# Motorway Traffic Flow Prediction using Advanced Deep Learning

## TL;DR

This paper introduces an advanced deep learning framework combining CNN and LSTM models to accurately predict motorway traffic flow using large-scale spatio-temporal data, outperforming traditional methods.

## Contribution

It presents a novel hybrid deep learning approach for large-scale, multi-station traffic prediction, addressing spatial and temporal dependencies simultaneously.

## Key findings

- Deep learning models outperform traditional traffic prediction methods.
- Hybrid CNN-LSTM models effectively capture spatio-temporal traffic dynamics.
- Optimal historical data horizon varies with prediction time.

## Abstract

Congestion prediction represents a major priority for traffic management centres around the world to ensure timely incident response handling. The increasing amounts of generated traffic data have been used to train machine learning predictors for traffic, however this is a challenging task due to inter-dependencies of traffic flow both in time and space. Recently, deep learning techniques have shown significant prediction improvements over traditional models, however open questions remain around their applicability, accuracy and parameter tuning. This paper proposes an advanced deep learning framework for simultaneously predicting the traffic flow on a large number of monitoring stations along a highly circulated motorway in Sydney, Australia, including exit and entry loop count stations, and over varying training and prediction time horizons. The spatial and temporal features extracted from the 36.34 million data points are used in various deep learning architectures that exploit their spatial structure (convolutional neuronal networks), their temporal dynamics (recurrent neuronal networks), or both through a hybrid spatio-temporal modelling (CNN-LSTM). We show that our deep learning models consistently outperform traditional methods, and we conduct a comparative analysis of the optimal time horizon of historical data required to predict traffic flow at different time points in the future.