考虑大型车因素的城市路网交通流速度预测

doi:10.3969/j.issn.1674-0696.2021.01.02

重庆交通大学学报（自然科学版） ›› 2021, Vol. 40 ›› Issue (01): 7-11.DOI: 10.3969/j.issn.1674-0696.2021.01.02

• 交通+大数据人工智能 • 上一篇下一篇

考虑大型车因素的城市路网交通流速度预测

闫佳庆1，邵鹏1，张立立2，王力1

(1. 北方工业大学电气与控制工程学院，北京 100144； 2. 北京石油化工学院信息工程学院，北京 102617)

收稿日期:2019-05-23 修回日期:2019-06-24 出版日期:2021-01-11 发布日期:2021-01-11
作者简介:闫佳庆(1985—)，男，河北唐山人，副教授，博士，主要从事计算神经科学方面的研究。E-mail：yjq@ncut.edu.cn
基金资助:
北京市优秀人才培养资助项目(2017000020124G287)；北京市属高校基本科研项目(110052971803/013)

Traffic Flow Velocity Prediction of Urban Road Network Considering Large-Scale Vehicles

YAN Jiaqing1，SHAO Peng1，ZHANG Lili2，WANG Li1

(1.School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China; 2. College of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China)

Received:2019-05-23 Revised:2019-06-24 Online:2021-01-11 Published:2021-01-11
Supported by:

摘要/Abstract

摘要： 针对大型车影响交通流速度问题，提出一种考虑大型车因素的路网交通流速度预测方法。首先根据二流理论将路段分为行驶流和排队流两部分；其次通过对路网路段的时空分析，将路段按旅行时间进行聚类，确定预测时长指数；再根据大型车特征与路段交通流速度的关系，引入大型车因素；最后使用GRU(gated recurrent unit)神经网络实现交通流速度预测，并利用VISSIM仿真进行验证。研究结果表明：考虑大型车因素可提高城市路网道路交通流速度的预测准确度。

关键词: 交通工程, 交通流速度预测, 大型车辆, GRU神经网络

Abstract: Aiming at the problem that large-scale vehicles affect the velocity of traffic flow, a method to predict the traffic flow velocity of road network with the consideration of large-scale vehicles was proposed. Firstly, according to the two-fluid theory, the road section was divided into two parts: driving flow and queuing flow. Then, through the temporal and spatial analysis of road network sections, the road sections were clustered according to travel time and the prediction time index was determined. According to the relationship between the characteristics of large-scale vehicles and the velocity of traffic flow, the factors of large-scale vehicles were introduced. Finally, the traffic flow velocity was predicted by GRU (gated recurrent unit) neural network and verified by VISSIM simulation. The results show that: the prediction accuracy of traffic flow velocity in urban road network can be improved by considering the large-scale vehicles.

Key words: traffic engineering, traffic flow velocity prediction, large-scale vehicles, GRU neural network

中图分类号:

U491

闫佳庆1，邵鹏1，张立立2，王力1. 考虑大型车因素的城市路网交通流速度预测[J]. 重庆交通大学学报（自然科学版）, 2021, 40(01): 7-11.

YAN Jiaqing1，SHAO Peng1，ZHANG Lili2，WANG Li1. Traffic Flow Velocity Prediction of Urban Road Network Considering Large-Scale Vehicles[J]. Journal of Chongqing Jiaotong University(Natural Science), 2021, 40(01): 7-11.

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考虑大型车因素的城市路网交通流速度预测

Traffic Flow Velocity Prediction of Urban Road Network Considering Large-Scale Vehicles

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