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运筹学学报 >

2023 , Vol. 27 >Issue 2: 27 - 48

DOI: https://doi.org/10.15960/j.cnki.issn.1007-6093.2023.02.002

 

城市交通流量估计的运筹学方法

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  • 1. 中国矿业大学数学学院, 江苏徐州 221116
邵虎, E-mail: shaohu@cumt.edu.cn

收稿日期: 2022-10-25

  网络出版日期: 2023-06-13

基金资助

国家自然科学基金(72071202)

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Operational research methods for urban traffic flow estimation

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  • 1. School of Mathematics, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China

Received date: 2022-10-25

  Online published: 2023-06-13

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摘要

随着社会经济的发展和人类生产方式的进步, 交通管理系统为运筹学提供一系列研究课题。运筹学方法在交通网络建模领域有着广泛应用, 其在智能交通管理系统中亦占有重要位置。充分应用运筹学的各个分支方法去解决交通系统中存在的问题, 可有效保障生活中交通的高效、有序运行。本文首先介绍交通问题中一些基本概念和交通流量问题的若干基本模型, 然后从线性规划、整数规划、动态规划、图论、统计方法、启发式方法和机器学习七个方面综述现有的相关研究成果。最后, 探讨了交通流量模型及相关问题的发展方向, 提出其尚需研究和解决的问题, 以期为交通运输管理者与研究者提供更多参考。

关键词： 流量估计与预测; 规划论; 图论; 统计学; 机器学习

本文引用格式

邵虎, 卓越, 刘鹏杰, 邵枫 . 城市交通流量估计的运筹学方法[J]. 运筹学学报, 2023 , 27(2) : 27 -48 . DOI: 10.15960/j.cnki.issn.1007-6093.2023.02.002

Abstract

With the development of the social economy and the progress of human production mode, the traffic management system provides a series of subjects for operations research. The operational research methods are widely applied in the field of traffic network modeling, and they also occupy some important positions in the intelligent traffic management system. To solve the problems existing in the traffic system, we can make full use of various branches of operations research, which can effectively ensure the efficiency and orderliness of transportation in real life. In this paper, we first introduce several solution models for solving traffic flow estimation problems and then review the existing research from seven aspects: linear programming, integer programming, dynamic programming, graph theory, statistical, heuristic approach, and machine learning method. Finally, to provide more references for transportation managers and researchers, we discuss the development directions and related problems for traffic flow estimation models and propose the potential problems that need to be further investigated and solved.

Key words： traffic estimation and prediction; programming theory; graph theory; statistics; machine learning

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