Abstract
In order to improve the accuracy, reliability, and economy of urban traffic information collection, an optimization model of traffic sensor layout is proposed in this paper. Considering the impact of traffic big data, a set of impact factors for traffic sensor layout is established, including system cost, multisource data sharing, data demand, sensor failures, road infrastructure, and sensor type. The impacts of these influential factors are taken into account in the traffic sensor layout optimization problem, which is formulated in the form of multiobjective programming model that includes minimum system cost, maximum truncation flow, minimum path coverage, and an origin-destination (OD) coverage constraint. The model is solved by the tolerant lexicographic method based on a genetic algorithm. A case study shows that the model reflects the influence of multisource data sharing and fault conditions and satisfies the origin-destination coverage constraint to achieve the multiobjective optimization of traffic sensor layout.
Highlights
With the development of Intelligent Transportation Systems, large-scale acquisition of urban traffic data has become possible by fixed detectors, such as inductive loop detectors [1], microwave radar detectors [2] and video detection technology [3], moving detectors as probe-vehicle systems [4], and new detectors, such as Bei Dou Navigation Satellite systems [5, 6], mobile devices [7, 8], and wireless transmission technology [9]. erefore, traffic sensor is an important part of urban traffic information collection, and its optimal layout is of great significance
With an optimal objective of minimum system cost, maximum truncation flow and minimum path of inclusion, and OD coverage as a constraint, the model was proposed and was solve based on the tolerance lexicographic method of genetic algorithm to demonstrate the validity of the optimization target and the feasibility of the solution, with the classical Nguyen–Dupuis network as a case
E multiobjective optimization model for traffic sensor layout can guarantee the optimal system cost and satisfy the data requirements consisting of OD coverage principle, maximum truncation flow principle, and minimum contained path principle and reduce the duplication of detector layout under multiple source data sharing
Summary
With the development of Intelligent Transportation Systems, large-scale acquisition of urban traffic data has become possible by fixed detectors, such as inductive loop detectors [1], microwave radar detectors [2] and video detection technology [3], moving detectors as probe-vehicle systems [4], and new detectors, such as Bei Dou Navigation Satellite systems [5, 6], mobile devices [7, 8], and wireless transmission technology [9]. erefore, traffic sensor is an important part of urban traffic information collection, and its optimal layout is of great significance. Optimal traffic sensor layout model is mostly based on the theory of intelligent algorithms [10], graph theory [11], and mathematical planning [12], considering various factors. The most studies develop mathematical models to optimize the sensor layout based on road conditions and research objectives. Erefore, the impact analysis on the number and location of traffic detector deployment in the context of traffic big data can be combined to build a deployment optimization model based on the data-driven modeling approach. According to the data characteristics of traffic big data, this paper establishes an evaluation index system for the influencing factors of traffic sensor layout and solves it by tolerance hierarchical sequence method based on genetic algorithm, which verifies the validity of the optimization goal and the feasibility of solution.
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