Data-Driven Estimation of Travel Latency Cost Functions via Inverse Optimization in Multi-Class Transportation Networks

TL;DR

This paper presents a data-driven method to estimate travel latency cost functions in multi-class transportation networks, effectively handling different vehicle types through inverse variational inequalities, validated by extensive numerical experiments.

Contribution

Introduces a novel inverse variational inequality-based approach for estimating travel latency functions in multi-class networks, accommodating diverse vehicle types.

Findings

Effective estimation of latency functions demonstrated on benchmark networks

Approach is efficient for networks of various sizes

Method outperforms existing techniques in accuracy

Abstract

We develop a method to estimate from data travel latency cost functions in multi-class transportation networks, which accommodate different types of vehicles with very different characteristics (e.g., cars and trucks). Leveraging our earlier work on inverse variational inequalities, we develop a data-driven approach to estimate the travel latency cost functions. Extensive numerical experiments using benchmark networks, ranging from moderate-sized to large-sized, demonstrate the effectiveness and efficiency of our approach.