# DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting

arXiv: https://arxiv.org/abs/1709.09585 · HTML: https://mlsanity.com/paper/1709.09585/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2017.

## Built on by (most cited first, 16 of 16)

- [SST: Multi-Scale Hybrid Mamba-Transformer Experts for Time Series Forecasting](https://mlsanity.com/paper/2404.14757/) (2024)
- [Applications of deep learning in traffic congestion detection, prediction and alleviation: A survey](https://mlsanity.com/paper/2102.09759/) (2021)
- [Residual Correction in Real-Time Traffic Forecasting](https://mlsanity.com/paper/2209.05406/) (2022)
- [Graph Neural Networks for Traffic Forecasting](https://mlsanity.com/paper/2104.13096/) (2021)
- [Interpretable Cascading Mixture-of-Experts for Urban Traffic Congestion Prediction](https://mlsanity.com/paper/2406.12923/) (2024)
- [Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study](https://mlsanity.com/paper/2201.13229/) (2022)
- [USTBench: Benchmarking and Dissecting Spatiotemporal Reasoning of LLMs as Urban Agents](https://mlsanity.com/paper/2505.17572/) (2025)
- [Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools](https://mlsanity.com/paper/2103.11824/) (2021)
- [TrafFormer: A Transformer Model for Predicting Long-term Traffic](https://mlsanity.com/paper/2302.12388/) (2023)
- [PSTN: Periodic Spatial-temporal Deep Neural Network for Traffic Condition Prediction](https://mlsanity.com/paper/2108.02424/) (2021)
- [FRTP: Federating Route Search Records to Enhance Long-term Traffic Prediction](https://mlsanity.com/paper/2412.17373/) (2024)
- [A Transferable Intersection Reconstruction Network for Traffic Speed Prediction](https://mlsanity.com/paper/2207.11030/) (2022)
- [Large-Scale Traffic Congestion Prediction based on Multimodal Fusion and Representation Mapping](https://mlsanity.com/paper/2208.11061/) (2022)
- [Newell's theory based feature transformations for spatio-temporal traffic prediction](https://mlsanity.com/paper/2307.05949/) (2023)
- [A Sparse Cross Attention-based Graph Convolution Network with Auxiliary Information Awareness for Traffic Flow Prediction](https://mlsanity.com/paper/2312.09050/) (2023)
- [A Progressive Visual-Logic-Aligned Framework for Ride-Hailing Adjudication](https://mlsanity.com/paper/2603.17328/) (2026)

## Abstract (arXiv)

Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on traffic observations of the target location as well as its adjacent regions, but they obtain somewhat limited accuracy due to a lack of mining road topology. To address the effect attenuation problem, we suggest taking into account the traffic of surrounding locations(wider than the adjacent range). We propose an end-to-end framework called DeepTransport, in which Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain spatial-temporal traffic information within a transport network topology. In addition, an attention mechanism is introduced to align spatial and temporal information. Moreover, we constructed and released a real-world large traffic condition dataset with a 5-minute resolution. Our experiments on this dataset demonstrate our method captures the complex relationship in the temporal and spatial domains. It significantly outperforms traditional statistical methods and a state-of-the-art deep learning method.