城市交通流量估计的运筹学方法

doi:10.15960/j.cnki.issn.1007-6093.2023.02.002

Abstract

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

CLC Number:

O221.2

Hu SHAO, Yue ZHUO, Pengjie LIU, Feng SHAO. Operational research methods for urban traffic flow estimation[J]. Operations Research Transactions, 2023, 27(2): 27-48.

Figures/Tables 4

References 95

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	流量可观测问题	流量估计问题	流量预测问题
线性规划	代数性质^{[22, 24, 25, 26, 27, 28]}
	矩阵变换^{[29, 30, 31, 32]}
整数规划	随机规划^[33]
	二元整数规划^[35]
图论方法	图的多种类型^{[44, 45, 46]}	子网络拓扑^[51]	网络拓扑^{[52, 53]}
统计方法	贝叶斯估计^[56]	贝叶斯估计^{[54, 55]}
		正态分布^{[57, 58, 59, 60, 61]}
		方差、协方差^{[62, 63, 64, 65, 66, 67]}
启发式方法	启发式算法^{[71, 73, 74, 75]}
	遗传算法^{[77, 78]}
机器学习		神经网络^{[79, 80, 82, 83, 84, 85]}
		向量机^{[86, 87, 88]}
		数据分析^{[93, 94]}	$ K $近邻^[91]

作者	使用方法	解决的问题
Castillo E	代数技术、矩阵变换	流量可观测问题^{[23, 24, 25, 26]}
	贝叶斯统计	流量估计问题^{[55, 56]}
Bianco L	图论、启发式算法	流量可观测问题^{[44, 73, 74]}
Ng M W	矩阵变换	流量可观测问题^{[29, 30]}
Shao H	正态分布、方差、协方差	流量估计问题^{[57, 58, 65, 66]}
Lo H K	神经网络	流量可观测、估计问题^{[79, 80]}
Lam W H K	正态分布、启发式算法	流量可观测、估计问题^{[59, 71]}

问题种类	求解方法	优缺点对比
流量可观测问题	线性规划	优点: 多使用代数性质求解, 借助矩阵工具, 方法简单
		缺点: 结果不精确, 只能得到一个大概的上下界
	整数规划	优点: 可考虑多种路况, 能较好得使用路径流量等多源信息, 减小求解误差
		缺点: 需要一些先验条件, 对源数据精确度要求高
	图论法	优点: 充分考虑交通网络的拓扑情况
		缺点: 结果不精确, 只能得到一个大概的上下界
	启发式算法	优点: 针对NP难问题, 得到结果较为精确
		缺点: 在一些特殊情况下不适用, 求解速度较慢
	遗传算法	优点: 使用网络中多种流量信息, 保证模型在大型网络上的可行性
		缺点: 收敛速度慢, 控制变量多, 编码较为复杂
流量估计问题	贝叶斯估计	优点: 多用于估计OD矩阵, 使用先验流量信息, 可最大化减小方差
		缺点: 需要先验概率, 属性之间相互独立的假设往往不成立
	最小二乘法	优点: 对网络中路径流量和OD信息进行很好的估计
		缺点: 对一些情况不适用, 局限性较大
	正态分布	优点: 对路段或路径的出行时间、流量的方差和协方差等信息都有很好的研究
		缺点: 结果不精确
流量预测问题	神经网络	优点: 能更好的捕捉时空特征和解释变量之间的相关性, 可对网络上动态流量较好的预测
		缺点: 计算代价高, 需要大量参数
	支持向量机方法	优点: 代替神经网络在后处理中的作用, 提高预测准确性
		缺点: 对大规模数据难以计算

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Operational research methods for urban traffic flow estimation

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