# DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision

arXiv: https://arxiv.org/abs/1802.02147 · HTML: https://mlsanity.com/paper/1802.02147/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2018.

## Built on by (most cited first, 21 of 21)

- [Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Era](https://mlsanity.com/paper/2403.14151/) (2024)
- [RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer](https://mlsanity.com/paper/2211.13234/) (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)
- [Spatio-Temporal Dual Graph Neural Networks for Travel Time Estimation](https://mlsanity.com/paper/2105.13591/) (2021)
- [PENGUIN: Enhancing Transformer with Periodic-Nested Group Attention for Long-term Time Series Forecasting](https://mlsanity.com/paper/2508.13773/) (2025)
- [Preserving Privacy in Personalized Models for Distributed Mobile Services](https://mlsanity.com/paper/2101.05855/) (2021)
- [GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation](https://mlsanity.com/paper/2306.04324/) (2023)
- [RLER-TTE: An Efficient and Effective Framework for En Route Travel Time Estimation with Reinforcement Learning](https://mlsanity.com/paper/2501.15493/) (2025)
- [Learning Spatio-Temporal Dynamics for Trajectory Recovery via Time-Aware Transformer](https://mlsanity.com/paper/2505.13857/) (2025)
- [An Empirical Survey and Benchmark of Learned Distance Indexes for Road Networks](https://mlsanity.com/paper/2602.04068/) (2026)
- [Unavailable Transit Feed Specification: Making it Available with Recurrent Neural Networks](https://mlsanity.com/paper/2102.10323/) (2021)
- [Partitioned Graph Convolution Using Adversarial and Regression Networks for Road Travel Speed Prediction](https://mlsanity.com/paper/2103.00067/) (2021)
- [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)
- [JiuTian Chuanliu: A Large Spatiotemporal Model for General-purpose Dynamic Urban Sensing](https://mlsanity.com/paper/2510.23662/) (2025)
- [MixTTE: Multi-Level Mixture-of-Experts for Scalable and Adaptive Travel Time Estimation](https://mlsanity.com/paper/2601.02943/) (2026)
- [PAtt: A Pattern Attention Network for ETA Prediction Using Historical Speed Profiles](https://mlsanity.com/paper/2601.13793/) (2026)
- [DSETA: A Dual-Stage Continual Learning Framework for Travel Time Prediction in Dynamic Traffic Environments](https://mlsanity.com/paper/2608.00402/) (2026)

## Abstract (arXiv)

Estimating the travel time of a path is of great importance to smart urban mobility. Existing approaches are either based on estimating the time cost of each road segment which are not able to capture many cross-segment complex factors, or designed heuristically in a non-learning-based way which fail to utilize the existing abundant temporal labels of the data, i.e., the time stamp of each trajectory point. In this paper, we leverage on new development of deep neural networks and propose a novel auxiliary supervision model, namely DeepTravel, that can automatically and effectively extract different features, as well as make full use of the temporal labels of the trajectory data. We have conducted comprehensive experiments on real datasets to demonstrate the out-performance of DeepTravel over existing approaches.