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为了提高城市交通信息采集的准确性、可靠性和经济性,提出了一种交通检测器优化布置模型。大数据背景下,考虑系统成本、多源数据共享、数据需求、检测器故障、道路基础设施、检测器类型等因素,构建了交通检测器布置的影响因素集。综合分析各个影响因素,提出了由最小系统成本优化、最大截断流优化、最小包含路径优化和OD(origin-destination)覆盖约束构成的多目标优化模型。应用基于遗传算法的宽容分层序列法,对模型进行求解。算例研究表明:该文的模型实现了多目标的优化,反映了多源数据共享和检测器故障的影响,满足了OD覆盖约束,可达到交通检测器的优化布置。
In order to improve the accuracy, reliability and economy of urban traffic information collection, a traffic distribution optimization model is proposed. Under the background of big data, the set of influencing factors of traffic detector layout is constructed considering such factors as system cost, multi-source data sharing, data requirement, detector failure, road infrastructure and detector type. Based on comprehensive analysis of various influencing factors, a multi-objective optimization model consisting of minimum system cost optimization, maximum cutoff flow optimization, minimum inclusion path optimization and OD (origin-destination) coverage constraints is proposed. The genetic algorithm based tolerance hierarchical sequence method is applied to solve the model. The case study shows that the proposed model achieves multi-objective optimization, which reflects the influence of multi-source data sharing and detector failure, satisfies the OD coverage constraint, and achieves the optimal placement of traffic detectors.