Accident Hotspot Identification and Safety Countermeasure: A Case Study of the Sirhind-Khanna Road Corridor, Punjab, India

Abstract

The threat of road traffic accidents remains a significant issue in road safety and transportation especially in such developing nations where these road collisions are becoming serious due to the growing numbers of vehicles on the roads and rapid urbanization in developing cities and towns coupled by inadequate road infrastructure. The present research is centered around the identification and prioritization of the accident hot spots on Sirhin- Khanna Road corridor in the state of Punjab, India, with the help of an integrated multi-method analytical approach. This study used data concerning five years of accidents (2020-2024) gathered by traffic police and highway authorities. The entire corridor length of 20 km was subdivided into equally sized sections of 500 m each for easier detailed analysis. In total six analytical methods were used in the project: Accident Frequency Method, Weighted Severity Index (WSI), Crash Severity Index (CSI), Critical Rate Method, Moving Average Method and Accident Density Method. The analysis showed that there were spatial clustering and not uniform distribution of incidents occurring along the corridor. The areas deemed to be the most critical accident-prone areas were repeatedly identified as segments S22, S28, S15 and S36. The most crashes, 34, were reported on Segment S22, which had the highest WSI value of 68, representing very high accident conditions. It also revealed that traffic control, non-traffic conditions on the roadway (e.g., over-speeding, mixed traffic operations, roadside commercial activity, inadequate traffic control, poor roadway geometry) are significant factors involved in the accident occurrence. Carrying out the analysis and verifying it in the field, engineering, enforcement, and educational countermeasures were then suggested to increase corridor safety. This study shows that combining several analysis methods gives more reliable and accurate identification of the hot spots and offers a working procedure for road safety management and transportation planning.

Introduction

Road transport is essential for economic development but is increasingly affected by rising traffic accidents due to rapid motorization, poor infrastructure, mixed traffic, and unsafe driving behavior. Accident hotspots (or black spots) are locations where crashes occur more frequently or with higher severity, and identifying them is crucial for targeted road safety interventions.

The study aims to identify these hotspots using multiple analytical methods instead of relying on a single approach, since individual methods (like only frequency-based or severity-based analysis) have limitations. The research also highlights that combining methods improves accuracy in hotspot detection.

The methodology covers a 20 km corridor on NH-44, divided into 40 segments of 500 meters each. Accident data from 2020–2024 was collected from traffic police and highway authorities, including crash location, severity, vehicle type, time, weather, and causes. Field surveys were also conducted to assess road conditions, traffic behavior, and infrastructure.

Four main hotspot identification methods were applied:

• Accident Frequency Method: Identified segments like S22, S28, and S15 as high-risk based on crash counts.
• Weighted Severity Index (WSI): Incorporated accident severity (fatal, serious, minor), showing S22 as the most dangerous segment.
• Crash Severity Index (CSI): Highlighted locations with not only frequent but also severe crashes, again identifying S22, S15, S28, and S36 as critical.
• Critical Rate Method (CRM): Considers traffic exposure to determine whether crash rates are statistically higher than expected.

Conclusion

A detailed multi-methods study for identification of an accident hotspot on Sirhind-Khanna Road corridor in Punjab, India was presented. The analysis used five years of accidents data (2020-2024) to be taken through six analytical methods which were Accident Frequency Method, Weighted Severity Index, Crash Severity Index, Critical Rate Method, Moving Average Method, and Accident Density Method. The results showed that the incidence of accidents along the corridor was very clustered and localized. High accident frequencies, high accident severities along with statistically significant accident frequencies and consistently ranked within the list of critical accident areas, segments S22, S28, S15, and S36 were identified as critical accident areas. The safety score for each segment was calculated, with Segment S22 having the top crash frequency and the highest WSI score highlighting major safety issues. The study also showed that using several analytical methods resulted in more reliable and accurate identification of hotspots than a single analytical method. Over speeding, mixed traffic, lack of traffic control, roadside commercial activity, roadway geometry, poor, lack of pedestrian facility were the major contributing factors identified. From the results, several engineering, enforcement, and educational solutions were recommended to increase the safety along the corridor. The study offers practical and reliable tools for transportation safety analysis and accident hotspot prioritization in situations with limited data. Spatial analysis using GIS, intelligent transportation systems, machine learning techniques for advanced accident prediction and pro-active road safety management can be integrated in future research.

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Copyright

Copyright © 2026 Telema J. Thomas, Er. Heena Manzoor. 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.