基于两级筛选机制及深度学习组合模型实现短时交通流预测
作者:
作者单位:

长安大学

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中图分类号:

U491.1

基金项目:

长安大学中央高校基本科研业务费专项资金资助(No. 300102321504, 300102321501, 300102321503);西安市智慧高速公路信息融合与控制重点实验室(No. ZD13CG46);陕西省重点研发计划(No. 2021GY-098).


Research on the Combination Model of Short-Term Traffic Flow Prediction Based on Two-Level Screening Mechanism
Author:
Affiliation:

CHANG''AN UNIVERSITY

Fund Project:

Special Funds for Fundamental Research Expenses of Central Universities of Chang"an University(No. 300102321504, 300102321501, 300102321503); Key Laboratory of Information Fusion and Control of Xi"an Smart Expressway(No. ZD13CG46); Shaanxi Province Key Research and Development Plan (No. 2021GY-098).

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    摘要:

    准确实时的短时交通流预测对现代交通管理服务体系的构建至关重要。为了充分挖掘并利用不同路段短时交通流交互作用而表现出的时空特性,构建了由自相关函数、互相关函数和KNN算法组成的两级筛选机制评估与目标路段的相关性优化路段组合,实现空间信息深度挖掘;提出一种GCN-GRU组合预测模型,利用图卷积网络(GCN)全局处理路段拓扑信息的优势进一步捕捉短时交通流的空间特性,并借助门控循环单元(GRU)对时间信息的长时记忆能力以提取其时间特性。利用实测高速公路短时交通流数据进行验证,仿真结果表明,采用两级筛选机制对路段进行有效筛选并引入深度学习组合模型,预测性能明显改善,优于堆栈式自编码网络(SAEs)和GRU等经典模型。

    Abstract:

    Accurate and real-time short-term traffic flow prediction is critical for the construction of modern traffic management service system. In order to fully exploit and utilize the spatial-temporal characteristics of traffic flow interaction in different road sections, a two-level screening mechanism composed of autocorrelation function, cross-correlation function and KNN algorithm is constructed to evaluate the correlation between the target road section and optimize the combination of road sections, and realize deep mining of spatial information. One of the GCN-GRU combination forecasting model is proposed. The spatial characteristics of short-term traffic flow are captured by using the advantage of Graph Convolutional Network (GCN) in the global processing of section topology information, and the time characteristics are extracted by using the long-term memory ability of Gated Recurrent Unit (GRU) for time information. They are verified by the measured short-term traffic flow data of expressway. The results show that using the two-level screening mechanism to effectively screen the road sections and introducing a deep learning combination model, the prediction performance will be significantly improved, which is better than the commonly used models such as Stacked Autoencoders network (SAEs) and Temporal Convolutional Network (TCN).

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历史
  • 收稿日期:2021-05-25
  • 最后修改日期:2021-09-16
  • 录用日期:2021-09-22
  • 在线发布日期: 2021-10-01
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