UniTE—The Best of Both Worlds: Unifying Function-fitting and Aggregation-based Approaches to Travel Time and Travel Speed Estimation

• Published in: ACM transactions on spatial algorithms and systems 2022-11, Vol.8 (4), p.1-26, Article 30
• Main Authors: Jepsen, Tobias Skovgaard, Jensen, Christian S., Nielsen, Thomas Dyhre
• Format: Article
• Language: English
• Subjects: Applied computing; Bayesian network models; Computing methodologies; Forecasting; Neural networks; Supervised learning by regression; Transportation
• ISSN: 2374-0353; 2374-0361
• DOI: 10.1145/3517335

Travel time and speed estimation are part of many intelligent transportation applications. Existing estimation approaches rely on either function fitting or data aggregation and represent different tradeoffs 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 when data is available. We demonstrate empirically that an instance of UniTE can improve the accuracies of travel speed and travel time estimation by 40–64% and 3–23%, respectively, compared to using only function fitting or data aggregation.