Joint Estimation of OD Demands and Cost Functions in Transportation Networks from Data

TL;DR

This paper introduces a novel joint estimation method for OD demands and nonparametric travel latency functions in transportation networks, using traffic data and bilevel optimization to improve network modeling accuracy.

Contribution

It proposes a nonparametric, joint estimation approach with an iterative optimization algorithm, addressing the limitations of separate, parametric methods.

Findings

Effective recovery of OD demands and latency functions demonstrated on Braess Network

Joint estimation improves model accuracy over separate approaches

Algorithm converges reliably in numerical experiments

Abstract

Existing work has tackled the problem of estimating Origin-Destination (OD) demands and recovering travel latency functions in transportation networks under the Wardropian assumption. The ultimate objective is to derive an accurate predictive model of the network to enable optimization and control. However, these two problems are typically treated separately and estimation is based on parametric models. In this paper, we propose a method to jointly recover nonparametric travel latency cost functions and estimate OD demands using traffic flow data. We formulate the problem as a bilevel optimization problem and develop an iterative first-order optimization algorithm to solve it. A numerical example using the Braess Network is presented to demonstrate the effectiveness of our method.