Car-Following Headways in Different Driving Situations: A Naturalistic Driving Study
Meixin Zhu, Xuesong Wang, Xiaomeng Wang
Abstract
Meixin Zhu, Xuesong Wang, Xiaomeng Wang
Abstract
Car-following is the most frequent driving scenario, and is characterized by headway. Headway is a measure of the temporal space between two vehicles. It relates to the time available for a driver to react, and is a safety measure of car-following behavior. The characteristics of car-following headways remain poorly understood in China and this has a secondary effect on road alignment design, road capacity, traffic flow stability, traffic safety, and the design of many in-vehicle systems. With high validity naturalistic driving data, this study investigated the distribution of drivers’ car-following headways and how drivers would adjust their headways due to level of operating speed, visibility, roadway type, and traffic density. From 60,689 km of naturalistic driving data, 1,489 car-following events were identified. Headways were then extracted and statistically compared across different driving situations to quantify changes in car-following headways as a result of driving situations. The results of this study show that (1) the distribution of car-following headway was similar to a lognormal distribution; (2) drivers tended to maintain longer headways in slow-speed driving, nighttime, surface roads, and dense traffic conditions. The results of this study may be valuable for understanding car-following behavior and traffic simulation.
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Car-following is the most frequent driving scenario, and is characterized by headway. Headway is a measure of the temporal space between two vehicles. It relates to the time available for a driver to react, and is a safety measure of car-following behavior. The characteristics of car-following headways remain poorly understood in China and this has a secondary effect on road alignment design, road capacity, traffic flow stability, traffic safety, and the design of many in-vehicle systems. With high validity naturalistic driving data, this study investigated the distribution of drivers’ car-following headways and how drivers would adjust their headways due to level of operating speed, visibility, roadway type, and traffic density. From 60,689 km of naturalistic driving data, 1,489 car-following events were identified. Headways were then extracted and statistically compared across different driving situations to quantify changes in car-following headways as a result of driving situations. The results of this study show that (1) the distribution of car-following headway was similar to a lognormal distribution; (2) drivers tended to maintain longer headways in slow-speed driving, nighttime, surface roads, and dense traffic conditions. The results of this study may be valuable for understanding car-following behavior and traffic simulation.
Key concepts: Headway, Automotive engineering, Visibility, Traffic flow (computer networking), Transport engineering, Computer science, Simulation, Environmental science