# Arc travel time and path choice model estimation subsumed

arXiv: https://arxiv.org/abs/2210.14351 · HTML: https://mlsanity.com/paper/2210.14351/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 41 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Monte Carlo Gradient Estimation in Machine Learning](https://mlsanity.com/paper/1906.10652/) (2019)
- [A tutorial on recursive models for analyzing and predicting path choice behavior](https://mlsanity.com/paper/1905.00883/) (2019)
- [NOMAD version 4: Nonlinear optimization with the MADS algorithm](https://mlsanity.com/paper/2104.11627/) (2021)

## Abstract (arXiv)

We address the problem of simultaneously estimating arc travel times in a network 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.