Abstract
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.
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.
Original language | English |
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Article number | 30 |
Journal | Transactions on Spatial Algorithms and Systems |
Volume | 8 |
Issue number | 4 |
Pages (from-to) | 1 |
Number of pages | 26 |
ISSN | 2374-0353 |
DOIs | |
Publication status | Published - 2 Nov 2022 |
Keywords
- Bayesian Learning
- Machine Learning
- Neural Networks
- Transportation