# A Simple Baseline for Travel Time Estimation using Large-Scale Trip Data

arXiv: https://arxiv.org/abs/1512.08580 · HTML: https://mlsanity.com/paper/1512.08580/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2015.

## Built on by (most cited first, 28 of 28)

- [Origin-Destination Travel Time Oracle for Map-based Services](https://mlsanity.com/paper/2307.03048/) (2023)
- [Semi-Supervised Graph-to-Graph Translation](https://mlsanity.com/paper/2103.08827/) (2021)
- [Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities](https://mlsanity.com/paper/2305.19591/) (2023)
- [TrajFM: A Vehicle Trajectory Foundation Model for Region and Task Transferability](https://mlsanity.com/paper/2408.15251/) (2024)
- [Modeling Network-level Traffic Flow Transitions on Sparse Data](https://mlsanity.com/paper/2208.06646/) (2022)
- [DuETA: Traffic Congestion Propagation Pattern Modeling via Efficient Graph Learning for ETA Prediction at Baidu Maps](https://mlsanity.com/paper/2208.06979/) (2022)
- [Fine-Grained Trajectory-based Travel Time Estimation for Multi-city Scenarios Based on Deep Meta-Learning](https://mlsanity.com/paper/2201.08017/) (2022)
- [DutyTTE: Deciphering Uncertainty in Origin-Destination Travel Time Estimation](https://mlsanity.com/paper/2408.12809/) (2024)
- [ODformer: Spatial-Temporal Transformers for Long Sequence Origin-Destination Matrix Forecasting Against Cross Application Scenario](https://mlsanity.com/paper/2208.08218/) (2022)
- [Inductive Graph Transformer for Delivery Time Estimation](https://mlsanity.com/paper/2211.02863/) (2022)
- [A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2309.01194/) (2023)
- [Learning to Estimate Package Delivery Time in Mixed Imbalanced Delivery and Pickup Logistics Services](https://mlsanity.com/paper/2505.00375/) (2025)
- [Spatio-Temporal Dual Graph Neural Networks for Travel Time Estimation](https://mlsanity.com/paper/2105.13591/) (2021)
- [Multitask Weakly Supervised Learning for Origin Destination Travel Time Estimation](https://mlsanity.com/paper/2301.05336/) (2023)
- [Dual Graph Multitask Framework for Imbalanced Delivery Time Estimation](https://mlsanity.com/paper/2302.07429/) (2023)
- [TransferTraj: A Vehicle Trajectory Learning Model for Region and Task Transferability](https://mlsanity.com/paper/2505.12672/) (2025)
- [UVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generation](https://mlsanity.com/paper/2402.07232/) (2024)
- [GOF-TTE: Generative Online Federated Learning Framework for Travel Time Estimation](https://mlsanity.com/paper/2207.00838/) (2022)
- [GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation](https://mlsanity.com/paper/2306.04324/) (2023)
- [An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival](https://mlsanity.com/paper/2203.09438/) (2022)
- [Link Representation Learning for Probabilistic Travel Time Estimation](https://mlsanity.com/paper/2407.05895/) (2024)
- [RLER-TTE: An Efficient and Effective Framework for En Route Travel Time Estimation with Reinforcement Learning](https://mlsanity.com/paper/2501.15493/) (2025)
- [TLETA: Deep Transfer Learning and Integrated Cellular Knowledge for Estimated Time of Arrival Prediction](https://mlsanity.com/paper/2206.08513/) (2022)
- [HTTE: A Hybrid Technique For Travel Time Estimation In Sparse Data Environments](https://mlsanity.com/paper/2301.05293/) (2023)
- [Trajectory Data Mining and Trip Travel Time Prediction on Specific Roads](https://mlsanity.com/paper/2407.07030/) (2024)
- [Three Pillars Towards Next-Generation Routing System](https://mlsanity.com/paper/2409.11412/) (2024)
- [Multimodal Trajectory Representation Learning for Travel Time Estimation](https://mlsanity.com/paper/2510.05840/) (2025)
- [DSETA: A Dual-Stage Continual Learning Framework for Travel Time Prediction in Dynamic Traffic Environments](https://mlsanity.com/paper/2608.00402/) (2026)

## Abstract (arXiv)

The increased availability of large-scale trajectory data around the world provides rich information for the study of urban dynamics. For example, New York City Taxi Limousine Commission regularly releases source-destination information about trips in the taxis they regulate. Taxi data provide information about traffic patterns, and thus enable the study of urban flow -- what will traffic between two locations look like at a certain date and time in the future? Existing big data methods try to outdo each other in terms of complexity and algorithmic sophistication. In the spirit of "big data beats algorithms", we present a very simple baseline which outperforms state-of-the-art approaches, including Bing Maps and Baidu Maps (whose APIs permit large scale experimentation). Such a travel time estimation baseline has several important uses, such as navigation (fast travel time estimates can serve as approximate heuristics for A search variants for path finding) and trip planning (which uses operating hours for popular destinations along with travel time estimates to create an itinerary).