基于注意力卷积长短时记忆模型的城市出租车流量预测

Urban taxi traffic flow prediction based on attentive ConvLSTM-ResNet model

为解决城市交通拥堵和安全问题,提出一种注意力卷积长短时记忆(ConvLSTM)残差(ACLR)模型,该模型通过结合ConvLSTM、注意力机制和残差结构,分别处理出租车流量的时间、空间、和其他特征,挖掘区域兴趣点(POI)数据对出租车流量的影响,有效提升交通时空特征的提取能力。同时,引入专门的学习元件考虑外部因素和POI密度对交通流量的影响,并利用北京市出租车轨迹数据验证。结果表明:ACLR模型在城市交通流预测中的精度高于差分自回归滑动平均(ARIMA)模型、长短时记忆(LSTM)网络、深度时空残差网络(ST-ResNet)、卷积神经网络(CNN)-残差神经单元-LSTM(CRL)循环神经网络、ACFM等模型,在无POI密度和考虑POI密度的情况下,均有助于提升模型的预测性能,ACLA模型的预测值与真实值基本一致,高峰时段也能与真实值较好地吻合,有效提升交通时空特征的提取能力,降低预测误差,使得交通流量预测性能得到优化。

In order to address the challenges of urban traffic congestion and safety,an ACLR model was proposed.By integrating ConvLSTM,attention mechanisms,and residual structures,the ACLR model effectively enhanced the extraction of spatio-temporal traffic features.The time,space and other characteristics of taxi traffic were processed respectively,and the influence of regional point of interest(POI)data on taxi traffic was mined.Additionally,a specialized learning component was incorporated to capture the impact of external factors and point-of-interest density on traffic flow.Using taxi trajectory data from Beijing,the ACLR model demonstrates superior prediction accuracy compared to other models such as the autoregressive integrated moving average(ARIMA)model,long short-term memory(LSTM),deep spatio-temporal residual networks(ST-ResNet),convolutional neural network(CNN)-ResNet-LSTM(CRL),and attentive crowd flow machines(ACFM)in urban traffic flow forecasting,which is helpful to improve the prediction performance of the model without POI density or considering POI density.The predicted value of the ACLA model is basically consistent with the real value,and it can also be in good agreement with the real value during peak hours,which effectively improves the ability to extract traffic temporal and spatial characteristics,reduces the prediction error,and optimizes the traffic flow prediction performance.