Journal of Computer Applications

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Spatial-temporal prediction model of urban short-term traffic flow based on grid division


  • Received:2021-05-21 Revised:2021-09-15 Online:2021-09-22 Published:2021-09-22



  1. 中国石油大学(华东) 海洋与空间信息学院,山东 青岛26658
  • 通讯作者: 王志海

Abstract: Accurate traffic flow prediction is very important in helping traffic management departments to take effective traffic control and guidance measures and help travelers reasonably plan routes. Aiming at the problem that the traditional deep learning model does not consider the temporal and spatial characteristics of traffic data, the STCAL (Spatial-Temporal Convolutional Attention-LSTM network) based on attention mechanism was established under the theoretical framework of Convolutional Neural Network (CNN) and Long and Short-Term Memory unit (LSTM) and combined with the temporal and spatial characteristics of urban traffic flow. Firstly, the fine-grained grid method was used to construct the spatio-temporal matrix of traffic flow; secondly, CNN model was used as a spatial component to extract the spatial characteristics of urban traffic flow in different periods; finally, the LSTM model based on attention mechanism was used as a dynamic time component to capture the temporal characteristics and trend variability of traffic flow, and realize the prediction of traffic flow. The experimental results show that compared with GRU and ST-ResNet, the RMSE index of the model STCAL is reduced by 17.15% and 7.37%, the MAE index is reduced by 22.75% and 9.14%, and the R2 index is increased by 11.27% and 2.37% respectively. At the same time, it is found that the prediction effect of the model on weekdays with high regularity is higher than that on weekends, and the prediction effect of morning peak on weekdays is the best, which can provide a basis for short-term urban regional traffic flow change monitoring.

Key words: short-term traffic flow prediction, spatial-temporal feature, Convolutional Neural Network (CNN), Long Short-Term Memory(LSTM), attention mechanism

摘要: 准确的交通流量预测在帮助交通管理部门采取有效的交通控制和诱导手段,和帮助出行者合理规划路线等方面具有重要意义。针对传统深度学习模型对交通数据时空特性考虑不足的问题,在卷积神经网络(CNN)和长短时记忆单元(LSTM)的理论框架下,结合城市交通流量的时空特性,建立了一种基于注意力机制的CNN-LSTM预测模型(STCAL)。首先,采用细粒度的网格划分方法来构建交通流量时空矩阵;其次,利用CNN模型作为空间组件来提取城市交通流量不同时期下的空间特性;最后,利用基于注意力机制的LSTM模型作为动态时间组件来捕获交通流的时序特征和趋势变动性,并实现交通流量的预测。实验表明,模型STCAL与GRU和ST-ResNet相比,RMSE指标分别减少了17.15%和7.37%,MAE指标分别减少了22.75%和9.14%,R2指标分别提升了11.27%和2.37%。同时,该模型在规律性较高的工作日的预测效果高于周末,且工作日早高峰的预测效果最好,可为短时城市区域交通流量变化监测提供依据。

关键词: 短时交通流量预测, 时空特性, 卷积神经网络, 长短时记忆, 注意力机制

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