This study addresses the problem of road side unit (RSU) location optimization for optimum link flow determination. The error of link flow determination using RSU information comes from two sources: measurement error and inference error. The inference error is caused by the propagation and accumulation of measurement error during the link flow inference process. The direct problem formulation aiming to minimize the total error is impossible because the minimization of inference error has to be indirectly formulated as the minimization of the cumulative number of unobserved links. Further, for a given number of RSU, the decrease in the cumulative number of unobserved links results in the increase of the number of observed links, and thus results in the increase of measurement error due to data packet queueing delay. Therefore, for a given RSU installation budget, the balance between the two types of error needs to be optimized in order to achieve the optimum link flow determination accuracy. To fulfill this goal, the RSU location optimization problem is formulated as a bi‐objective nonlinear binary integer programming. This programming is constrained by complete link flow determination requirements. In order to accelerate computation, an efficient ε‐constraint method is designed to generate Pareto optimal frontier. The subproblem solved at each iteration is linearized using piecewise linear approximation and solved using a constraint generation method. The proposed model and the solution algorithm are evaluated through numerical examples. The results reveal that the Pareto optimal solutions achieved by the proposed model are at least not inferior to a non‐Pareto solution obtained in the baseline scenario. Further, both the measurement error and inference error associated with some of the Pareto optimal points are lower than those associated with the non‐Pareto optimal solution.

中文翻译：

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路侧单元位置优化，以最佳确定链路流量

这项研究解决了路边单元（RSU）位置优化的问题，以优化链路流量确定。使用RSU信息确定链路流量的错误来自两个来源：测量错误和推断错误。推断误差是由链路流推断过程中测量误差的传播和累积引起的。旨在使总误差最小的直接问题公式化是不可能的，因为必须将推理误差的最小化间接地表示为未观察链路的累积数量的最小化。此外，对于给定数量的RSU，未观察到的链路的累积数量的减少导致所观察到的链路的数量增加，因此由于数据分组排队延迟而导致测量误差的增加。所以，对于给定的RSU安装预算，需要优化两种错误之间的平衡，以实现最佳的链路流量确定精度。为了实现此目标，RSU位置优化问题被表述为双目标非线性二进制整数规划。此编程受完整的链路流确定要求约束。为了加快计算速度，ε约束方法旨在生成帕累托最优边界。使用分段线性逼近对每次迭代求解的子问题进行线性化，并使用约束生成方法进行求解。通过数值算例对提出的模型和求解算法进行了评估。结果表明，所提出的模型获得的帕累托最优解至少不逊于基准情景中获得的非帕累托解。此外，与某些帕累托最优点相关的测量误差和推断误差均低于与非帕累托最优解相关的测量误差和推断误差。