Abstract
AbstractResearch of the car-following model is always of great interest. Recent studies have indicated that drivers’ following behavior varies when the lead vehicle is a passenger car as opposed to a heavy vehicle. In the age of big data, this paper presents a data-driven car-following model that is able to capture the characteristics of driving behavior contained in the field data. By using the Support Vector Regression approach and incorporating vehicle-type dependent parameters, the data-driven model is capable of reproducing car-following behaviors of multiple vehicle types with high prediction accuracy. Three vehicle-type combinations are considered in this paper: car-following-car, car-following-truck, truck-following-car. The data analysis results confirm that there exists a strong correlation between driving behavior and vehicle type and the significant differences are revealed in desired space headway and desired speed among different vehicle-type combinations. These findings provide insight into driving behavior in mixed traffic flow and illustrate the necessity of taking vehicle types into account in microscopic traffic simulation.KeywordsCar-Following ModelSupport Vector RegressionNGSIMMixed Traffic FlowVehicle-Type Combination
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