Operational Evaluation of Mixed Flow on Highways Considering Trucks and Autonomous Vehicles Based on an Improved Car-Following Decision Framework

Nan Kang (), Chun Qian, Yiyan Zhou and Wenting Luo
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Nan Kang: College of Transportation Engineering, Nanjing Tech University, Nanjing 211816, China
Chun Qian: College of Transportation Engineering, Nanjing Tech University, Nanjing 211816, China
Yiyan Zhou: College of Transportation Engineering, Nanjing Tech University, Nanjing 211816, China
Wenting Luo: College of Transportation Engineering, Nanjing Tech University, Nanjing 211816, China

Sustainability, 2025, vol. 17, issue 14, 1-18

Abstract: This study proposes a new method to improve the accuracy of car-following models in predicting the mobility of mixed traffic flow involving trucks and automated vehicles (AVs). A classification is developed to categorize car-following behaviors into eight distinct modes based on vehicle type (passenger car/truck) and autonomy level (human-driven vehicle [HDV]/AV) for parameter calibration and simulation. The car-following model parameters are calibrated based on the HighD dataset, and the models are selected through minimizing statistical error. A cellular-automaton-based simulation platform is implemented in MATLAB (R2023b), and a decision framework is developed for the simulation. Key findings demonstrate that mode-specific parameter calibration improves model accuracy, achieving an average error reduction of 80% compared to empirical methods. The simulation results reveal a positive correlation between the AV penetration rate and traffic flow stability, which consequently enhances capacity. Specifically, a full transition from 0% to 100% AV penetration increases traffic capacity by 50%. Conversely, elevated truck penetration rates degrade traffic flow stability, reducing the average speed by 75.37% under full truck penetration scenarios. Additionally, higher AV penetration helps stabilize traffic flow, leading to reduced speed fluctuations and lower emissions, while higher truck proportions contribute to higher emissions due to increased traffic instability.

Keywords: traffic engineering; car-following model; mixed passenger car and truck; autonomous vehicle; traffic flow characteristics; emissions reduction (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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