基于全局时空特性的城市路网交通速度预测模型

doi:10.19678/j.issn.1000-3428.0061397

计算机工程 ›› 2022, Vol. 48 ›› Issue (5): 112-117. doi: 10.19678/j.issn.1000-3428.0061397

基于全局时空特性的城市路网交通速度预测模型

冯思芸¹, 施振佺^1,2, 曹阳^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

收稿日期:2021-04-24 修回日期:2021-06-20 发布日期:2021-06-02
作者简介:冯思芸(1995—),女,硕士研究生,主研方向为智能信息处理;施振佺(通信作者),博士;曹阳,教授、博士、博士生导师。
基金资助:
国家自然科学基金面上项目“面向流量预测的交通智脑关键技术研究”（61771265）；江苏省“333工程”科研项目“基于多源异构交通大数据的交通流量预测研究”（BRA2017475）；江苏省“青蓝工程”项目；南通市“226”科研项目“基于时空融合的灰色多变量城市路网短时交通流预测研究”（131320633045）。

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

FENG Siyun¹, SHI Zhenquan^1,2, CAO Yang^1,2

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

Received:2021-04-24 Revised:2021-06-20 Published:2021-06-02

摘要/Abstract

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

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

中图分类号:

TP18

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.

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.

https://www.ecice06.com/CN/Y2022/V48/I5/112

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