论文标题
弧旅行时间和路径选择模型估计已包含
Arc travel time and path choice model estimation subsumed
论文作者
论文摘要
我们提出了一种使用不同粒度级别的数据，以最大程度地估计路径选择模型参数和弧旅行时间的方法。迄今为止，这两个任务已在强有力的假设下分别解决。使用一个小例子，我们说明这可能导致偏差结果。与现有基准相比，对真实（纽约黄色驾驶室）和模拟数据的结果都表明我们方法的性能很强。
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.
