A Novel Method to Improve the Prediction of Vehicle Numbers Involved in Crashes at Rural Areas Using Reinforcement Learning Models

Marthandan T., Veena P., Jeyabarath R., Pragadeesh G., Prithiviraj S., Ragavendhra A.

2025

Abstract

Aim: The current study aims to design a new approach to enhance the prediction of the number of vehicles involved in crashes in rural areas using reinforcement learning models. Materials and Methods: Two groups were compared, where Group 1 is a traditional machine learning approaches how much vehicles involved in accidents based on historical crash data; Group 2 is a reinforcement learning (RL) model with a crash data driven model, which integrates crash data and can responds to real-time traffic as well as environmental factors for feedback, and can dynamically adjust prediction. Result: The system shows improved prediction accuracy compared to traditional Conventional model. The mean accuracy of the RL model is 95.2% while the mean of the comparative model is lower than the RL model and it is 88.9%. This increase in accuracy was statistically significant (p =.042), as verified by the independent samples test. Conclusion: This study demonstrates that the use of an RL-based prediction model yields reliable and higher performance in predicting the numbers of vehicles involved in crash events at rural locations. Also, this combination offers more plentiful and evolving detention action plans providing greater road safety.

Paper Citation

in Harvard Style

T. M., P. V., R. J., G. P., S. P. and A. R. (2025). A Novel Method to Improve the Prediction of Vehicle Numbers Involved in Crashes at Rural Areas Using Reinforcement Learning Models. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 690-696. DOI: 10.5220/0013919100004919

in Bibtex Style

@conference{icrdicct`2525,
author={Marthandan T. and Veena P. and Jeyabarath R. and Pragadeesh G. and Prithiviraj S. and Ragavendhra A.},
title={A Novel Method to Improve the Prediction of Vehicle Numbers Involved in Crashes at Rural Areas Using Reinforcement Learning Models},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={690-696},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013919100004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - A Novel Method to Improve the Prediction of Vehicle Numbers Involved in Crashes at Rural Areas Using Reinforcement Learning Models
SN - 978-989-758-777-1
AU - T. M.
AU - P. V.
AU - R. J.
AU - G. P.
AU - S. P.
AU - A. R.
PY - 2025
SP - 690
EP - 696
DO - 10.5220/0013919100004919
PB - SciTePress