A GIS-Based Framework for Identifying High-Risk Road Segments Using Spatial Analysis and K-Means Clustering for Road Safety Enhancement

Abstract

Road traffic accidents on national highways pose a significant public health and economic challenge in Bangladesh, necessitating systematic safety assessment. This study analyzes accident trends, contributing factors, and spatial patterns by identifying accident-prone locations (blackspots) along the Kushtia–Jhenaidah National Highway (N704). Accident data for the period 2017–2021 were obtained from nearby police stations. In addition, a cluster random sampling approach was used to conduct a questionnaire survey involving 100 participants, including drivers and general road users, to capture behavioural insights related to accident occurrence. The study integrates descriptive statistical methods, such as trend analysis and frequency distribution, with spatial techniques including severity index evaluation, Kernel Density Estimation (KDE), and hotspot analysis.The findings indicate a decline in overall accident frequency from 2018 to 2021, while fatality rates increased in 2021. Heavy vehicles, particularly trucks, were identified as major contributors to accidents, and head-on collisions emerged as the most common crash type. Key risk factors include driver inexperience, mobile phone usage while driving, overspeeding, inadequate training, and nighttime driving conditions. The analysis further reveals that individuals aged 20–40 are the most affected group, with higher fatality rates among males and higher injury rates among females.A total of 35 accident-prone locations were identified, with several segments classified as blackspots based on accident frequency, injury severity, and fatality occurrence. The study recommends targeted interventions such as driver training, infrastructure improvement, enhanced enforcement, and coordinated policy actions to improve highway safety and reduce accident risks.