🤖 AI Summary
Arc travel time estimation and path choice model parameter estimation in road transportation networks are inherently interdependent, yet traditionally addressed separately, leading to biased estimates due to ignored coupling.
Method: This paper proposes the first joint maximum likelihood estimation framework, built upon a differentiable path choice model that integrates random utility theory with numerical optimization. It supports unified modeling of multi-granularity, noisy, and partially observed path data—including real-world GPS trajectories (e.g., NYC taxi data).
Contribution/Results: Our approach enables end-to-end joint estimation of travel times and path choice parameters—the first such method—effectively mitigating bias inherent in sequential estimation. Experiments demonstrate significant improvements in path choice parameter accuracy and superior arc travel time estimation performance compared to link-only baselines. This framework provides a more reliable foundation for both strategic and tactical transportation network planning.
📝 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.
