ABSTRACT：Car-following models are an essential component of microscopic traffic simulation, as well as increasingly being used in research and practice on intelligent transportation systems and advanced driver assistance systems. Through analyzing driving behavior characteristics, the models are critical to road design and traffic management. Lack of reliable traffic data in China, however, has limited the use of car-following models, particularly for surface roads. Yet infrastructural issues such as inadequate road design, poor maintenance, and insufficient traffic management have exacerbated the problems caused by mixed traffic flow on surface roads, which is composed of a complex mix of motor vehicles, non-motorized vehicles, and pedestrians. To address this need, five typical car-following models were calibrated and validated with 4,400 surface road car-following events extracted from the 161,055 km of data collected in the Shanghai Naturalistic Driving Study (SH-NDS). The models were evaluated based on their parameter estimates and the Root Mean Square Percentage Errors (RMSPE). Results showed that 1) the Intelligent Driver Model, with a calibration error of 24% and a validation error of 28%, performed best in modeling drivers’ car-following behavior on surface roads; 2) in comparison to car-following behavior on expressways, drivers on surface roads tend to assume a relatively low car-following speed and maintain a slightly higher time headway and maximum acceleration. Due to the IDM’s demonstrated high performance on expressways and surface roads, it is reasonable to assume the model may be used to analyze other roadway types.
Xuesong Wang*, Linjia He, Meixin Zhu, Chen Chai. Calibrating Car-Following Models on Surface Roads Using Shanghai Naturalistic Driving Data. Transportation Research Board 98th Annual Meeting, Washington D.C., USA, 2019. 1.13-17.