Article
Engineering, Mechanical
Sunita Yadav, Poonam Redhu
Summary: According to traffic flow theory, driver behavior significantly affects traffic stability. This research proposes a novel car-following model that considers both the driver's cautious and aggressive instincts. Numerical simulations and theoretical analyses show that the aspects of the enhanced model related to driver characteristics have a major impact on traffic flow stability.
JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS
(2023)
Article
Construction & Building Technology
Ting Shang, Jiaxin Lu, Ying Luo, Song Wang, Zhengyu He, Aobo Wang
Summary: The study reveals significant variations in car-following behavior across different types of tunnels and consecutive sections of the same tunnel. As tunnel length increases, the driving stability of following vehicles decreases, but the level of driving safety risk is not positively correlated with tunnel length. Significant vehicle trajectory oscillation is observed within the inner sections of long and extra-long tunnels, and a significant relationship between the acceleration of following vehicles and the location within the tunnel section is found.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Environmental Studies
Shirui Zhou, Junfang Tian, Ying-En Ge, Shaowei Yu, Rui Jiang
Summary: This paper investigates the emissions and fuel consumption features in a car-following platoon using two experimental datasets. Four classical models are employed for emissions and fuel consumption prediction, and a universal concave growth pattern is observed. The study also tests a general framework for coupling emissions and car-following models, finding that all models perform well at the vehicle-pair level. However, at the platoon level, the predicted fuel consumption remains constant, which is different from the experimental observation. The research highlights the significance of considering oscillation growth and evolution in fuel consumption estimation for platoon-level analysis.
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2023)
Article
Transportation Science & Technology
Michail Makridis, Konstantinos Mattas, Aikaterini Anesiadou, Biagio Ciuffo
Summary: ACC systems are becoming more prevalent in commercial vehicles, with limited public information available on their operation differences across vehicle manufacturers. As they are introduced for comfort rather than safety, implications of their interactions with other road users are not effectively monitored or improved. OpenACC aims to provide a unified data structure for research into ACC vehicles to understand their impacts on traffic flow and the need for regulations and new car-following models.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Computer Science, Interdisciplinary Applications
Hua Kuang, Fang-Hua Lu, Feng-Lan Yang, Guang-Han Peng, Xing-Li Li
Summary: An extended car-following model incorporating driver's memory and mean expected velocity field effects is proposed in this paper, showing significantly improved traffic stability compared to existing models. Numerical simulations demonstrate that the coupling effect of driver's memory and mean expected velocity field can effectively suppress traffic congestion.
INTERNATIONAL JOURNAL OF MODERN PHYSICS C
(2021)
Article
Physics, Multidisciplinary
Weixiu Pan, Jing Zhang, Junfang Tian, Fengying Cui, Tao Wang
Summary: This paper proposes a combination car-following model that integrates a theory-driven model with a data-driven model. By optimizing parameters and integrating prediction outcomes, the model improves accuracy and controllability, and achieves significant error reduction in numerical simulations.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Chemistry, Analytical
Pinpin Qin, Hao Li, Ziming Li, Weilai Guan, Yuxin He
Summary: In this study, a novel car-following model that combines a convolutional neural network (CNN) with a long short-term memory (LSTM) network is proposed to explore the potential relationship between the leading vehicle and the following vehicle during car-following. The proposed CNN-LSTM model analyzes the dynamic parameters of the vehicle and extracts car-following behavior features using CNN, while LSTM is used to predict the speed of the following vehicle. The results show that the proposed model outperforms the other two classical car-following models in terms of accuracy and ability to learn the heterogeneity. Additionally, the CNN-LSTM model accurately reproduces the hysteresis phenomenon of congested traffic flow and is capable of handling heterogeneous traffic flow mixed with adaptive cruise control vehicles on the freeway, indicating its strong generalization ability.
Article
Computer Science, Interdisciplinary Applications
Xiaoqin Li, Yanyan Zhou, Guanghan Peng
Summary: A new optimal velocity model is proposed in this study, which takes into account the traffic interruption probability and self-expected velocity. The linear stable condition and mKdV equation are derived based on the self-interruption probability of the current optimal velocity from linear stable analysis and nonlinear analysis, respectively. The numerical simulation demonstrates that the self-interruption probability of the current optimal velocity can enhance traffic stability, highlighting its significant influence on the traffic system.
INTERNATIONAL JOURNAL OF MODERN PHYSICS C
(2022)
Article
Economics
Prateek Bansal, Rubal Dua
Summary: China and India, as the largest automotive markets and carbon emitters, have considered various policy levers to reduce carbon emissions. This study finds that fuel consumption in both markets is relatively unresponsive to fuel price and income, but responsive to car price and fuel economy. The rebound effect on fuel savings is 17.1% for India and 18.8% for China, indicating a slight decrease in effectiveness of a feebate policy.
Article
Computer Science, Information Systems
Majid Abdollahzade, Reza Kazemi
Summary: This paper presents a new approach, called Evolving Time-Variant Local Model (ETLM), to address the structural and range variations in car-following process. The ETLM is an intelligent model capable of changing its structure and adapting its parameters. It consists of a network of temporal local linear models, each representing a range of car-following behaviors. A decision-making procedure is designed to determine whether the model should evolve to a new structure or adapt its parameters to describe new car-following behaviors. Experimental results demonstrate the efficacy and superiority of the ETLM model compared to other methods.
Article
Engineering, Marine
Wenzhang Yang, Shangkun Jiang, Peng Liao, Hao Wang
Summary: Due to the importance of inland waterway transport in reducing carbon emissions, the demand for inland vessel transportation has significantly increased in China. This research conducted experiments to study vessel-following behavior in different restricted waterways and proposed improved car-following models to describe this behavior accurately. The results provide valuable insights into the characteristics of inland vessel traffic, aiding professionals in understanding and organizing inland waterway transportation effectively.
Article
Mathematics, Applied
Yanfei Jin, Jingwei Meng, Meng Xu
Summary: In this paper, a novel delayed-feedback control scheme based on a car-following model is proposed to stabilize the unstable traffic flow and suppress traffic jam. The control scheme utilizes the difference information of velocity and acceleration, and does not require the knowledge of other vehicles' velocity and acceleration. By calculating the number of unstable eigenvalues of the characteristic equation, the stable intervals of time delays and feedback gains are obtained. The proposed control method expands the stable delay interval and improves the robust performance compared to the control based on velocity difference. Numerical simulations validate the theoretical results.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2022)
Article
Computer Science, Information Systems
Mehmet Fatih Ozkan, Yao Ma
Summary: This study uses Inverse Reinforcement Learning to model diverse car-following behaviors of human drivers when interacting with connected and automated vehicles (CAVs) and other human-driven vehicles. The personalized driving behaviors accurately characterize different types of preceding vehicles, and the energy efficiency of different human-driven vehicles varies among tested human drivers due to their intrinsic preferences and perception of CAV. Overall, the findings suggest that human-CAV interactions can effectively improve the energy efficiency of mixed traffic.
Article
Green & Sustainable Science & Technology
Aaron Kolleck
Summary: The sharing economy, particularly car-sharing, is gaining popularity and shows significant impact on car ownership levels. A study in 35 large German cities found that each additional station-based shared car is associated with a reduction of about nine private cars. Neither station-based nor free-floating car-sharing appears to significantly impact the markets for used and new cars, prompting further research on the dynamics of car markets.
Article
Physics, Multidisciplinary
Guang-Han Peng, Rui Tang, Hua Kuang, Hui-Li Tan, Tao Chen
Summary: A new coupled map car-following model is proposed in this paper, which aims to improve the stability of vehicle running and reduce CO2 emissions by considering the difference in estimated optimal speeds. The stability of the new model is analyzed using control theory, and conditions for the stability of the traffic system are obtained. Simulation results show that the inclusion of optimal estimation of speed difference in the control term can effectively enhance the stability of vehicle running in the CM car-following model.
