Deep Learning-Based Algorithms in Solving Traffic Jam in Smart Transportation

Jingru Deng

2024

Abstract

With the increase in the number of private cars, people's travel has become more convenient. However, currently artificial intelligence has not played a particularly significant role in solving traffic congestion. Traffic congestion has lots of disadvantage: it can increase people's commuting time; it can increase travel costs and so on. This article aims at providing an overview of some possible ways in predicting traffic flow, ranging from machine-learning based method to deep learning methods, which give a feasible scheme for the traffic system to moderate the traffic light time and better smooth the traffic flow. In the discussion part, the article analysis that machine-learning based methods still have shortcomings in terms of interpretability and adaptability. In the future, the predicting method will be improved by adapting SHapley Additive exPlanations and domain adaptation. Also, computational speed also needs to be taken into account, with the use of parallel computing. The author proposes a framework that focuses on parallel computing. This article provides a good overview of the field of predicting traffic flow.

Paper Citation

in Harvard Style

Deng J. (2024). Deep Learning-Based Algorithms in Solving Traffic Jam in Smart Transportation. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 541-545. DOI: 10.5220/0012958500004508

in Bibtex Style

@conference{emiti24,
author={Jingru Deng},
title={Deep Learning-Based Algorithms in Solving Traffic Jam in Smart Transportation},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={541-545},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012958500004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Deep Learning-Based Algorithms in Solving Traffic Jam in Smart Transportation
SN - 978-989-758-713-9
AU - Deng J.
PY - 2024
SP - 541
EP - 545
DO - 10.5220/0012958500004508
PB - SciTePress