# Establishing a real-time traffic alarm in the city of Valencia with Deep Learning

arXiv: https://arxiv.org/abs/2309.02010 · HTML: https://mlsanity.com/paper/2309.02010/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 5 Sep 2023.

## Builds on (most cited first, 13 of 13)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Diffusion-Convolutional Neural Networks](https://mlsanity.com/paper/1511.02136/) (2015)
- [Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting](https://mlsanity.com/paper/1802.07007/) (2018)
- [Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction](https://mlsanity.com/paper/1801.02143/) (2018)
- [Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values](https://mlsanity.com/paper/2005.11627/) (2020)
- [Forecasting Transportation Network Speed Using Deep Capsule Networks with Nested LSTM Models](https://mlsanity.com/paper/1811.04745/) (2018)
- [DDP-GCN: Multi-Graph Convolutional Network for Spatiotemporal Traffic Forecasting](https://mlsanity.com/paper/1905.12256/) (2019)
- [A Deep Generative Adversarial Architecture for Network-Wide Spatial-Temporal Traffic State Estimation](https://mlsanity.com/paper/1801.03818/) (2018)
- [A spatial-temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism](https://mlsanity.com/paper/2205.04762/) (2022)
- [Generalizing the Convolution Operator to extend CNNs to Irregular Domains](https://mlsanity.com/paper/1606.01166/) (2016)
- [Predicting the traffic flux in the city of Valencia with Deep Learning](https://mlsanity.com/paper/2210.01630/) (2022)

## Abstract (arXiv)

Urban traffic emissions represent a significant concern due to their detrimental impacts on both public health and the environment. Consequently, decision-makers have flagged their reduction as a crucial goal. In this study, we first analyze the correlation between traffic flux and pollution in the city of Valencia, Spain. Our results demonstrate that traffic has a significant impact on the levels of certain pollutants (especially NOx). Secondly, we develop an alarm system to predict if a street is likely to experience unusually high traffic in the next 30 minutes, using an independent three-tier level for each street. To make the predictions, we use traffic data updated every 10 minutes and Long Short-Term Memory (LSTM) neural networks. We trained the LSTM using traffic data from 2018, and tested it using traffic data from 2019.