Driver headway has recently become an important question with much attention being given to unsafe headway or 'tailgating'. This paper reviews a series of recent studies undertaken at the University of Southampton, which have sought to measure and model distance keeping, demonstrating how following distance depends on a wide range of factors, some of which are only recently being explored. These include variations in following distance for any particular driver and the relationship with time to collision, variations in following distances of drivers of differing nationalities and the ability of the driver to 'read the road ahead', which may be affected by interaction with different vehicle types. It is demonstrated that providing clear unequivocal statements regarding car following and safety levels, even after such studies, is still far from straightforward.
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http://dx.doi.org/10.1080/00140130701318665 | DOI Listing |
Traffic Inj Prev
December 2024
Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, Hubei, China.
Objective: Exit ramps are accident-prone areas of freeways. One of the reasons for this is the information overload induced by destination signs, which makes them challenging to recognize and may even result in tension or mistakes. This study examined the cognitive workload that destination signs place on drivers and the compensatory behavior they use to counteract the additional workload.
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February 2025
School of Automotive Studies, Tongji University, No. 4800, Cao-an Road, Shanghai 201804, China.
Phys Rev E
October 2024
College of Electrical Engineering, Lanzhou Institute of Technology, Lanzhou, Gansu 730050, China.
Based on the measured data, in this paper, we find that there are significant differences in the ideal speed of drivers with different attributes during their driving process. Through wavelet analysis, it is proved that the regularity of the car-following behavior of the driver is poor in a short time, and it cannot be predicted and analyzed at a small time scale. As one of the important participants in the traffic system, it is necessary to consider the driving behavior factors in the traffic flow model.
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December 2024
Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science and Technology, Changsha 410205, China.
A connected environment is crucial for improving road traffic safety and efficiency. However, it remains unclear how different connected environments affect the interaction between vehicles and their impact on driving safety and traffic efficiency in scenarios with potential risks, such as forced lane changes during emergency events. To investigate the effects of different connected environments on drivers' interaction characteristics and their impact on driving safety and traffic efficiency, a group of simulated driving test was implemented in a multi-agent interactive intelligent connected vehicle driving simulation platform.
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December 2024
The Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology, Daejeon 34051, Republic of Korea. Electronic address:
This study highlights the significance of understanding and categorizing driving styles to improve traffic safety and increase fuel efficiency. By analyzing a comprehensive dataset of naturalistic driving records from taxi drivers, it offers insight into driving behaviors in various environments. Utilizing deep clustering methodology, the research develops a novel framework for categorizing driving behaviors into Baseline Driving Characteristics (BDC), encompassing aspects such as turning, cruising, acceleration, and deceleration.
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