Arc travel time and path choice model estimation subsumed
This addresses the need for more accurate strategic and tactical network planning in transportation by integrating previously separate estimation tasks.
The paper tackles the problem of simultaneously estimating arc travel times and route choice model parameters in road traffic networks, showing that ignoring their interdependence leads to errors, and demonstrates strong performance using real taxi data from New York City.
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.