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

arXiv: https://arxiv.org/abs/2104.13321 · HTML: https://mlsanity.com/paper/2104.13321/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 53 references that are in the map.
- First posted on arXiv 27 Apr 2021.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting](https://mlsanity.com/paper/1802.07007/) (2018)
- [Relational Fusion Networks: Graph Convolutional Networks for Road Networks](https://mlsanity.com/paper/2006.09030/) (2020)
- [DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation](https://mlsanity.com/paper/1909.05637/) (2019)
- [Travel Time Estimation without Road Networks: An Urban Morphological Layout Representation Approach](https://mlsanity.com/paper/1907.03381/) (2019)
- [BusTr: Predicting Bus Travel Times from Real-Time Traffic](https://mlsanity.com/paper/2007.00882/) (2020)
- [On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network](https://mlsanity.com/paper/1911.06217/) (2019)
- [Using Incomplete Information for Complete Weight Annotation of Road Networks -- Extended Version](https://mlsanity.com/paper/1308.0484/) (2013)
- [STAD: Spatio-Temporal Adjustment of Traffic-Oblivious Travel-Time Estimation](https://mlsanity.com/paper/2006.09892/) (2020)
- [Scalable Unsupervised Multi-Criteria Trajectory Segmentation and Driving Preference Mining](https://mlsanity.com/paper/2011.03331/) (2020)

## Abstract (arXiv)

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