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

Mapping road traffic crash hotspots using GIS-based methods: a case study of Muscat Governorate in the Sultanate of Oman
Mapping road traffic crash hotspots using GIS-based methods: a case study of Muscat Governorate in the Sultanate of Oman
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.
Clustering, Kernel Density Estimation, RTC hot-zones, Road geometry features, Road traffic crashes, Spatiotemporal modelling
2211-6753
1-26
Al Aamri, Amira K
861b9bb1-cb2d-4022-938b-a8dd742682ef
Hornby, Graeme
52fc0227-a0b1-46eb-a08f-ec689c460bf8
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
Al-Maniri, Abdullah
03f637a5-a625-4dbd-b6d6-dc74b066b7a8
Padmadas, Sabu S
64b6ab89-152b-48a3-838b-e9167964b508
Al Aamri, Amira K
861b9bb1-cb2d-4022-938b-a8dd742682ef
Hornby, Graeme
52fc0227-a0b1-46eb-a08f-ec689c460bf8
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
Al-Maniri, Abdullah
03f637a5-a625-4dbd-b6d6-dc74b066b7a8
Padmadas, Sabu S
64b6ab89-152b-48a3-838b-e9167964b508

Al Aamri, Amira K, Hornby, Graeme, Zhang, Li-Chun, Al-Maniri, Abdullah and Padmadas, Sabu S (2020) Mapping road traffic crash hotspots using GIS-based methods: a case study of Muscat Governorate in the Sultanate of Oman. Spatial Statistics, 1-26, [100458]. (doi:10.1016/j.spasta.2020.100458).

Record type: Article

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.

Text
Spatial Statistics Journal Paper - Accepted Manuscript
Restricted to Repository staff only until 2 July 2021.
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More information

Accepted/In Press date: 11 June 2020
e-pub ahead of print date: 2 July 2020
Keywords: Clustering, Kernel Density Estimation, RTC hot-zones, Road geometry features, Road traffic crashes, Spatiotemporal modelling

Identifiers

Local EPrints ID: 443160
URI: http://eprints.soton.ac.uk/id/eprint/443160
ISSN: 2211-6753
PURE UUID: be40a3ed-2556-49a9-a152-e0e7d4bbd6e4
ORCID for Graeme Hornby: ORCID iD orcid.org/0000-0002-2833-8711
ORCID for Li-Chun Zhang: ORCID iD orcid.org/0000-0002-3944-9484
ORCID for Sabu S Padmadas: ORCID iD orcid.org/0000-0002-6538-9374

Catalogue record

Date deposited: 12 Aug 2020 16:41
Last modified: 18 Feb 2021 17:22

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