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Bayesian Network for Analysis and Prediction of Traffic Congestion Using the Accident Data

Topics: Accident Analysis; Accident Prevention; Analytics for Intelligent Transportation; Big Data & Vehicle Analytics; Congestion Management and Avoidance; Pattern Recognition for Vehicles; Road Safety and Transport Security; Traffic and Vehicle Data Collection and Processing

Authors: Kranthi Talluri and Galia Weidl

Affiliation: Connected Urban Mobility, University of Applied Science, Aschaffenburg, Germany

Keyword(s): Bayesian Network, Congestion, Accident Hotspots, Labelling Techniques.

Abstract: Traffic congestion has become a significant concern regarding social safety and economic impact. Understanding the relationship between congestion and accidents is vital in providing the patterns to the Traffic Management System to mitigate the congestion as early as possible. Furthermore, traffic accidents lead to property damage, casualties, and increased congestion levels. So, a lot of research is going on to tackle this problem of accidents and congestion. This paper proposes a Bayesian Network (BN) to predict and analyze the factors of the probability of traffic congestion using accident data. A novel technique of labeling the congestion is being introduced, namely the formula-based and hotspot-based approaches, utilizing the accident dataset. Different scenarios are developed to understand the patterns causing congestion, and two classification models are used to evaluate the performance of the BN model. Model results are compared with different machine learning models. Results show that the proposed model outperforms in terms of accuracy and precision. It shows comparative performance concerning other machine learning algorithms. (More)

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Paper citation in several formats:
Talluri, K. and Weidl, G. (2024). Bayesian Network for Analysis and Prediction of Traffic Congestion Using the Accident Data. In Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS; ISBN 978-989-758-703-0; ISSN 2184-495X, SciTePress, pages 19-30. DOI: 10.5220/0012551200003702

@conference{vehits24,
author={Kranthi Talluri and Galia Weidl},
title={Bayesian Network for Analysis and Prediction of Traffic Congestion Using the Accident Data},
booktitle={Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS},
year={2024},
pages={19-30},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012551200003702},
isbn={978-989-758-703-0},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - Bayesian Network for Analysis and Prediction of Traffic Congestion Using the Accident Data
SN - 978-989-758-703-0
IS - 2184-495X
AU - Talluri, K.
AU - Weidl, G.
PY - 2024
SP - 19
EP - 30
DO - 10.5220/0012551200003702
PB - SciTePress

- Science and Technology Publications, Lda.
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