Abstract: As an unavoidable bottleneck in the traffic scene, the short-time traffic flow prediction of narrow roads is very important to optimize the path planning and improve the traffic condition. Aiming at the timeliness of narrow roads and considering the accuracy of applicable model, a short-time narrow roads traffic prediction model based on Good Node set initialization population, nonlinear parameter control and Cauchy variation perturbation was proposed. An empirical study was carried out with SUMO simulation data. The experimental results show that the improved whale algorithm has better global performance, convergence speed and stability. Compared with WOA-GRU, PSO-GRU and LSTM, RMSE decreased by 10.96%, 28.71% and 42.23%, and MAPE decreased by 13.92%, 46.18% and 52.83%, respectively, showing significant accuracy and stability.

摘要： 窄路段作为交通场景中不可避免的瓶颈路段，其短时车流量预测对优化路径规划、改善交通状况有着非常重要的意义。针对窄路段的时效性，同时考虑适用模型的准确度，提出一种基于佳点集初始化种群、非线性参数控制及柯西变异扰动的改进鲸鱼优化门控循环单元（GRU）的窄路短时车流量预测模型，以SUMO仿真数据进行了实证研究。对比实验结果显示，改进后的鲸鱼算法有较好的全局性、收敛速度且更加稳定。基于改进鲸鱼优化GRU的窄路短时车流量预测模型，指标RMSE相较于WOA-GRU、PSO-GRU、LSTM分别降低了10.96%、28.71%、42.23%，指标MAPE分别降低 了13.92%、46.18%、52.83%，有较为显著的准确性和稳定性。