Urban Road Network Traffic Speed Prediction Model Based on Global Spatio-Temporal Characteristics

doi:10.19678/j.issn.1000-3428.0061397

Abstract

Abstract: Urban road network traffic speed prediction is an important aspect of Intelligent Transportation System(ITS) and is of great significance in improving road capacity by providing real-time traffic information for travelers.The existing prediction model based on the Graph Convolution Network(GCN) strengthens the mining of the spatial correlation degree between first-order adjacent sections to a certain extent.However, if the correlation degree of some non-first-order adjacent sections is greater than that of some first-order adjacent sections, when the original adjacency matrix is input, some relatively important section spatial information may be lost, precluding the realization of better prediction results.To accurately mine the temporal and spatial characteristics of urban road networks, a traffic speed prediction model of an urban road network based on global GCN and Gated Recurrent Unit(GRU), G-GCGRU, is proposed.Considering the degree of spatial influence between non-first-order adjacent road sections under the global road network, the correlation degree matrix between road sections is calculated using the correlation analysis method, which is a new convolution method to further deepen the mining of spatial features.Based on this, the gated cycle unit method is used to extract the time features of the road network.The results show that the performance of the model is better than those of the GCN, Gated Recurrent Unit (GRU), and GCN-GRU hybrid models.Using the root mean square error as the evaluation index, the prediction accuracy is improved by 25.3%, 4.7%, and 2.1%, respectively.

Key words: Intelligent Transportation System(ITS), traffic speed prediction, Graph Convolutional Network(GCN), urban road network, global spatio-temporal characteristics

摘要： 城市路网交通速度预测是智能交通系统中的重要组成部分，其可为出行者提供实时的交通信息，对提升道路通行能力具有重要意义。现有基于图卷积网络的预测模型一定程度上加强了对一阶相邻路段间空间关联程度的挖掘，但在非一阶相邻路段关联度大于一阶相邻路段关联度的情况下，如果仍输入原始的邻接矩阵，会遗失一些相对重要的路段空间信息，无法得到较好的预测结果。为准确挖掘城市路网中的时空特性，提出一种基于全局图卷积和门控循环单元的城市路网交通速度预测模型G-GCGRU。考虑全局路网下非一阶相邻路段间的空间影响程度，利用相关性分析方法计算得到路段间的关联度矩阵，并作为新的卷积方式进一步加深对空间特征的挖掘，在此基础上，采用门控循环单元方法提取路网时间特征。使用深圳市罗湖区城市路网车速数据进行实验，结果表明，该模型预测性能优于图卷积网络（GCN）、门控循环单元（GRU）和GCN-GRU混合模型，以均方根误差为评价指标，预测精度分别提高25.3%、4.7%和2.1%。

关键词: 智能交通系统, 交通速度预测, 图卷积网络, 城市路网, 全局时空特征

CLC Number:

TP18

FENG Siyun, SHI Zhenquan, CAO Yang. Urban Road Network Traffic Speed Prediction Model Based on Global Spatio-Temporal Characteristics[J]. Computer Engineering, 2022, 48(5): 112-117.

冯思芸, 施振佺, 曹阳. 基于全局时空特性的城市路网交通速度预测模型[J]. 计算机工程, 2022, 48(5): 112-117.

/ / Recommend / Download Citations

URL: http://www.ecice06.com/EN/10.19678/j.issn.1000-3428.0061397

http://www.ecice06.com/EN/Y2022/V48/I5/112

Figures/Tables 9

References

[1] ZHU L, YU F R, WANG Y G, et al.Big data analytics in intelligent transportation systems:a survey[J].IEEE Transactions on Intelligent Transportation Systems, 2019, 20(1):383-398.
[2] DO L N N, TAHERIFAR N, VU H L.Survey of neural network-based models for short-term traffic state prediction[J].WIREs Data Mining and Knowledge Discovery, 2019, 9(1):1285-1309.
[3] JO D, YU B, JEON H, et al.Image-to-image learning to predict traffic speeds by considering area-wide spatio-temporal dependencies[J].IEEE Transactions on Vehicular Technology, 2019, 68(2):1188-1197.
[4] MA X L, TAO Z M, WANG Y H, et al.Long short-term memory neural network for traffic speed prediction using remote microwave sensor data[J].Transportation Research Part C:Emerging Technologies, 2015, 54:187-197.
[5] GUO G, YUAN W.Short-term traffic speed forecasting based on graph attention temporal convolutional networks[J].Neurocomputing, 2020, 410:387-393.
[6] 沈琴琴, 王玥, 黄悦, 等.改进初值的灰色Verhulst-Markov模型及其应用[J].统计与决策, 2020, 36(7):30-33. SHEN Q Q, WANG Y, HUANG Y, et al.Grey verhulst-Markov model with improved initial value and its application[J].Statistics & Decision, 2020, 36(7):30-33.(in Chinese)
[7] DE MEDRANO R, AZNARTE J L.A spatio-temporal attention-based spot-forecasting framework for urban traffic prediction[J].Applied Soft Computing, 2020, 96:1-15.
[8] ZHANG Z C, LI M, LIN X, et al.Multistep speed prediction on traffic networks:a deep learning approach considering spatio-temporal dependencies[J].Transportation Research Part C:Emerging Technologies, 2019, 105:297-322.
[9] 杜圣东, 李天瑞, 杨燕, 等.一种基于序列到序列时空注意力学习的交通流预测模型[J].计算机研究与发展, 2020, 57(8):1715-1728. DU S D, LI T R, YANG Y, et al.A sequence-to-sequence spatial-temporal attention learning model for urban traffic flow prediction[J].Journal of Computer Research and Development, 2020, 57(8):1715-1728.(in Chinese)
[10] 闫杨, 孙丽珺, 朱兰婷.基于时空相关性的短时交通流量预测方法[J].计算机工程, 2020, 46(1):31-37. YAN Y, SUN L J, ZHU L T.Short-term traffic flow prediction method based on spatiotemporal relativity[J].Computer Engineering, 2020, 46(1):31-37.(in Chinese)
[11] BOGAERTS T, MASEGOSA A D, ANGARITA-ZAPATA J S, et al.A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data[J].Transportation Research Part C:Emerging Technologies, 2020, 112:62-77.
[12] ZHANG K P, ZHENG L, LIU Z J, et al.A deep learning based multitask model for network-wide traffic speed prediction[J].Neurocomputing, 2020, 396:438-450.
[13] YIN X, WU G, WEI J, et al.Multi-stage attention spatial-temporal graph networks for traffic prediction[J].Neurocomputing, 2021, 428:42-53.
[14] 徐冰冰, 岑科廷, 黄俊杰, 等.图卷积神经网络综述[J].计算机学报, 2020, 43(5):755-780. XU B B, CEN K T, HUANG J J, et al.A survey on graph convolutional neural network[J].Chinese Journal of Computers, 2020, 43(5):755-780.(in Chinese)
[15] BOUKERCHE A, WANG J H.A performance modeling and analysis of a novel vehicular traffic flow prediction system using a hybrid machine learning-based model[J].Ad Hoc Networks, 2020, 106:1-10.
[16] ZHAO L, SONG Y J, ZHANG C, et al.T-GCN:a temporal graph convolutional network for traffic prediction[J].IEEE Transactions on Intelligent Transportation Systems, 2020, 21(9):3848-3858.
[17] CUI Z Y, HENRICKSON K, KE R M, et al.Traffic graph convolutional recurrent neural network:a deep learning framework for network-scale traffic learning and forecasting[J].IEEE Transactions on Intelligent Transportation Systems, 2020, 21(11):4883-4894.
[18] 陈喜群, 周凌霄, 曹震.基于图卷积网络的路网短时交通流预测研究[J].交通运输系统工程与信息, 2020, 20(4):49-55. CHEN X Q, ZHOU L X, CAO Z.Short-term network-wide traffic prediction based on graph convolutional network[J].Journal of Transportation Systems Engineering and Information Technology, 2020, 20(4):49-55.(in Chinese)
[19] YU B, LEE Y J, SOHN K.Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a Graph Convolutional Neural network(GCN)[J].Transportation Research Part C:Emerging Technologies, 2020, 114:189-204.
[20] PAVLYUK D.Feature selection and extraction in spatiotemporal traffic forecasting:a systematic literature review[J].European Transport Research Review, 2019, 11:1-5.
[21] ERMAGUN A, LEVINSON D.Spatiotemporal traffic forecasting:review and proposed directions[J].Transport Reviews, 2018, 38(6):786-814.
[22] 孔繁钰, 周愉峰, 陈纲.基于时空特征挖掘的交通流量预测方法[J].计算机科学, 2019, 46(7):322-326. KONG F Y, ZHOU Y F, CHEN G.Traffic flow prediction method based on spatio-temporal feature mining[J].Computer Science, 2019, 46(7):322-326.(in Chinese)

Please choose a citation manager

Content to export