# DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation

arXiv: https://arxiv.org/abs/1909.05637 · HTML: https://mlsanity.com/paper/1909.05637/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2019.

## Built on by (most cited first, 11 of 11)

- [Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Era](https://mlsanity.com/paper/2403.14151/) (2024)
- [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)
- [Multitask Weakly Supervised Learning for Origin Destination Travel Time Estimation](https://mlsanity.com/paper/2301.05336/) (2023)
- [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)
- [Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework](https://mlsanity.com/paper/2410.01011/) (2024)
- [Multi View Spatial-Temporal Model for Travel Time Estimation](https://mlsanity.com/paper/2109.07402/) (2021)
- [Meta-Learning over Time for Destination Prediction Tasks](https://mlsanity.com/paper/2206.14801/) (2022)
- [Learning Citywide Patterns of Life from Trajectory Monitoring](https://mlsanity.com/paper/2206.15352/) (2022)
- [UniTE -- The Best of Both Worlds: Unifying Function-Fitting and Aggregation-Based Approaches to Travel Time and Travel Speed Estimation](https://mlsanity.com/paper/2104.13321/) (2021)

## Abstract (arXiv)

Estimating the travel time for a given path is a fundamental problem in many urban transportation systems. However, prior works fail to well capture moving behaviors embedded in paths and thus do not estimate the travel time accurately. To fill in this gap, in this work, we propose a novel neural network framework, namely em Deep Image-based Spatio-Temporal network (DeepIST), for travel time estimation of a given path. The novelty of DeepIST lies in the following aspects: 1) we propose to plot a path as a sequence of "generalized images" which include sub-paths along with additional information, such as traffic conditions, road network and traffic signals, in order to harness the power of convolutional neural network model (CNN) on image processing; 2) we design a novel two-dimensional CNN, namely em PathCNN, to extract spatial patterns for lines in images by regularization and adopting multiple pooling methods; and 3) we apply a one-dimensional CNN to capture temporal patterns among the spatial patterns along the paths for the estimation. Empirical results show that DeepIST soundly outperforms the state-of-the-art travel time estimation models by 24.37% to 25.64% of mean absolute error (MAE) in multiple large-scale real-world datasets.