A Sustainable Approach to Modeling Human-Centric and Energy-Efficient Vehicle Acceleration Profiles in Non-Car-Following Scenarios

Wei Deng, Yi Luo, Shaopeng Yang, Yini Ren (), Dongyi Hu and Yong Shi ()
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Wei Deng: School of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang 550025, China
Yi Luo: School of Management, Guizhou University of Commerce, Guiyang 550014, China
Shaopeng Yang: School of Management, Guizhou University of Commerce, Guiyang 550014, China
Yini Ren: Department of Rail Transit Engineering, Guizhou Communications Polytechnic University, Guiyang 551400, China
Dongyi Hu: School of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang 550025, China
Yong Shi: School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China

Sustainability, 2025, vol. 17, issue 14, 1-20

Abstract: Previous studies have described vehicle acceleration profiles in non-car-following scenarios; however, the underlying mechanisms governing these profiles remain incompletely understood. This study aims to enhance the understanding of these mechanisms by proposing an improved model based on an optimal control problem with two bounded conditions (OCP2B), segmenting vehicle acceleration curves into three distinct phases. Specifically, the proposed model imposes constraints on acceleration through maximum jerk and maximum acceleration functions, thereby capturing essential dynamics previously unexplained by conventional models. Our key contributions include establishing a comprehensive analytical framework for accurately describing vehicle acceleration profiles and elucidating critical characteristics overlooked in the prior literature. Our findings demonstrate that incorporating human-centric considerations, such as driving comfort, significantly enhances the model’s practical applicability. Moreover, the proposed approach provides crucial insights for designing autonomous vehicle (CAV) trajectories consistent with human driving behaviors and effectively predicts the movements of human-driven vehicles (HVs), thus facilitating smoother interactions and potentially reducing conflicts between CAVs and HVs.

Keywords: acceleration profile model; non-car-following state; optimal control problem; energy consumption (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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