基于多模型融合的车辆通过时间预测

doi:10.3969/j.issn.1006-2475.2019.02.012

计算机与现代化

基于多模型融合的车辆通过时间预测

(江苏科技大学经济管理学院，江苏镇江212003)

收稿日期:2018-08-04 出版日期:2019-02-25 发布日期:2019-02-26
作者简介:刘银萍（1991-），女，河南商丘人，硕士研究生，研究方向：交通预测,E-mail: 1058550610@qq.com; 马少辉（1972-），男，河北承德人，教授，研究方向：大数据与商务智能,E-mail: msh@tju.edu.cn。
基金资助:
国家自然科学基金资助项目(71571089)

Vehicle Transit Time Prediction Based on Multi-model Fusion

(School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang 212003, China)

Received:2018-08-04 Online:2019-02-25 Published:2019-02-26

摘要/Abstract

摘要： 车辆通过某一路网的时间是测算交通拥堵程度的重要指标。为提高车辆通过时间的预测精度，不仅要考虑数据采集精度的影响还要考虑模型的选择。本文提出多模型融合的车辆通过时间预测方法，发现多模型融合的预测精度较高。以某高速公路3个交叉口路段的车辆通行监测数据作为实证数据，用模型融合算法与单一模型进行对比，说明多模型融合算法在交通拥堵治理领域的应用潜力。

关键词: 多模型融合, 车辆通过时间, 交通拥堵

Abstract: The time that a vehicle passes through a certain road network is one of an important indicator to measure traffic congestion degree. In order to improve the prediction accuracy of vehicle transit time, it is necessary to consider not only the influence of data acquisition accuracy but also the choice of model. This paper proposes a method of the multi-model fusion to predict vehicle transit time, and it is found that the multi-model fusion has higher prediction accuracy. Vehicle traffic monitoring data of three intersections of a highway is empirical data. Comparing the multi-model fusion algorithm with a single model, it shows the application potential of multi-model fusion algorithm in the field of traffic congestion management.

Key words: multi-model fusion, vehicle transit time, traffic congestion

中图分类号:

TP391

刘银萍，马少辉. 基于多模型融合的车辆通过时间预测[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2019.02.012.

LIU Yin-ping, MA Shao-hui. Vehicle Transit Time Prediction Based on Multi-model Fusion[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2019.02.012.

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基于多模型融合的车辆通过时间预测

Vehicle Transit Time Prediction Based on Multi-model Fusion

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