Abstract：An optimal traffic sensor layout model was developed to improve the accuracy, reliability and economy of urban traffic information collection. The traffic sensor layout was optimized in light of big data traffic information with the system optimized with consideration of the system cost, multi-source data sharing, data demand, fault conditions, road infrastructure, and different types of sensors. The impact of these influential factors was taken into account in a multi-objective programming model that included system cost minimization, traffic flow intercept maximization, path coverage minimization, and an origin-destination(OD) coverage constraint. The model was solved by the tolerant lexicographic method based on a genetic algorithm. A case study shows that the model provides multi-objective optimization, reflects the influence of multi-source data sharing and fault conditions, satisfies the origin-destination coverage constraint, and provides the optimal traffic sensor layout.
孙智源, 陆化普. 考虑交通大数据的交通检测器优化布置模型[J]. 清华大学学报（自然科学版）, 2016, 56(7): 743-750.
SUN Zhiyuan, LU Huapu. Optimal traffic sensor layout model considering traffic big data. Journal of Tsinghua University(Science and Technology), 2016, 56(7): 743-750.
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2016, Vol. 56 
