The network sensor location problem (NSLP) for origin–destination (OD) estimation identifies the optimal locations for sensors to estimate the vehicular flow of OD pairs in a road network. Like other measurement apparatuses, these sensors are subject to failure, which can affect the reliability of the OD estimations. In this paper, we propose a novel model that allows us to solve the NSLP for OD demand estimation by identifying the most reliable locations to install sets of sensors with consideration for a nonhomogeneous Poisson process to account for time-dependent sensor failure. The proposed model does not rely on the assumption that true OD demand information is known. We introduce two separate objective functions to minimize the maximum possible information loss (MPIL) associated with OD demand on sensor-equipped links and OD pairs during the lifetimes of the sensors. Both objective functions are formulated to incorporate the possibility of sensor failure into the calculated OD demands. We use stochastic user equilibrium (SUE) to address the stochasticity of traffic route selection. We then employ the weighted sums method (WSM) and an ε-constraint to incorporate the objective functions into an integrated formulation. Two sensor types with different time-dependent failure rates are considered to identify the optimal locations for sets of sensors for OD demand estimation purposes while addressing the available budget constraints. We also address the problem of scheduled/routine maintenance of existing sensors by introducing an additional sensor deployment phase that focuses on maintaining the reliability of information by repairing or replacing failed sensors, installing additional sensors or a combination of both. The numerical results from the proposed model demonstrate how the deployment of more advanced sensors with lower failure rates can effectively improve the reliability of the information obtained from sensors. We also evaluate the use of different weights for the WSM’s objective functions to explore alternative combinations of sensor configurations. The introduction of additional sensors to a network shows that the decision between repairing failed sensors and installing new sensors is highly dependent on the available budget and the failed sensors’ locations.

用于起点-终点 (OD) 估计的网络传感器位置问题 (NSLP) 确定传感器的最佳位置，以估计道路网络中 OD 对的车辆流量。像其他测量设备一样，这些传感器容易出现故障，这会影响 OD 估计的可靠性。在本文中，我们提出了一种新模型，该模型允许我们通过确定安装传感器组的最可靠位置来解决 NSLP 以进行 OD 需求估计，同时考虑非齐次泊松过程以解决与时间相关的传感器故障。所提出的模型不依赖于真实 OD 需求信息已知的假设。我们引入了两个单独的目标函数，以在传感器的使用寿命期间最大限度地减少与配备传感器的链路和 OD 对的 OD 需求相关的最大可能信息丢失 (MPIL)。这两个目标函数都经过公式化，以将传感器故障的可能性纳入计算的 OD 需求中。我们使用随机用户均衡（SUE）来解决交通路线选择的随机性。然后，我们采用加权和方法 (WSM) 和 ε 约束将目标函数合并到一个集成公式中。考虑了具有不同时间相关故障率的两种传感器类型，以确定用于 OD 需求估计目的的传感器组的最佳位置，同时解决可用的预算限制。我们还通过引入额外的传感器部署阶段来解决现有传感器的计划/日常维护问题，该阶段侧重于通过修复或更换故障传感器、安装额外传感器或两者的组合来维护信息的可靠性。所提出模型的数值结果表明，部署具有更低故障率的更先进的传感器如何有效提高从传感器获得的信息的可靠性。我们还评估了 WSM 目标函数的不同权重的使用，以探索传感器配置的替代组合。在网络中引入额外的传感器表明，在修复故障传感器和安装新传感器之间做出的决定在很大程度上取决于可用预算和故障传感器的位置。