A significant portion of the observed variability in roadway performance can be due to the difference and innate heterogeneity in drivers’ behaviour. Analytical models, stated preference data collection and studies and laboratory-based simulator experiments are developed to understand the driver behaviour for years. However, little has been done to fill the important gap between the survey/laboratory observed behaviour and the field observed behaviour. This study investigates drivers’ actual behaviour by conducting real-world field experiments in Beijing's roadway system. In the experiment platform developed, instrumented vehicles are employed for the advanced data collection and analysis in order to understand the impact of roadway category on drivers’ longitudinal behaviour, that is, car-following and car-approaching. These behaviour dimensions are identified in this study and quantified by parameters including relative speed, leading vehicle speed, accelerator release, braking activation, distance headway, time headway and time-to-collision. The analysis suggests that the drivers’ behaviour variation heavily depends on roadway characteristics, which supplements further theoretical and survey-based behavioural research. The research findings provide insight for theoretical advances, evaluating driving assistance systems and roadway-specific incentive designs for traffic harmonisation, speed reduction, collision warning/avoidance, safety enhancement and energy consumption savings.
Longitudinal driving behaviour on different roadway categories: an instrumented-vehicle experiment, data collection and case study in China
IET Intelligent Transport Systems ; 9 , 5 ; 555-563
2015-06-01
9 pages
Article (Journal)
Electronic Resource
English
Beijing roadway system , driving assistance system , instrumented-vehicle experiment , roadway categories , stated preference data collection , energy consumption savings , China , data collection , speed reduction , safety enhancement , traffic harmonisation , longitudinal driving behaviour , road traffic , collision warning-avoidance , driver information systems
Metadata by IET is licensed under CC BY 3.0
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