  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
