Evaluation of various GIS-based methods for the analysis of road traffic accident hotspot

M M Zahranel-Said,Tan Soon Jiann,Nurul Amirah ’Atiqah Binti Mohamad ’Asri Putra,Yok Hoe Yap,Ena Kartina Abdul Rahman,Eng Hie Angel Tan,A Awaludin,K Nur Rahma Putri,T Matsumoto,A Fajar Setiawan,S Pessiki,S Siswosukarto,H Jonkers

doi:10.1051/matecconf/201925803008

Abstract

In order to establish objective criteria for road traffic accident (RTA) hotspots, this paper examines the application of three different hotspot analysis methods to both identify and rank the RTA hotspots. The three methods selected are the network Kernel Density Estimation (KDE+) method, the Getis-Ord GI* method, and a recently proposed risk-based method that accounts for RTA frequency, severity and socioeconomic costs - STAA method. The study road, Jalan Tutong, is a major dual-carriageway connecting major residential and commercial areas from the west of Brunei-Muara district and beyond to the capital, Bandar Seri Begawan. The RTA data consists of cases reported to the police during a 5-year period from 2012 to 2016. The RTA data were digitised and prepared, before being imported into ESRI ArcGIS 10.2 software for analysis using each of these methods. The outcomes, particularly the location, extent and priority of the RTA hotspots, are subsequently compared to results from road safety audits, in order to determine the relative merits and drawbacks of each method. The findings from the comparative study would be useful to recommend the most suitable method to identify and rank the RTA hotspots for the study road.

Highlights

1.1 BackgroundAccording to the World Health Organisation, 1.25 million die from road traffic accidents (RTAs) every year and in most countries, RTAs cost approximately 3% of the gross domestic product [1], with generally higher RTA deaths in low- and medium-income countries and lesser in highincome countries
Time-efficient and cost-effective methods to identify RTA hotspots are in demand worldwide and with recent advances in Geographic Information System (GIS) technology, researchers in transportation are gaining leverage over this
Planar KDE has long been used to detect RTA hotspots but it has been proven that network KDE is more appropriate for network-restricted incidents, which have been illustrated by KDE+

Summary

Introduction

According to the World Health Organisation, 1.25 million die from road traffic accidents (RTAs) every year and in most countries, RTAs cost approximately 3% of the gross domestic product [1], with generally higher RTA deaths in low- and medium-income countries and lesser in highincome countries. In order to establish objective criteria to reduce RTA and improve road in the face of limited budgets, it is important to recognise how, where and when RTA occurs [4]. Understanding the spatial patterns of RTA allows road authority engineers, design consultants and maintenance teams to implement appropriate RTA reduction measures [4] and prioritise them through a ranking scheme [5]. Identifying RTA hotspots or blackspots along the road has been made easier in recent years with the integrated application of Geographic Information System (GIS) software and Global Positioning System (GPS) devices

Methods

Findings

Discussion

Conclusion