Long-term traffic status prediction of urban expressway based on spatio-temporal eigenvector

YAO Bofan; DENG Rufeng; XIONG Chen; CAI Ming

doi:10.13471/j.cnki.acta.snus.2019.11.04.2019B111

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Long-term traffic status prediction of urban expressway based on spatio-temporal eigenvector

Research articles | 更新时间：2023-12-08

- Long-term traffic status prediction of urban expressway based on spatio-temporal eigenvector
- Acta Scientiarum Naturalium Universitatis Sunyatseni Vol. 60, Issue 3, Pages: 115-123(2021)
- 作者机构：
  
  1.中山大学智能工程学院，广东深圳 518106
  2.广东省智能交通系统重点实验室，广东广州 510006
  3.佛山交通运行监测中心，广东佛山 528000
- 作者简介：
- 基金信息：
- DOI：10.13471/j.cnki.acta.snus.2019.11.04.2019B111
  CLC： U491
- Published：25 May 2021，
  
  Published Online：10 November 2020，
  
  Received：04 November 2019，
  
  Accepted：03 December 2019
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姚博凡,邓如丰,熊宸等.基于时空特征向量的城市快速路交通状态长时段预测[J].中山大学学报(自然科学版),2021,60(03):115-123.

YAO Bofan,DENG Rufeng,XIONG Chen,et al.Long-term traffic status prediction of urban expressway based on spatio-temporal eigenvector[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2021,60(03):115-123.
姚博凡,邓如丰,熊宸等.基于时空特征向量的城市快速路交通状态长时段预测[J].中山大学学报(自然科学版),2021,60(03):115-123. DOI： 10.13471/j.cnki.acta.snus.2019.11.04.2019B111.

YAO Bofan,DENG Rufeng,XIONG Chen,et al.Long-term traffic status prediction of urban expressway based on spatio-temporal eigenvector[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2021,60(03):115-123. DOI： 10.13471/j.cnki.acta.snus.2019.11.04.2019B111.

摘要

针对交通状态长时段预测问题，提出了基于时空特征向量的长时段交通状态预测模型。该模型在分析交通状态变化时空关联性的基础上，构建了表征交通状态变化规律的时空特征向量，包括时间轴特征、路段历史平均交通状态特征、时间常发拥堵特征和上下游路段的历史交通状态特征。单路段预测实验结果表明，模型预测准确度较高，全天预测准确度在90%左右，高峰预测准确度在80%左右。此外，与单一时间特征相比，提出的时空特征向量能显著提高预测准确度。多路段的实验结果表明，基于时空特征向量的SVM预测方法具有较好的普适性，全天平均预测准确度达到了95%左右，高峰平均预测准确度达到了88%左右。

Abstract

This paper proposes a long-term traffic status prediction model based on a spatio-temporal eigenvector in order to solve the problem of long-term traffic status prediction. Based on the analysis of the spatio-temporal correlation of traffic status changes， the model constructs the spatio-temporal eigenvector that represents the law of traffic status variation， including time axis feature， historical average traffic status feature， recurrent congestion feature and historical average traffic status feature of upstream and downstream roads. The prediction results of the single roads show that the model has a good prediction performance. The accuracy of all-day prediction is about 90% and the accuracy of peak hour prediction is about 80%. Furthermore， compared with single time features， the proposed spatio-temporal eigenvector can significantly improve the prediction accuracy. The prediction results of multiple roads show that the SVM prediction method based on spatio-temporal has a good universality. The average accuracy of allday prediction is about 95% and the average accuracy of peak hour prediction is about 88%.

关键词

长时段预测支持向量机交通状态时空特征向量

Keywords

long-term predictionsupport vector machinetraffic statusspatio-temporal eigenvector

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Related Institution

Key Laboratory of Intelligent Transportation System in Guangdong Province， Sun Yat-sen University

School of Intelligent Systems Engineering / Guangdong Provincial Key Laboratory of Intelligent Transportation System， Sun Yat-sen University

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School of Information Science and Technology, Sun Yatsen University

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