Elsevier

Spatial Statistics

Volume 42, April 2021, 100458
Spatial Statistics

Mapping road traffic crash hotspots using GIS-based methods: A case study of Muscat Governorate in the Sultanate of Oman

https://doi.org/10.1016/j.spasta.2020.100458Get rights and content

Abstract

Objective:

Road traffic crashes (RTCs) are a major global public health problem and cause substantial burden on national economy and healthcare. There is little systematic understanding of the geography of RTCs and the spatial correlations of RTCs in the Middle-East region, particularly in Oman where RTCs are the leading cause of disability-adjusted life years lost. The overarching goal of this paper is to evaluate the spatial and temporal dimensions, identifying the high risk areas or hot-zones where RTCs are more frequent, using the geocoded data from the Muscat governorate.

Data:

This study is based on data drawn from the Royal Oman Police (ROP) sample iMAAP database and the National Road Traffic Crash (NRTC) database, managed by the ROP and made available for research use by The Research Council of the Sultanate of Oman. The data covered the period from 1st January 2010 to 2nd November 2014. Only RTCs occurred in Muscat Governorate were included in the study. The study is based on 12,438 registered incidents, however, due to disconnections found on road network, RTCs occurred on disconnected parts were removed and the final analysis considered only 9,357 incidents.

Methods:

We considered an adjacency network analysis integrating GIS and RTC data using robust estimation techniques including: Kernel Density Estimation (KDE) of both 1-D and 2-D space dimensions, Network-based Nearest Neighbour Distance (Net-NND), Network-based K-Function, Random Forest Algorithm (RF) and spatiotemporal Hot-zone analysis.

Findings:

The analysis highlight evidence of spatial clustering and recurrence of RTC hot-zones on long roads demarcated by intersections and roundabouts in Muscat. The findings confirm that road intersections elevate the risk of RTCs than other effects attributed to road geometry features. The results from GIS application of NRTC data are validated using the sample data generated by iMAAP database.

Conclusion:

The findings of this study provide statistical evidence and confirm our research hypothesis that road intersections (roundabouts, crosses and bridges) represent higher risk of causing RTCs than other road geometric features. The results also demonstrate systematic quantitative evidence of spatio-temporal patterns associated with the crash risk over different locations on road networks in Muscat. More importantly, the findings clearly pinpoint the importance and influence of the road and traffic related features in road crash spatial analysis.

Introduction

Identifying the location and time of Road Traffic Crashes (RTCs) is crucial for the enforcement authorities to take effective measures to reduce the risk of RTCs (Yu et al., 2014, Benedek et al., 2016, Le et al., 2019). The heterogeneity in RTC frequencies and rates is attributed to complex roadside features, traffic and weather conditions and driving behaviours (Cheng and Washington, 2005, Harirforoush et al., 2019). Different terminologies have been used to describe high-risk RTC locations; hazardous road locations, high-risk locations, accident-prone locations, black spots, hot spots, hot zones, black zones, sites with promise and priority investigation locations (Montella, 2010, Choudhary et al., 2015, Yao et al., 2018). Past studies have no universally standard definition for hazardous road locations, which suggests that there is no clear definition or consensus of identifying crash locations (Elvik, 2008, Anderson, 2009, Choudhary et al., 2015). The major challenge, therefore, is to make judgements on the definitions and criteria for determining RTC hotspots (Miranda-Moreno et al. 2007; Elvik, 2008, Anderson, 2009).

There is little systematic understanding of the spatial patterns and correlations of RTCs in the Middle-East region, particularly in Oman, where RTCs are the leading cause of disability-adjusted life years lost. The goal of this study is to identify the locations of hot-zones (groups of neighbouring hotspots) and spatial clustering of RTCs in the Muscat governorate. Muscat is the capital of Oman and the most densely populated (345 people per km2) governorate in the country, covering more than 32% of the total population (NCSI, 2016, NCSI, 2018). Muscat is located in the north-eastern part of Oman, it represents a mix of ancient cultural heritage and modern style and it is considered as the heart of the Sultanate. Spurred by rapid economic growth and urbanisation, the use of private vehicles to commute to both short and long distances to workplace, shopping and leisure centres are becoming increasingly common in Oman, especially commuting from adjoining governorates to Muscat. This has led to an increase in the concentration of daily commuting within limited major roads, which in turn has resulted in a high level of traffic congestion coupled with a high rate of traffic crashes (annually, more than 33% of RTCs in Oman occur in Muscat) (Al-Rawas, 1993, Royal Oman Police, 2017).

Network Kernel Density (Net-KDE) estimation technique can be applied to develop an adjacency network analysis by focusing on the spatial and temporal dimensions, which is useful in identifying the high risk or hot-zone areas where RTCs are more frequent. It also identifies the significant factors affecting these spatial patterns. The identification of the so-called hot-zones or high risk areas would help transportation safety professionals and authorities to identify high-crash corridors more efficiently so that they can develop safety strategies on these hazardous locations (Harirforoush et al., 2019). Consequently, these hot-zones would have a priority to benefit from a systemic safety improvement programme including suitable road design, proper traffic control, and effective enforcement of traffic rules (Young and Park, 2014, Achu et al., 2019).

The present research addresses the following questions:

  • 1.

    Where are the high risk or hot-zone areas for road crashes in Muscat Governorate where crashes are more frequent?

  • 2.

    How can we use a GIS-based spatial analysis to understand and model the patterns of road crashes integrating relevant predictors such as road geometry and traffic related features?

Additionally, it addresses two sub-questions:

  • a.

    What factors characterise the hot-zones from normal- and cold-zones?

  • b.

    Over time, which road zones represent high risk areas for road traffic crashes in Muscat?

We hypothesise that road intersections (roundabouts, crosses and bridges) elevate the risks to RTCs than other road geometric features.

Section snippets

Data

From a statistical perspective, data of a minimum of three years are needed for any spatial analysis to obtain credible results (Benedek et al., 2016, Yao et al., 2018). This study is based on data drawn from the National Road Traffic Crash (NRTC) database and sample iMAAP database managed by the Royal Omani Police (ROP), and made available for research use by The Research Council of the Sultanate of Oman. iMAAP is implemented by ROP and supported by The Research Council under the National Road

Net-KDE

The results of the Net-KDE are presented in Fig. 4, Fig. 5, Fig. 6, Fig. 7. They are coded into different colours, so the maps provide a clear visualisation of high risk areas where there is an increase in the degree of redness of road section. Findings from the Net-KDE analysis demonstrate evidence of spatial clustering of RTC hot-zones on long roads, especially on Sultan Qaboos Highway, demarcated by intersections, and complex bridges and roundabouts. The crash-risk increases with higher

Discussion and conclusions

The identification of road crash hot-zones is pertinent to design the most effective strategies to reduce the crash density on high-risk areas. Our research aimed to: (1) identify high density crash zones in the Muscat Governorate (2) explore the characteristics of crash hot-zone, and (3) examine the spatio-temporal patterns of RTCs in the study area. It has exemplified the use of GIS in detecting hot-zones by employing a wide range of statistical techniques using data of five yeas (2010–2014)

CRediT authorship contribution statement

Amira Al Aamri (AAA) and Sabu S. Padmadas (SSP) conceptualised and prepared the writing of the original draft. AAA conducted literature review, data curation, formal analysis, funding acquisition and project administration. Graeme Hornby (GH) provided support on the methodology and data analysis including GIS application, visualisation and validation. SSP and Li-Chun Zhang (LCZ) provided overall supervision of the project and contributed to methodology and validation. Abdullah Al Maniri (AAM)

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

We are grateful to The Research Council and the Royal Oman Police for providing us access to the national road traffic accident database. We acknowledge the Ministry of Higher Education, Sultanate of Oman for funding the research project.

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