Spot Alert: Black Spot Identification & Alerting System

Abstract

One of the most serious threats to security on roads are accidents that keep occurring nearly on all roads particularly on accident prone specific location also called as “black spot”. Providing information for such locations is a good governance initiative but providing the data in non-interactive and static formats through government platforms makes it difficult for the drivers to make best use of information while being on travel. This project intends to develop an intelligent navigation application to ensure improved road safety through alerts and immediate information regarding hazards. The proposed system is an alternative navigation system that is capable of providing traffic notification, a 3D view of the route, and voice notification to the user whenever the user approaches to accidents, construction site and dangerous places. Moreover, the accidents and construction sites can be updated on the system by authorized users such as police department, and road construction department to provide notified to the user accurately and timely.

Introduction

The paper presents SpotAlert, an intelligent road safety system designed to identify and warn users about accident-prone locations (black spots) using geospatial data and real-time navigation alerts.

Road accidents are influenced by multiple factors such as driver behavior, infrastructure, and environmental conditions. Traditional black spot detection methods rely on historical accident records and manual analysis, which are slow, static, and not suitable for real-time prevention.

SpotAlert improves this by using a proactive, map-based approach. It detects hazardous locations using OpenStreetMap (OSM) data and administrative inputs, identifying high-risk zones such as schools, traffic signals, roundabouts, and railway crossings. Each hazard is stored in a structured database with attributes like location, type, severity, and verification status.

The system works through a pipeline:

• Data collection from OSM and authorities
• Geospatial hazard detection and extraction
• Storage in a structured hazard database
• Risk classification (low, medium, high)
• Route-based filtering using GPS to identify relevant hazards
• Real-time alerts via visual and voice notifications

Unlike traditional systems, SpotAlert focuses on preventive safety rather than historical analysis, providing drivers with context-aware warnings while navigating. This improves road safety by reducing false alerts and ensuring timely warnings near dangerous zones.

References

[1] I. Karamanlis, A. Kokkalis, V. Profillidis, G. Botzoris, and A. Galanis, \"Identifying Road Accident Black Spots using Classical and Modern Approaches,\" Department of Civil Engineering, Democritus University of Thrace & International Hellenic University, Greece. [2] T. Tanprasert, C. Siripanpornchana, N. Surasvadi, and S. Thajchayapong, \"Recognizing Traffic Black Spots From Street View Images Using Environment-Aware Image Processing and Neural Network,\" National Electronics and Computer Technology Center (NECTEC), Thailand. [3] L. S. Bisht and G. Tiwari, \"Assessing the Black Spots Focused Policies for Indian National Highways,\" in Proc. World Conference on Transport Research (WCTR 2019), Mumbai, India, May 26-30, 2019. [4] A. T. Fetene, \"A Comprehensive Literature Review of Traditional and Spatial Methods for Black Spot Identification in Road Crash Analysis,\" School of Civil, Hydraulic and Water Resource Engineering, University of Gondar, Ethiopia. [5] R. Chaudhari and B. Khode, \"Identification & Analysis of Black Spots on National Highways – A Current Scenario,\" IOP Conference Series: Earth and Environmental Science, vol. 1326, p. 012108, 2024. [6] C. Zhang, Y. Shu, and L. Yan, \"A Novel Identification Model for Road Traffic Accident Black Spots: A Case Study in Ningbo, China,\" School of Transportation and Logistics, East China Jiaotong University, China. [7] K. N. Thakare, B. S. Shete, and A. R. Bijwe, \"A Review on the Study of Different Black Spot Identification Methods,\" Dr. Rajendra Gode Institute of Technology and Research & Dr. Sau Kamaltai Gawai Institute of Engineering & Technology, Amravati.

Copyright

Copyright © 2026 Piyush Ghatage, Atharv Babar, Saurabh Lohar, Rajvardhan Ulape, Alfaj Mahabri, Prof. Priyanka Khopkar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.