Deep Learning-Based Prediction and Analysis of Highway Traffic Flow near Airports

Boyuan Jiang

2024

Abstract

The surrounding highways of large airports play a crucial role in traffic, making it essential to accurately predict traffic flow. In this study, the Long Short-Term Memory (LSTM) model was employed as the data prediction model to forecast data from six stations on the M25 highway near London Heathrow Airport in August 2019. The LSTM model utilized a prediction interval with a time slot length of 5, and error analysis was conducted. The final predictions revealed a bimodal pattern in daily traffic volume on the highway, with a unimodal pattern in average vehicle speed. On highway ramps, daily traffic volume exhibited a multimodal pattern, and although average vehicle speed displayed slight fluctuations, it remained relatively stable overall. Furthermore, error analysis indicated that the LSTM model demonstrated a good fit and produced satisfactory prediction results. This paper has the potential to greatly contribute to the improvement and enhanced management of highway traffic surrounding large airports.

Paper Citation

in Harvard Style

Jiang B. (2024). Deep Learning-Based Prediction and Analysis of Highway Traffic Flow near Airports. In Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-690-3, SciTePress, pages 387-393. DOI: 10.5220/0012886200004547

in Bibtex Style

@conference{icdse24,
author={Boyuan Jiang},
title={Deep Learning-Based Prediction and Analysis of Highway Traffic Flow near Airports},
booktitle={Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2024},
pages={387-393},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012886200004547},
isbn={978-989-758-690-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Deep Learning-Based Prediction and Analysis of Highway Traffic Flow near Airports
SN - 978-989-758-690-3
AU - Jiang B.
PY - 2024
SP - 387
EP - 393
DO - 10.5220/0012886200004547
PB - SciTePress