Investigating Traffic Characteristics at Freeway Merging Areas in Heterogeneous Mixed-Flow Environments

Shubo Wu, Yajie Zou, Danyang Liu (2231374@tongji.edu.cn), Xinqiang Chen, Yinsong Wang and Amin Moeinaddini
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Shubo Wu: Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China
Yajie Zou: Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China
Danyang Liu: Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China
Xinqiang Chen: Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
Yinsong Wang: Nebula Link (Shanghai) Technology Co., Ltd., Shanghai 201804, China
Amin Moeinaddini: Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran 15914, Iran

Sustainability, 2025, vol. 17, issue 5, 1-25

Abstract: The rapid development of Connected and Autonomous Vehicles (CAVs) presents challenges in managing mixed traffic flows. Previous studies have primarily focused on mixed traffic flow involving CAVs and Human-Driven Vehicles (HDVs), or on the combination of trucks and cars. However, these studies have not fully addressed the heterogeneous mixed traffic flow consisting of CAVs and HDVs, including trucks and cars, influenced by varying human driving styles. Therefore, this study investigates the influences of the market penetration rate (MPR) of CAVs, truck proportion, and driving style on operational characteristics in heterogeneous mixed traffic flow. A total of 1105 events were extracted from highD dataset to analyze four car-following types: car-following-car (CC), car-following-truck (CT), truck-following-car (TC), and truck-following-truck (TT). Principal Component Analysis (PCA) and clustering techniques were employed to categorize distinct driving styles, while the Intelligent Driver Model (IDM) was calibrated to represent the various car-following behaviors. Subsequently, microscopic simulations were conducted using the Simulation of Urban Mobility (SUMO) platform to evaluate the impact of CAVs on sustainable traffic operations, including road capacity, stability, safety, traffic oscillations, fuel consumption, and emissions under various traffic conditions. The results demonstrate that CAVs can significantly enhance road capacity, improve emissions, and stabilize traffic flow at high MPRs. For instance, when the MPR increases from 40% to 80%, the road capacity improves by approximately 25%, while stability enhances by approximately 33%. In contrast, higher truck proportions lead to reduced capacity, increased emissions, and decreased traffic flow stability. In addition, an increased proportion of mild drivers reduces capacity, raises emissions per kilometer, and improves stability and safety. However, a high proportion of mild human drivers (e.g., 100% mild drivers) may negatively impact traffic safety when CAVs are present. This study provides valuable insights into evaluating heterogeneous traffic flows and supports the development of future traffic management strategies for more sustainable transportation systems.

Keywords: heterogeneous mixed traffic flow; connected and autonomous vehicles; traffic stability; traffic emissions; microscopic simulation; sustainable transportation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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