Arc travel time and path choice model estimation subsumed

The recent study on arc travel time and route choice model estimation presents a significant advancement in traffic network analysis. Traditionally, these interdependent tasks have been approached separately, leading to potential inaccuracies in route choice model parameters. The proposed maximum likelihood estimation method allows for simultaneous estimation, effectively integrating observations of varying granularity, including noisy or partial data. Utilizing real taxi data from New York City, the method demonstrated strong performance, outperforming benchmark approaches focused solely on arc travel time estimation. This research underscores the critical nature of recognizing interdependence in traffic modeling, paving the way for more accurate and reliable traffic network planning.