A Novel Framework for Road Side Unit Location Optimization for Origin-Destination Demand Estimation

This study deals with the problem of road side unit (RSU) location optimization for origin-destination (OD) demand estimation. With the point-to-point measurement provided by RSUs in connected vehicle environment, the errors of OD demand estimation come from two sources: 1) the lack of enough path flow information; and 2) the vehicle-to-RSU (V2R) communication delay. However, increasing the amount of path flow information collected by RSUs results in the increase of V2R communication delay encountered by each collected data packet. Moreover, it is difficult to find a global optimal solution by formulating the problem as a single objective program. To address the investigated problem, this study proposes a novel framework consisting of solving a bi-objective RSU location optimization problem and an OD demand estimation problem. This RSU location optimization problem is formulated as a bi-objective nonlinear binary integer program to balance the maximization of the amount of path flow information and the minimization of V2R communication delay. The OD demand estimation problem is formulated as a least square estimator to identify the RSU location scheme with the smallest OD demand estimation error, among the Pareto optimal solutions to the bi-objective program. An efficient
$\varepsilon $
-constraint method is developed to generate the Pareto optimal solutions. The numerical example demonstrates that the proposed framework achieves 6.95 lower root-mean-square error of OD demand estimation, compared with the baseline framework.