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.
@article{arxiv.1703.04010,
title = {Data-Driven Estimation of Travel Latency Cost Functions via Inverse Optimization in Multi-Class Transportation Networks},
author = {Jing Zhang and Ioannis Ch. Paschalidis},
journal= {arXiv preprint arXiv:1703.04010},
year = {2017}
}
Comments
Preprint submitted to the 56th IEEE Conference on Decision and Control (2017)