction of real vehicle experiment and data collection under PPCAV, the lane selection model based on phase-field coupling and set pair logic, which considers the full-information of lanes, was used to carry out microscopic traffic simulation. From the analysis of microsimulation results, the basic relationships between Penetration of Connected and Automated Vehicles (PCAV), traffic volume, and Lane-Changing Times, also that between PCAV, traffic volume, and LUR in the basic section of the urban expressway were studied. Moreover, the influence of driving propensity on the effect of PCAVs was also studied. The research results could enrich the traffic flow theory and provide the theoretical basis for traffic management and control.

Highlights

The road traffic system is a complex open system, integrating the human-vehicleenvironment and other subsystems, with high randomness, indeterminacy, and real-time capability
Times (LCT) and Lane Utilization Ratio (LUR) under different Penetration of Connected and Automated Vehicles (PCAV) and traffic volumes are obtained through the simulation experiment
The LCT and LUR under different PCAVs and traffic fic volume considering driving propensity are obtained through simulation by adjusting volume considering driving propensity are obtained through simulation by adjusting the driving driving propensity propensity composition

Summary

Introduction

The road traffic system is a complex open system, integrating the human-vehicleenvironment and other subsystems, with high randomness, indeterminacy, and real-time capability. The contradiction between humans, vehicles and the environment has become more prominent. The statistics show that nearly 100,000 people are dead, and more than 250,000 people are injured in road traffic accidents every year in China [1]. Through road construction and expansion, and conventional traffic control, it is no longer possible to solve the contradiction between traffic supply and demand well. With the development of new technologies such as the Internet of Things, Artificial Intelligence, and cloud computing, the connected and automated road traffic system is gradually developing. As the critical component of the road traffic system, the vehicle has a repaid development of connection and automation technologies. The Connected and Automated Vehicle (CAV) has become the effective means to alleviate the traffic contradiction.

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