Research progress on car-following models

YANG Long-hai; ZHANG Chun; CHOU Xiao-yun; LI Shuai; WANG Hui

doi:10.19818/j.cnki.1671-1637.2019.05.013

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YANG Long-hai, ZHANG Chun, CHOU Xiao-yun, LI Shuai, WANG Hui. Research progress on car-following models[J]. Journal of Traffic and Transportation Engineering, 2019, 19(5): 125-138. doi: 10.19818/j.cnki.1671-1637.2019.05.013

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YANG Long-hai, ZHANG Chun, CHOU Xiao-yun, LI Shuai, WANG Hui. Research progress on car-following models[J]. Journal of Traffic and Transportation Engineering, 2019, 19(5): 125-138. doi: 10.19818/j.cnki.1671-1637.2019.05.013

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Research progress on car-following models

doi: 10.19818/j.cnki.1671-1637.2019.05.013

1.
School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, Heilongjiang, China
2.
Shenzhen Urban Transport Planning Center, Shenzhen 518057, Guangdong, China

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Author Bio:
YANG Long-hai(1970-), male, associate, professor, PhD, yanglonghai@hit.edu.cn
Received Date: 2019-04-23
Publish Date: 2019-10-25

Abstract

Abstract

The researches on the car-following models in the past 70 years were reviewed. According to the modeling methods, car-following models were divided into two types: theory-driven model and data-driven model, and the hotspots were summarized. The theory-driven car-following model was reviewed from five aspects: human factor, infrastructure, traffic information, heterogeneous traffic flow, and new modeling theory. According to different machine learning algorithms, the data-driven car-following model was also reviewed from five aspects: fuzzy logic, artificial neural network, instance learning, support vector regression, and deep learning. Analysis result shows that the theory-driven car-following model can theoretically deduces the traffic phenomenon. But it is difficult to comprehensively consider the influencing factors, and some human factors are difficult to quantify, and the explanation of driver decision-making process is not accurate enough. The car-following model of heterogeneous traffic flow lacks effective theoretical basis and formal proof under general traffic conditions. The data-driven car-following models summarize the traffic rules by traffic phenomenon. Due to different of data sources, evaluation indicators and methods, the models based on machine learning algorithms cannot be systematically compared. The data-driven models focuse on micro-angles to study driving behavior characteristics, but are not very explanatory for complex traffic phenomena(such as traffic oscillation, hysteresis, etc.). The research of the car-following models should innovate the data collection method, and capture the drivers' psychological tendencies, perceptual characteristics and cognitive abilities, as well as quantify the influence of human factors and make full use of big data. The data-driven car-following models should provide technical support for the development of driverless technology. Before the automatic driving is fully popularized, the characteristics of drivers' car-following behaviors in the mixed scene of manual driving and automatic driving need to be further studied.
- traffic information,
- car-following model,
- theory-driven model,
- data-driven model,
- human factor

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References

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