AI Hybrid Blackspot Detection and Reporting

Bhavya Jain; Jahnvi Singh; Sandeep Saxena; Faisal Siddiqui; Radhika Gupta; Sahil Kumar Aggarwal

doi:10.2991/978-94-6239-723-1_2

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AI Hybrid Blackspot Detection and Reporting

Authors

Bhavya Jain¹^{, *}, Jahnvi Singh², Sandeep Saxena³, Faisal Siddiqui⁴, Radhika Gupta⁵, Sahil Kumar Aggarwal⁶

¹Abes Engineering College, Ghaziabad, India

²Abes Engineering College, Ghaziabad, India

³IILM University, Greater Noida, India

⁴Abes Engineering College, Ghaziabad, India

⁵Abes Engineering College, Ghaziabad, India

⁶Abes Engineering College, Ghaziabad, India

^*Corresponding author. Email: bhavyajain020105@gmail.com

Corresponding Author

Bhavya Jain

Available Online 14 July 2026.

DOI: 10.2991/978-94-6239-723-1_2 How to use a DOI?
Keywords: Accident prediction; Intelligent Transportation Systems (ITS); machine learning; weather data integration; real-time risk assessment
Abstract: Predicting road accidents is a significant part of Intelligent Transportation Systems (ITS), especially in areas where there is a high volume of traffic and varying environmental conditions. Most of the current prediction models for road accidents are based on historical data regarding road accidents and traffic. These prediction models are inadequate for evaluating road accidents in real-time with varying environmental conditions. This paper aims to provide a framework for predicting road accidents by applying Artificial Intelligence (AI) techniques and machine learning algorithms with real-time dynamic meteorological parameters. Supervised machine learning algorithms are used for predicting road accidents by considering historical data regarding road accidents along with dynamic meteorological parameters such as visibility, rainfall, humidity, temperature, and wind speed. The main contribution of this work is the integration of dynamic weather parameters with traffic parameters for predicting road accidents. The proposed framework can be used for improving situational awareness and intelligent decision-making for road users, as well as for autonomous transport systems.
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the International Conference on Responsible, Risk-aware, and Regulated AI (RRRAI 2026)
Series: Advances in Intelligent Systems Research
Publication Date: 14 July 2026
ISBN: 978-94-6239-723-1
ISSN: 1951-6851
DOI: 10.2991/978-94-6239-723-1_2 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Bhavya Jain
AU  - Jahnvi Singh
AU  - Sandeep Saxena
AU  - Faisal Siddiqui
AU  - Radhika Gupta
AU  - Sahil Kumar Aggarwal
PY  - 2026
DA  - 2026/07/14
TI  - AI Hybrid Blackspot Detection and Reporting
BT  - Proceedings of the International Conference on Responsible, Risk-aware, and Regulated AI (RRRAI 2026)
PB  - Atlantis Press
SP  - 7
EP  - 14
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-723-1_2
DO  - 10.2991/978-94-6239-723-1_2
ID  - Jain2026
ER  -

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