微观轨迹信息驱动的Bi-LSTM合流区车速预测

Bi-LSTM merging area speed prediction driven by microscopic trajectory information

为明确城市快速路合流区的微观速度特性,确保车辆在衔接段运行速度协调可控,使车辆安全运行。首先,基于无人机高空视频,从广域视角提取了典型多车道交织区全样本高精度车辆轨迹数据,分析车速的累积频率、分布趋势、特征百分位值等运行特性。然后,基于可有效捕捉前向历史速度数据的变化特征的LSTM模型,构建Bi-LSTM车速预测模型;考虑到人工设置训练参数对模型预测性能的影响较大、时间较长,提出基于遗传算法优化的Bi-LSTM速度预测模型(GA-BiLSTM)。最后,以R^(2)、Error Mean、Error StD、MSE、RMSE、NRMSE、秩相关rs这7类评价指标,建立多指标融合的评价方案。结果表明:GA-Bi-LSTM速度预测模型表现较优,拟合指标R^(2)、秩相关rs分别为0.9046、0.9495,误差指标Error Mean、Error StD、MSE、RMSE、NRMSE分别为0.0041、0.4470、0.1997、0.4469、0.0765。研究成果可为城市快速路的合流区车速调控提供理论依据。

In order to guarantee the vehicle safety,it is necessary to clarify the microscopic speed characteristics of the urban expressway merging area and to ensure the coordination and control of the vehicle speed in the area.First,after the full-sample high-precision vehicle trajectory data of typical multi-lane interweaving area were extracted from a wide-area view based on the UAV overhead video,the operational characteristics of vehicle speed,such as cumulative frequency,distribution trend,and characteristic percentile value,were analyzed.Then,the Bi-LSTM vehicle speed prediction model was constructed based on the LSTM model that could effectively capture the change characteristics of forward historical speed data.Considering the significant effect of manual setting of training parameters on the model prediction performance and the long time they take,the Bi-LSTM speed prediction model based on genetic algorithm optimization(GA-Bi-LSTM)was proposed.Finally,a multimetric fusion evaluation scheme was established with seven types of evaluation metrics,namely,R^(2),Error Mean,Error StD,MSE,RMSE,NRMSE,and Rank Correlation.The results show that the GA-Bi-LSTM speed prediction model performs better,with the fitting indicators R^(2)and Rank Correlation rs of 0.9046 and 0.9495,respectively,and the error indicators Error Mean,Error StD,MSE,RMSE,and NRMSE of 0.0041,0.4470,0.1997,0.4469 and 0.0765,respectively.The findings can provide a theoretical basis for speed regulation in merging zones of urban expressways.