Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (7): 2274-2280.DOI: 10.11772/j.issn.1001-9081.2021050838

• Frontier and comprehensive applications • Previous Articles    

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

Haiqi WANG, Zhihai WANG(), Liuke LI, Haoran KONG, Qiong WANG, Jianbo XU   

  1. College of Oceanography and Space Informatics,China University of Petroleum (East China),Qingdao Shandong 266580,China
  • Received:2021-05-21 Revised:2021-09-15 Accepted:2021-09-22 Online:2021-09-15 Published:2022-07-10
  • Contact: Zhihai WANG
  • About author:WANG Haiqi, born in 1972, Ph. D., associate professor. His research interests include geographic information, machine learning, spatial/spatiotemporal statistical analysis.
    LI Liuke, born in 1997, M. S. candidate. Her research interests include spatiotemporal big data mining, geographic knowledge graph.
    KONG Haoran, born in 1997, M. S. candidate. His research interests include geographic named entity recognition.
    WANG Qiong, born in 1999, M. S. candidate. His research interests include spatial-temporal big data mining.
    XU Jianbo, born in 1994, M. S. candidate. His research interests include spatial-temporal distribution characteristics of cities based on specific themes.
  • Supported by:
    National Natural Science Foundation of China(62071492);Natural Science Foundation of Shandong Province(ZR202102180193)

基于网格划分的城市短时交通流量时空预测模型

王海起, 王志海(), 李留珂, 孔浩然, 王琼, 徐建波   

  1. 中国石油大学(华东) 海洋与空间信息学院,山东 青岛 266580
  • 通讯作者: 王志海
  • 作者简介:王海起(1972—),男,河南南阳人,副教授,博士,主要研究方向:地理信息、机器学习、空间和时空统计分析
    李留珂(1997—),女,河南商丘人,硕士研究生,主要研究方向:时空大数据挖掘、地理知识图谱
    孔浩然(1997—),男,山东济宁人,硕士研究生,主要研究方向:地理命名实体识别
    王琼(1999—),男,安徽宿州人,硕士研究生,主要研究方向:时空大数据挖掘
    徐建波(1994—),男,江苏盐城人,硕士研究生,主要研究方向:基于特定主题的城市时空分布特征。
  • 基金资助:
    国家自然科学基金资助项目(62071492);山东省自然科学基金资助项目(ZR202102180193)

Abstract:

Accurate traffic flow prediction is very important in helping traffic management departments to take effective traffic control and guidance measures and travelers to plan routes reasonably. Aiming at the problem that the traditional deep learning models do not fully consider the spatial-temporal characteristics of traffic data, a CNN-LSTM prediction model based on attention mechanism, namely STCAL (Spatial-Temporal Convolutional Attention-LSTM network), was established under the theoretical frameworks of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) unit and with the combination of the spatial-temporal characteristics of urban traffic flow. Firstly, the fine-grained grid division method was used to construct the spatial-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 the prediction of traffic flow was realized. Experimental results show that compared with Gated Recurrent Unit (GRU) and Spatio-Temporal Residual Network (ST-ResNet), STCAL model has the Root Mean Square Error (RMSE) index reduced by 17.15% and 7.37% respectively, the Mean Absolute Error (MAE) index reduced by 22.75% and 9.14% respectively, and the coefficient of determination (R2) index increased by 11.27% and 2.37% respectively. At the same time, it is found that the proposed model has the prediction effect on weekdays with high regularity higher than that on weekends, and has the best prediction effect of morning peak on weekdays, showing that it 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以及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(LSTM)单元的理论框架下,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的时空矩阵;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量的时序特征和趋势变动性,并实现交通流量的预测。实验结果表明,STCAL模型与循环门单元(GRU)和时空残差网络(ST-ResNet)相比,均方根误差(RMSE)指标分别减小了17.15%和7.37%,均绝对误差(MAE)指标分别减小了22.75%和9.14%,决定系数(R2)指标分别提升了11.27%和2.37%。同时,发现该模型在规律性较高的工作日的预测效果好于周末,且对工作日早高峰的预测效果最好,可见该模型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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