MDPI - Publisher of Open Access Journals

14 pages, 1691 KB

Open AccessArticle

Modeling Mixed Traffic Flow with Connected Autonomous Vehicles and Human-Driven Vehicles in Off-Ramp Diverging Areas

by Xiangquan Chen, Zhizhou Wu and Yunyi Liang

Sustainability 2023, 15(7), 5651; https://doi.org/10.3390/su15075651 - 23 Mar 2023

Cited by 15 | Viewed by 5306

This paper focuses on modeling mixed traffic flow that comprises human-driven vehicles (HV), adaptive cruise control (ACC) vehicles, and cooperative adaptive cruise control (CACC) vehicles in the off-ramp diverging area. The car-following behaviors of HVs, ACC vehicles, and CACC vehicles are modeled using an intelligent driver model (IDM), ACC car-following model, and CACC car-following model, respectively. The lane-changing behaviors of different types of vehicles in off-ramp diverging areas are modeled using the anticipatory lane change (ALC) model and the mandatory lane change (MLC) model. These models are important for describing the interaction among different types of vehicles in mixed traffic. The safety and efficiency of mixed traffic flow are analyzed by integrating the developed car-following models and lane-changing models in numerical simulation. A one-way, two-lane scenario is established for the simulation. The results reveal that when the proportion of CACC vehicles is about 0.6, the safety and general operating efficiency of mixed traffic flow in the off-ramp area deteriorate significantly. Increasing the conservative MLC zone length can improve the average speed of traffic flow. Guiding drivers in changing lanes is one way to improve the efficiency of traffic flow. Full article

(This article belongs to the Special Issue Intelligent Vehicle-Infrastructure System and Sustainable Transportation)

► Show Figures

Figure 1

24 pages, 4613 KB

Open AccessArticle

A Dynamic Lane-Changing Driving Strategy for CAV in Diverging Areas Based on MPC System

by Hongben Liu, Xianghui Song, Bo Liu, Jia Liu, Huan Gao and Yunyi Liang

Sensors 2023, 23(2), 559; https://doi.org/10.3390/s23020559 - 4 Jan 2023

Cited by 12 | Viewed by 3819

Abstract

Freeway-diverging areas are prone to low traffic efficiency, congestion, and frequent accidents. Because of the fluctuation of the surrounding traffic flow distribution, the individual decision-making of vehicles in diverging areas is typically unable to plan a departure trajectory that balances safety and efficiency well. Consequently, it is critical that vehicles in freeway-diverging regions develop a lane-changing driving strategy that strives to improve both the safety and efficiency of divergence areas. For CAV leaving the diverging area, this study suggested a full-time horizon optimum solution. Since it is a dynamic strategy, an MPC system based on rolling time horizon optimization was constructed as the primary algorithm of the strategy. A simulation experiment was created to verify the viability of the proposed methodology based on a mixed-flow environment. The results show that, in comparison with the feasible strategies exiting to off-ramp, the proposed strategy can take over 60% reduction in lost time traveling through a diverging area under the premise of safety and comfort without playing a negative impact on the surrounding traffic flow. Thus, the MPC system designed for the subject vehicle is capable of performing an optimal driving strategy in diverging areas within the full-time and space horizon. Full article

(This article belongs to the Section Vehicular Sensing)

► Show Figures

Figure 1

Search Results (2)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI

Saved Queries

Search Filter Reset All

Years

Feature Papers

Subjects

Journals

Article Types

Countries / Regions

Search Results (2)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI