# Deep Learning for Road Traffic Forecasting: Does it Make a Difference?

arXiv: https://arxiv.org/abs/2012.02260 · HTML: https://mlsanity.com/paper/2012.02260/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2020.

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

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting](https://mlsanity.com/paper/2306.08259/) (2023)
- [Evaluation of post-hoc interpretability methods in time-series classification](https://mlsanity.com/paper/2202.05656/) (2022)
- [Context-aware multi-head self-attentional neural network model for next location prediction](https://mlsanity.com/paper/2212.01953/) (2022)
- [Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities](https://mlsanity.com/paper/2305.19591/) (2023)
- [Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT)](https://mlsanity.com/paper/2305.05237/) (2023)
- [Experimental Standards for Deep Learning in Natural Language Processing Research](https://mlsanity.com/paper/2204.06251/) (2022)
- [Traffic Prediction with Transfer Learning: A Mutual Information-based Approach](https://mlsanity.com/paper/2303.07184/) (2023)
- [Gradient boosting machines and careful pre-processing work best: ASHRAE Great Energy Predictor III lessons learned](https://mlsanity.com/paper/2202.02898/) (2022)
- [Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors](https://mlsanity.com/paper/2303.07758/) (2023)
- [DSTCGCN: Learning Dynamic Spatial-Temporal Cross Dependencies for Traffic Forecasting](https://mlsanity.com/paper/2307.00518/) (2023)
- [A Graph-based Methodology for the Sensorless Estimation of Road Traffic Profiles](https://mlsanity.com/paper/2201.04968/) (2022)
- [Measuring the Confidence of Traffic Forecasting Models: Techniques, Experimental Comparison and Guidelines towards Their Actionability](https://mlsanity.com/paper/2210.16049/) (2022)
- [Inverting the Fundamental Diagram and Forecasting Boundary Conditions: How Machine Learning Can Improve Macroscopic Models for Traffic Flow](https://mlsanity.com/paper/2303.12740/) (2023)
- [Distil the informative essence of loop detector data set: Is network-level traffic forecasting hungry for more data?](https://mlsanity.com/paper/2310.20366/) (2023)
- [A causal intervention framework for synthesizing mobility data and evaluating predictive neural networks](https://mlsanity.com/paper/2311.11749/) (2023)
- [Graph Neural Networks in Intelligent Transportation Systems: Advances, Applications and Trends](https://mlsanity.com/paper/2401.00713/) (2024)
- [Benchmarking M-LTSF: Frequency and Noise-Based Evaluation of Multivariate Long Time Series Forecasting Models](https://mlsanity.com/paper/2510.04900/) (2025)
- [A comparative study of deep learning and ensemble learning to extend the horizon of traffic forecasting](https://mlsanity.com/paper/2504.21358/) (2025)
- [Error Adjustment Based on Spatiotemporal Correlation Fusion for Traffic Forecasting](https://mlsanity.com/paper/2510.23656/) (2025)
- [UniST-Pred: A Robust Unified Framework for Spatio-Temporal Traffic Forecasting in Transportation Networks Under Disruptions](https://mlsanity.com/paper/2602.14049/) (2026)

## Abstract (arXiv)

Deep Learning methods have been proven to be flexible to model complex phenomena. This has also been the case of Intelligent Transportation Systems (ITS), in which several areas such as vehicular perception and traffic analysis have widely embraced Deep Learning as a core modeling technology. Particularly in short-term traffic forecasting, the capability of Deep Learning to deliver good results has generated a prevalent inertia towards using Deep Learning models, without examining in depth their benefits and downsides. This paper focuses on critically analyzing the state of the art in what refers to the use of Deep Learning for this particular ITS research area. To this end, we elaborate on the findings distilled from a review of publications from recent years, based on two taxonomic criteria. A posterior critical analysis is held to formulate questions and trigger a necessary debate about the issues of Deep Learning for traffic forecasting. The study is completed with a benchmark of diverse short-term traffic forecasting methods over traffic datasets of different nature, aimed to cover a wide spectrum of possible scenarios. Our experimentation reveals that Deep Learning could not be the best modeling technique for every case, which unveils some caveats unconsidered to date that should be addressed by the community in prospective studies. These insights reveal new challenges and research opportunities in road traffic forecasting, which are enumerated and discussed thoroughly, with the intention of inspiring and guiding future research efforts in this field.