顾及交叉路口和车辆模态的轨迹重构与分析.

Objectives: Due to the limitation of data transmission and storage cost, the sampling rates of most trajectories are low and uncertain. However, detailed urban models often require high-frequency trajectory data, for example, microscopic vehicle emission models often require trajectory data with a time interval of 1 s. Therefore, it is of great significance to reconstruct the trajectory data using the technique of interpolation. Methods: We propose a method to interpolate low-frequency trajectories considering the road intersections and vehicle modes. First, high-frequency trajectory data are used to train the theoretical probability model of vehicle motion modes. Second, the road intersections are used to determine the mode sequence between low-frequency trajectory points. Third, the theoretical probability model is solved by the genetic algorithm to calculate the distribution of time and distance of each mode, and then complete the high-frequency reconstruction of trajectory points. Results: The results suggest that the proposed method performs better than the conventional interpolation method by decreasing the root mean square error (RMSE) value with 62.9%, and better than the mode method that does not consider the road intersection by reducing the RMSE value by 12.2%. Conclusions: Therefore, the proposed method is valuable for the reconstruction of low-frequency vehicle trajectories. [ABSTRACT FROM AUTHOR]