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The sensor location problem: methodological approach and application

机译：传感器位置问题：方法论与应用

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The sensor location problem is of particular importance when planning the allocation of limited field equipment intended to be used for advanced traffic management systems and traveller information services. The locations within a network that satisfy specific goals need to be carefully selected, based on predefined goals related to the effective collection of data and the subsequent estimation of traffic related information. The detection of traffic volumes is mainly associated with two purposes, the travel time and the Origin–Destination (O–D) trip matrix estimation. In this context, this paper presents a quadratic programing model, able to determine the optimal location of tracking sensors. The model is implemented in the urban road network of the city of Thessaloniki (Greece) in which specific number of sensors is installed and utilized for real-time travel time information provision. The proposed methodology models the sensor location problem under the general framework of a set covering problem, which is one of the most popular optimization problems and has been applied in many industrial problems. The results of the case study in Thessaloniki reveal that the proposed model defines the optimal location of the limited number of sensors in such a way that the network, which is created having all sensors as origin or destination of all possible paths, represents to great extent (87% of the traffic flow along the major paths) the traffic volumes of the whole road network of the city. First published online: 11 Jan 2017.
机译：当计划分配用于高级交通管理系统和旅行者信息服务的有限现场设备时，传感器位置问题特别重要。需要根据与数据的有效收集和流量相关信息的后续估计有关的预定义目标，仔细选择网络中满足特定目标的位置。交通量的检测主要与两个目的相关，旅行时间和始发地-目的地（O-D）旅行矩阵估计。在这种情况下，本文提出了一种二次编程模型，能够确定跟踪传感器的最佳位置。该模型在塞萨洛尼基（希腊）的城市道路网中实现，在该网络中，已安装特定数量的传感器，并将其用于实时旅行时间信息提供。所提出的方法在集合覆盖问题的一般框架下对传感器位置问题进行建模，该问题是最流行的优化问题之一，并且已在许多工业问题中得到应用。塞萨洛尼基的案例研究结果表明，所提出的模型以这样一种方式定义了有限数量的传感器的最佳位置，即以所有传感器作为所有可能路径的起点或终点的网络在很大程度上代表了该网络。 （沿着主要路径的交通量的87％）是城市整个道路网的交通量。首次在线发布：2017年1月11日。

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