# Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools

arXiv: https://arxiv.org/abs/2103.11824 · HTML: https://mlsanity.com/paper/2103.11824/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 115 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 12 of 12)

- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction](https://mlsanity.com/paper/1610.00081/) (2016)
- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach](https://mlsanity.com/paper/2003.08725/) (2020)
- [Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction](https://mlsanity.com/paper/1801.02143/) (2018)
- [Applications of deep learning in stock market prediction: recent progress](https://mlsanity.com/paper/2003.01859/) (2020)
- [Deep Sequence Learning with Auxiliary Information for Traffic Prediction](https://mlsanity.com/paper/1806.07380/) (2018)
- [A Countrywide Traffic Accident Dataset](https://mlsanity.com/paper/1906.05409/) (2019)
- [DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting](https://mlsanity.com/paper/1709.09585/) (2017)
- [Efficient Metropolitan Traffic Prediction Based on Graph Recurrent Neural Network](https://mlsanity.com/paper/1811.00740/) (2018)
- [Urban Sensing based on Mobile Phone Data: Approaches, Applications and Challenges](https://mlsanity.com/paper/2008.12992/) (2020)
- [Characteristics of human mobility patterns revealed by high-frequency cell-phone position data](https://mlsanity.com/paper/1907.03604/) (2019)

## Built on by (most cited first, 3 of 3)

- [Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities](https://mlsanity.com/paper/2305.19591/) (2023)
- [Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction](https://mlsanity.com/paper/2109.00924/) (2021)
- [PrivateHub: Contrastive Diffusion Model for Private Sensor-Intensive Environment Data Generation](https://mlsanity.com/paper/2609.02958/) (2026)

## Abstract (arXiv)

Big data has been used widely in many areas including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted for improving the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction. Different data types are categorized and the off-the-shelf tools are introduced. To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies.