Document Type: Article
Jiangsu Key Laboratory of Urban ITS, Southeast University
Space headway calculation and analysis play an important role in identifying surrounding obstacles and understanding traffic scene. However, the performance of existing methods is limited by the complexity of computer processing. In addition, it is quite difficult to obtain space headway at turn movement trajectories, mainly owing to the limitation of rectilinear propagation. Therefore, a hybrid model based on spline curve and numerical integration was proposed to estimate distance of the front vehicle and vehicle trajectory in this study. The space headway at turn movement trajectories was analogous to the track of a vehicle, which could be fitted by a quadratic spline curve. Newton-Cotes numerical integration was employed to calculate distance due to its meshing flexibility and ease of implementation. Data collected from Lankershim Boulevard in the city of Los Angeles, California (USA) were used to evaluate performance of the hybrid model. Compared with another algorithm based on computer vision and trilinear method, the results showed that the proposed model worked successfully and outperformed the competing method in terms of accuracy and reliability. Finally, the proposed method was applied to investigate the effects of vehicle speed, relative speed of vehicles, and time period on the spacing of vehicles during car-following.