Arc travel time and path choice model estimation subsumed

TL;DR

This paper introduces a maximum likelihood estimation method that simultaneously estimates arc travel times and route choice model parameters, improving accuracy by accounting for their interdependence and handling diverse data granularities.

Contribution

It presents a novel joint estimation approach for arc travel times and route choice models, addressing a gap where these tasks were previously handled separately.

Findings

Method performs well on real NYC taxi data

Outperforms a benchmark focused only on arc travel time estimation

Handles noisy and partial path data effectively

Abstract

We address the problem of simultaneously estimating arc travel times in a network \emph{and} parameters of route choice models for strategic and tactical network planning purposes. Hitherto, these interdependent tasks have been approached separately in the literature on road traffic networks. We illustrate that ignoring this interdependence can lead to erroneous route choice model parameter estimates. We propose a method for maximum likelihood estimation to solve the simultaneous estimation problem that is applicable to any differentiable route choice model. Moreover, our approach allows to naturally mix observations at varying levels of granularity, including noisy or partial path data. Numerical results based on real taxi data from New York City show strong performance of our method, even in comparison to a benchmark method focused solely on arc travel time estimation.