Machine Learning Models for Traffic Prediction on Arterial Roads Using Traffic Features and Weather Information

Rusul Abduljabbar; Hussein Dia; Sohani Liyanage

doi:10.3390/app142311047

journal article Open Access Nov 27, 2024

Machine Learning Models for Traffic Prediction on Arterial Roads Using Traffic Features and Weather Information

Rusul Abduljabbar

Hussein Dia

Sohani Liyanage

Applied Sciences Vol. 14 No. 23 pp. 11047 · MDPI AG

View at Publisher Save 10.3390/app142311047

Abstract

This study addresses the challenges of predicting traffic flow on arterial roads where dynamic behaviours such as passenger pick-ups, vehicle turns, and parking interruptions complicate forecasting. The research develops and evaluates unidirectional and bidirectional Long Short-Term Memory (LSTM) models using a dataset of 70,072 observations collected over 12 months from Hoddle Street in Melbourne, Australia. The models were trained to predict traffic speeds and volumes up to 60 min ahead. The results indicated that the BiLSTM model significantly outperformed unidirectional LSTM, achieving over 99% accuracy for speed predictions and over 96% for volume predictions. The research also tested the impacts of incorporating weather variables such as rainfall, temperature, humidity, and wind speed on model performance, which was found to provide small improvements. Traffic flow prediction accuracy increased from 97.5% to 97.6% for 30-min horizons, and from 96.9% to 97.6% for 60-min horizons. Although the inclusion of weather data only marginally enhanced prediction performance, its inclusion has practical implications for public awareness of travel impacts under severe weather. The findings in this study highlight the effectiveness of deep learning techniques for traffic forecasting on arterial roads, establishing a foundation for future research in this area.

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Details

Published: Nov 27, 2024
Vol/Issue: 14(23)
Pages: 11047
License: View

Authors

R

Rusul Abduljabbar

Department of Civil and Construction Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia

H

Hussein Dia

Department of Civil and Construction Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia

S

Sohani Liyanage

Department of Civil and Construction Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia

Cite This Article

Rusul Abduljabbar, Hussein Dia, Sohani Liyanage (2024). Machine Learning Models for Traffic Prediction on Arterial Roads Using Traffic Features and Weather Information. Applied Sciences, 14(23), 11047. https://doi.org/10.3390/app142311047

Machine Learning Models for Traffic Prediction on Arterial Roads Using Traffic Features and Weather Information

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