A perturbed utility route choice model

TL;DR

This paper introduces a novel route choice model that accounts for route overlap and can be efficiently estimated and predicted using convex optimization, validated on a large GPS dataset from Copenhagen.

Contribution

It presents a perturbed utility model that estimates route choices without choice set generation, enabling fast and scalable predictions for large networks.

Findings

Model accurately predicts traveler route choices.

Estimation is computationally efficient and scalable.

Validated on over 1.3 million GPS traces.

Abstract

We propose a route choice model in which traveler behavior is represented as a utility maximizing assignment of flow across an entire network under a flow conservation constraint}. Substitution between routes depends on how much they overlap. {\tr The model is estimated considering the full set of route alternatives, and no choice set generation is required. Nevertheless, estimation requires only linear regression and is very fast. Predictions from the model can be computed using convex optimization, and computation is straightforward even for large networks. We estimate and validate the model using a large dataset comprising 1,337,096 GPS traces of trips in the Greater Copenhagen road network.