城市交通网格集群的Bi-LSTM的流量预测

Forecasting for urban traffic grid clusters based on Bi-LSTM

为提升交通流预测精度,深层次地挖掘交通流数据的时空特征,提出一种基于双向长短时记忆网络(Bi-LSTM)的城市交通网格集群流量预测模型。将所获得的网约车轨迹数据集网格化;考虑人为确定集群个数对结果的影响,用贝叶斯信息准则进行参数估计确定集群数,利用高斯混合模型对交通状况相似的网格进行聚类得到不同交通网格集群;利用集群内部交通网格的输入时间序列的相互影响设计多对多模型,构建Bi-LSTM模型预测不相交集群上的流量;以经典多元线性回归模型(MLRA)作为对照组进行实验验证,采用平均绝对误差(MAE)、均方根误差(RMSE)、平均绝对百分比误差(MAPE)和动态时间规整(DTW)这四类评价指标对预测结果进行综合评价,验证基于Bi-LSTM模型的城市交通网格集群流量预测的可行性。实验结果表明:MLRA模型和Bi-LSTM模型对城市交通网格集群流量的预测值小于真实值,早高峰时段尤为明显;各交通网格集群的交通状态态势相似,集群的簇内相关性较强,两类模型均可实现较好的流量预测效果,Bi-LSTM表现更优;MLRA和Bi-LSTM预测模型的精度MAE、RMSE、MAPE分别为3.2011、4.4009、0.3187,3.0687、4.2943、0.3045,Bi-LSTM与MLRA相比,模型精度分别提高了4.14%、2.40%、4.46%,说明所构建的Bi-LSTM交通流网格集群流量预测精度高、误差低,要优于MLRA模型,表现出较好的泛化性能;MLRA和Bi-LSTM的DTW结果分别为52938.6356、54815.1055,构建的Bi-LSTM模型较MLRA模型各自工作日和节假日时间序列相似性DTW结果提高3.42%,表现出更好的鲁棒性。利用城市交通流量的特点和交通轨迹数据网格化的优点,基于Bi-LSTM模型的城市交通网格集群流量预测与MLRA交通流量预测模型相比,具有精度高、误差低的特点。同时,DTW指标方面,基于Bi-LSTM对城市交通网格集群流量模型与真实流量变化趋势一致,表现出较好的鲁棒性。

This study aims to improve the accuracy of traffic prediction and to explore the spatio-temporal characteristics of traffic data in urban areas by proposing a traffic flow prediction model based on Bi-LSTM(Bidirectional Long Short Term Memory)for urban traffic grid clusters.The trajectory dataset of ride-hailing vehicles was gridified,and the Bayesian information criterion was used for parameter estimation to determine the cluster number,with considering the influence of manually determining the number of clusters on the results.The Gaussian mixture model was then employed to cluster grids with similar traffic conditions,resulting in distinct traffic grid clusters.A Multi-to-Multi model was designed by considering the mutual influence of input time series of traffic grids within each cluster.The Bi-LSTM model was established to predict traffic flow in non-overlapping clusters.Experimental validation was conducted using the classical MLRA(multiple linear regression analysis)as a control group,and four performance metrics:MAE(mean absolute error),RMSE(mean squared root error),MAPE(mean absolute percentage error)and DTW(dynamic time warping)were used to comprehensively evaluate the prediction results,confirming the feasibility and superiority of the Bi-LSTM model for city traffic grid cluster flow prediction.The results showed that both MLRA and Bi-LSTM models predicted urban traffic grid cluster traffic values were generally smaller than the real value,with more pronounced discrepancies observed during morning peak hours.Increasing data volume improved the prediction performance of the models.Traffic state dynamics within each traffic grid cluster were similar,displaying strong intra-cluster correlation.Both models achieved better traffic prediction results,with Bi-LSTM outperforming MLRA.In terms of model accuracy,the Bi-LSTM model showed improved MAE,RMSE and MAPE(3.0687,4.2943,0.3045,respectively)compared to MLRA(3.2011,4.4009,0.3187,respectively),representing a 4.14%,2.40%and 4.46%enhancement,respectively.The constructed Bi-LSTM model exhibited higher accuracy,lower error and better generalization performance.In terms of similarity result evaluation,the DTW results of MLRA and Bi-LSTM were 52938.6356 and 54815.1055 respectively.The Bi-LSTM model showed better robustness by 3.42%compared to the respective weekday and holiday time series similarity DTW results of the MLRA model.By considering the characteristics of urban traffic flow and leveraging the advantages of gridding traffic trajectory data,the Bi-LSTM-based model for urban traffic grid cluster traffic prediction exhibited high accuracy,low error and superior robustness compared to the MLRA traffic flow prediction model.Meanwhile,in terms of DTW metrics,the Bi-LSTM-based urban traffic grid cluster traffic model captured the real traffic variation trend and demonstrated excellent performance in traffic flow prediction for urban areas.