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.

翻译：我们建议了一种方法,用不同颗粒水平的数据对路径选择模型参数和弧旅行时间进行最大可能性估计。 这两项任务一直都是在强有力的假设下分别处理的。 我们举一个小例子,说明这可能导致有偏差的结果。 真实(纽约黄色出租车)和模拟数据的结果显示,与现有基线相比,我们的方法表现良好。