Analysis of Traffic Congestion Using LSTM and Graph Theory

Boyang Han

2024

Abstract

As the urbanization process continues to advance, the per capita vehicle ownership in cities keeps increasing. However, with the growth in traffic volume on roads, the originally short commuting time is constantly lengthened. Meanwhile, as a large number of vehicles converge at intersections, congestion rates also rise. Despite the measures taken by traffic police and volunteers to guide traffic during peak hours, congestion often occurs randomly and exhibits complexity. Moreover, if congested roads are not addressed, congestion can spread. In this paper, the Long Short-Term Memory(LSTM) algorithm is employed to analyze and predict traffic volume based on traffic flow information at intersections. Considering that detection devices at intersections in some second and third-tier cities may experience aging or malfunctions, a gradient descent algorithm is utilized to calculate the turning intentions of vehicles at each intersection at different times. This information is then used to extrapolate the approximate traffic volume at neighboring intersections. This approach not only aids the work of traffic police but also allows drivers to choose routes based on current congestion conditions and future congestion predictions.

Paper Citation

in Harvard Style

Han B. (2024). Analysis of Traffic Congestion Using LSTM and Graph Theory. In Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-690-3, SciTePress, pages 416-421. DOI: 10.5220/0012805700004547

in Bibtex Style

@conference{icdse24,
author={Boyang Han},
title={Analysis of Traffic Congestion Using LSTM and Graph Theory},
booktitle={Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2024},
pages={416-421},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012805700004547},
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 - Analysis of Traffic Congestion Using LSTM and Graph Theory
SN - 978-989-758-690-3
AU - Han B.
PY - 2024
SP - 416
EP - 421
DO - 10.5220/0012805700004547
PB - SciTePress