Improved spatio-temporal residual convolutional neural network for urban road network short-term traffic flow prediction

doi:10.11772/j.issn.1001-9081.2021010080

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (1): 258-264.DOI: 10.11772/j.issn.1001-9081.2021010080

• Frontier and comprehensive applications • Previous Articles

Improved spatio-temporal residual convolutional neural network for urban road network short-term traffic flow prediction

Yinxin BAO¹, Yang CAO¹^,², Quan SHI¹^,²()

^1.School of Information Science and Technology，Nantong University，Nantong Jiangsu 226019，China
^2.College of Transportation and Civil Engineering，Nantong University，Nantong Jiangsu 226019，China

Received:2021-01-15 Revised:2021-04-20 Accepted:2021-04-29 Online:2021-05-12 Published:2022-01-10
Contact: Quan SHI
About author:BAO Yinxin， born in 1996， Ph. D. candidate. His research interests include intelligent information processing.
CAO Yang， born in 1986， Ph. D.， professor. His research interests include scientific computing， intelligent information processing.
SHI Quan， born in 1973， Ph. D.， professor. His research interests include intelligent information processing.
Supported by:
National Natural Science Foundation of China(61771265);“Project 333” Scientific Research Project of Jiangsu(BRA2017475);“226” Scientific Research Project of Nantong(131320633045)

基于改进时空残差卷积神经网络的城市路网短时交通流预测

包银鑫¹, 曹阳¹^,², 施佺¹^,²()

^1.南通大学信息科学技术学院，江苏南通 226019
^2.南通大学交通与土木工程学院，江苏南通 226019

通讯作者: 施佺
作者简介:包银鑫（1996—），男，江苏淮安人，博士研究生，主要研究方向：智能信息处理
曹阳（1986—），江苏如皋人，教授，博士生导师，博士，主要研究方向：科学计算、智能信息处理
施佺（1973—），男，江苏海门人，教授，博士生导师，博士，CCF高级会员，主要研究方向：智能信息处理。
基金资助:
国家自然科学基金资助项目(61771265);江苏省“333工程”科研项目(BRA2017475);南通市“226”科研项目(131320633045)

Abstract

Abstract:

Traffic flow prediction for urban road network is influenced by historical traffic flow and traffic flow at adjacent intersections， which has complex spatio-temporal correlation. For the lack of correlation analysis on traffic flow data， capturing small changes but ignoring long-term time characteristics in traditional spatio-temporal residual models， a short-term traffic flow prediction model for urban road network based on improved spatio-temporal residual Convolutional Neural Network （CNN） was proposed. In the proposed model， the original traffic flow data was transformed into traffic grid data， and Pearson Correlation Coefficient （PCC） was used to analyze the correlation of traffic grid data， so as to determine the periodic series and adjacent series with high correlation. At the same time， the periodic series model and the adjacent series model were established， and Long Short-Term Memory （LSTM） network was introduced as the hybrid model to extract the time characteristics and capture the long-term time characteristics of the two series. Experimental results on Chengdu taxi dataset show that the proposed model can predict traffic flow better than benchmark models of LSTM， CNN and the traditional residual model. When the evaluation index is Root Mean Square Error （RMSE）， the average prediction accuracy of traffic road network in the test set is improved by 25.6%， 13.3% and 3.2% respectively.

Key words: short-term traffic flow prediction, spatio-temporal analysis, Residual Network (ResNet), Pearson Correlation Coefficient (PCC), Long Short-Term Memory (LSTM) network, Convolutional Neural Network (CNN), combination model

摘要：

城市路网交通流预测受到历史交通流和相邻路口交通流的影响，具有复杂的时空关联性。针对传统时空残差模型缺乏对交通流数据进行相关性分析、捕获微小变化而容易忽略长期时间特征等问题，提出一种基于改进时空残差卷积神经网络（CNN）的城市路网短时交通流预测模型。该模型将原始交通流数据转化成交通栅格数据，利用皮尔逊相关系数（PCC）对交通栅格数据进行相关性分析，确定相关性高的周期序列和邻近序列；同时，建立周期序列模型和邻近序列模型，并引入长短时记忆（LSTM）网络作为混合模型提取时间特征以及捕获两种序列的长期时间特征。利用成都市出租车数据集对模型进行验证，结果表明该模型预测结果优于LSTM、CNN和传统残差模型等基准模型，以均方根误差（RMSE）为评价指标时，所提模型将测试集中交通路网的平均预测精度分别提高了25.6%、13.3%和3.2%。

关键词: 短时交通流预测, 时空分析, 残差网络, 皮尔逊相关系数, 长短时记忆网络, 卷积神经网络, 组合模型

CLC Number:

TP18

Yinxin BAO, Yang CAO, Quan SHI. Improved spatio-temporal residual convolutional neural network for urban road network short-term traffic flow prediction[J]. Journal of Computer Applications, 2022, 42(1): 258-264.

包银鑫, 曹阳, 施佺. 基于改进时空残差卷积神经网络的城市路网短时交通流预测[J]. 《计算机应用》唯一官方网站, 2022, 42(1): 258-264.

Figures/Tables 16

Fig. 1 Traffic grid data transformation

Fig. 2 Structure of traditional spatio-temporal residual model

Fig. 3 Schematic diagram of 2D convolution

Fig. 4 Structure of residual module

Fig. 5 Structure of improved spatio-temporal residual convolutional neural network

Fig. 6 Structure of LSTM network

Fig. 7 Periodic changes in traffic flow

Tab. 1 Dataset parameters

数据集	成都市出租车数据
地点	中国四川省成都市
时间	2014年8月3日到2014年8月30日
栅格面积	648 km²
区域经度（最小值）	103.945 689
区域经度（最大值）	104.204 976
区域纬度（最小值）	30.786 707
区域纬度（最大值）	30.585 958
时间间隔	5 min
栅格数据尺寸	24×24
栅格数据数量	6 048

Fig. 8 Correlation analysis of traffic grid data

Tab. 2 Structural parameters of improved spatio-temporal residual convolutional neural network model

参数	取值
输入尺寸	［BATCH_SIZE，1，24，24］
残差单元数量	3
卷积核尺寸	3×3
卷积核步长	1
卷积补零圈数	1
LSTM输入层维度	1×576
LSTM隐藏层层数	12
LSTM输出层维度	1×576
激活函数	残差单元：ReLU，其余为Sigmoid

Fig. 9 Comparison of real and predicted traffic flow values at intersection x7,3 on August 26， 2014

Fig. 10 Real value and predicted value of traffic flow in urban road network

Tab. 3 Model performance comparison results

模型类别	成都市出租车数据（测试集）
模型类别	RMSE（平均）	MAE（平均）
LSTM（路网）	9.282	5.444
CNN（路网）	7.972	3.588
ST-ResNet（路网）	7.139	4.188
改进ST-ResNet（路网）	6.909	3.110

Fig. 11 Comparison of model prediction data fitting

Fig. 12 Prediction effect comparison between the proposed model and traditional residual model

Fig. 13 RMSE and MAE comparison results on test set

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Improved spatio-temporal residual convolutional neural network for urban road network short-term traffic flow prediction

基于改进时空残差卷积神经网络的城市路网短时交通流预测

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