Vehicle type-dependent heterogeneous car-following modeling and road capacity analysis

Due to the lack of natural driving databases containing heterogeneous traffic in the existing heterogeneous car-following modeling research, there is an urgent need for the support of a large amount of measured trajectory data for modeling. To this end, four different car-following modes of heterogeneous traffic under the influence of different vehicle types are extracted from the HighD data set, with which the statistical characteristics of the following car speed, speed difference, gap, time headway and acceleration in each mode are studied separately. Moreover, the correlation analysis of two parameters in speed-gap and speed difference-gap is carried out. On this basis, the intelligent driver model (IDM) and the full velocity difference (FVD) model are, respectively, employed to model the car-following characteristics in each mode. The results show that the existence of the truck in the following vehicle pair makes the following vehicle tend to maintain a larger gap and a smaller following speed, that is, larger time headway and gap. With the increase of trucks' ratio, the capacity of traffic decreases. The research can lay the foundation for more accurate mixed traffic flow modeling of heterogeneous human driving vehicles, and even subsequent research on heterogeneous traffic characteristics under a mixture of human driving vehicles and autonomous vehicles.

Automated summary

Due to the lack of natural driving databases containing heterogeneous traffic, the research urgently needs a large amount of measured trajectory data for modeling. This study extracts four different car-following modes of heterogeneous traffic from the HighD data set and studies the statistical characteristics of each mode separately. The results show that the existence of a truck in the following vehicle pair affects the following vehicle's speed and gap, resulting in a decrease in traffic capacity as the truck ratio increases.