Data-Driven Estimation of Travel Latency Cost Functions via Inverse Optimization in Multi-Class Transportation Networks
For transportation planners, this provides a practical tool to estimate travel time functions from data, enabling better traffic modeling and management.
The paper develops a data-driven method to estimate travel latency cost functions for multi-class transportation networks (e.g., cars and trucks) using inverse variational inequalities, demonstrating effectiveness and efficiency on benchmark networks of various sizes.
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.