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.

I. Introduction

Sensor location optimization for Origin-destination (OD) demand estimation is crucial for cost-effective OD demand estimation. Traditional ways to obtain OD demand of a transportation network, such as roadside interviews and postcard surveys, are costly, time consuming, and labor intensive. Sensors, such as loop detectors and Automatic Vehicle Identifications (AVIs), located in a transportation network is able to collect link flow information or path flow information. This provides a cost-effective way to obtain OD demand and motivates the development of some effective OD demand estimation method, such as entropy maximization method [1], generalized least squares method [2] and deep learning method [3]. Sensor location significantly affects the accuracy of OD demand estimation from two ways: 1) sensor location affects the quality of the solution to an OD demand estimation model, given that the input data is correctly observed; 2) different sensor location decisions usually associate with different errors of the input data. To reduce the errors caused by the first factor, Yang et al. proposed the concept of “Maximum Possible Relative Error” to evaluate the quality of the solution to an OD demand estimation model [4]. Built on this concept, rules and corresponding models for OD-demand-estimation-oriented sensor location optimization are proposed in the literatures [5], [6], [7]. To reduce the errors induced by the second factor, OD-demand-estimation-oriented sensor location models are developed to minimize the input data errors, such as the error of priori route flow information [8], [9], the error of prior OD demand [10], and the measurement error of collected data [11].