Estimating the perturbed utility route choice model with trip-level data

2026-08-11

AI Summary

This study addresses the challenge of estimating perturbed-utility route choice models using individual-level travel data by proposing a novel nested fixed-point algorithm. The approach employs bias-corrected linear regression at the upper level and solves individual perturbed utility maximization problems at the lower level. This method constitutes the first identifiable estimation framework for such microeconomic models and establishes a corresponding theory for statistical inference. Simulation experiments confirm the consistency and asymptotic normality of the proposed estimator, while its effectiveness and practical applicability are demonstrated through successful implementation on large-scale real-world travel data.

Abstract

We provide an estimator for the perturbed utility route choice (PURC) model that works with data at the level of individual trips. The estimator is a nested fixed-point algorithm that combines an upper bias-corrected linear regression problem with a lower individual-level perturbed utility maximization problem. We establish the statistical properties of the microPURC estimator and confirm these results with an experiment using simulated data. Finally, we demonstrate the estimator in practice using a large real-world dataset.

Problem

• perturbed utility
• route choice
• trip-level data
• estimation
• microPURC

Innovation

• perturbed utility
• route choice model
• trip-level data
• nested fixed-point algorithm
• bias-corrected regression