UniTE -- The Best of Both Worlds: Unifying Function-Fitting and Aggregation-Based Approaches to Travel Time and Travel Speed Estimation
This work addresses a key problem in intelligent transportation systems by providing a more accurate and generalizable solution for travel time and speed estimation, though it is incremental as it combines existing approaches.
The paper tackles the trade-off between generalizability and accuracy in travel time and speed estimation by unifying function-fitting and aggregation-based approaches into a single framework, resulting in improvements of 40-64% for travel speed distribution and 3-23% for travel time estimation compared to using either method alone.
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