UniTE -- The Best of Both Worlds: Unifying Function-Fitting and Aggregation-Based Approaches to Travel Time and Travel Speed Estimation

TL;DR

UniTE is a unified framework that combines function-fitting and aggregation methods to improve travel time and speed estimation accuracy, balancing generalizability and precision in transportation applications.

Contribution

This paper introduces UniTE, a novel approach that unifies function-fitting and aggregation-based methods for travel estimation, enhancing accuracy and generalizability.

Findings

Improves travel speed distribution accuracy by 40-64%.

Enhances travel time estimation accuracy by 3-23%.

Outperforms individual methods in empirical tests.

Abstract

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