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

TL;DR

This paper presents a real-time traffic alarm system for Valencia using LSTM neural networks, predicting high traffic levels to help reduce pollution and improve urban health.

Contribution

It introduces a novel LSTM-based prediction model for real-time traffic alerts tailored to urban pollution reduction efforts.

Findings

Traffic significantly impacts NOx pollution levels.

The LSTM model accurately predicts high traffic events 30 minutes in advance.

The system can be integrated into city traffic management.

Abstract

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 $\text{NO}_\text{x}$). 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.