  We propose a method for maximum likelihood estimation of path choice model
parameters and arc travel time using data of different levels of granularity.
Hitherto these two tasks have been tackled separately under strong assumptions.
Using a small example, we illustrate that this can lead to biased results.
Results on both real (New York yellow cab) and simulated data show strong
performance of our method compared to existing baselines.
