Integrated Determination of Network Origin–Destination Trip Matrix and Heterogeneous Sensor Selection and Location Strategy

Abstract

This paper proposes a two-stage optimization model to determine the origin–destination (O–D) trip matrix and the heterogeneous sensor deployment strategy in an integrated manner for a vehicular traffic network using sensor information from active (camera-based license plate recognition) and passive (vehicle detector) sensors. The first stage solves the heterogeneous sensor selection and location problem to determine the optimal sensor deployment strategy, in terms of the selection of the numbers of the two sensor types and their installation locations, to maximize the traffic information available for the O–D matrix estimation problem. The traffic information includes the observed link flow, path trajectory, and path coverage information. The second stage leverages this traffic information to determine the network O–D matrix that minimizes the error between the observed and estimated traffic flows (link, O–D, and/or path). Correspondingly, two network O–D matrix estimation models are proposed where the link-based model incorporates the flow conservation rule between O–D and link flows and uses the link-node incidence matrix, and the path-based model assumes a given link-path incidence matrix. An iterative solution procedure is designed to determine the network O–D matrix and link flow estimates. Results from numerical experiments suggest that the path-based model outperforms the link-based model in the estimation of network O–D matrices. The relative contributions of combinations of the two sensor types to the network O–D matrix estimation problem are also analyzed. They suggest that active sensors provide valuable path information to solve the O–D matrix estimation problem, but at the cost of a significantly higher unit price. The study results have key implications for heterogeneous sensor selection and location strategies.