Travel time or speed estimation are part of many intelligent transportation applications. Existing estimation approaches rely on either function fitting or aggregation and represent different trade-offs between generalizability and accuracy. Function-fitting approaches learn functions that map feature vectors of, e.g., routes, to travel time or speed estimates, which enables generalization to unseen routes. However, mapping functions are imperfect and offer poor accuracy in practice. Aggregation-based approaches instead form estimates by aggregating historical data, e.g., traversal data for routes. This enables very high accuracy given sufficient data. However, they rely on simplistic heuristics when insufficient data is available, yielding poor generalizability. We present a Unifying approach to Travel time and speed Estimation (UniTE) that combines function-fitting and aggregation-based approaches into a unified framework that aims to achieve the generalizability of function-fitting approaches and the accuracy of aggregation-based approaches. An empirical study finds that an instance of UniTE can improve the accuracies of travel speed distribution and travel time estimation by $40-64\%$ and $3-23\%$, respectively, compared to using function fitting or aggregation alone

中文翻译：

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UniTE-两全其美：结合功能拟合和基于聚集的旅行时间和旅行速度估算方法

行驶时间或速度估算是许多智能交通应用程序的一部分。现有的估计方法依赖于函数拟合或聚合，并且代表了可概括性和准确性之间的不同权衡。功能拟合方法学习的功能是将例如路线的特征向量映射到行进时间或速度估计值，从而可以泛化到看不见的路线。但是，映射功能是不完善的，并且在实践中提供了较差的准确性。相反，基于聚集的方法通过聚集历史数据（例如，路线的遍历数据）来形成估算值。给定足够的数据，这可以实现非常高的准确性。但是，当没有足够的数据可用时，它们依赖于简单的启发式方法，从而导致泛化性差。我们提出了旅行时间和速度估计（UniTE）的统一方法，该方法将基于函数拟合和基于聚集的方法组合到一个统一的框架中，旨在实现函数拟合方法的通用性和基于聚集的方法的准确性。一项经验研究发现，与单独使用函数拟合或聚合相比，UniTE实例可以将行驶速度分布和行驶时间估计的精度分别提高$ 40-64 \％$和$ 3-23 \％$