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 which is 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:

The analysis 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 the research hypothesis that 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 network in Muscat. More importantly, the findings clearly pinpoint the importance and influence of the road and traffic related feature in road crash spatial analysis.

中文翻译：

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使用基于GIS的方法绘制道路交通撞车热点的地图：以阿曼苏丹国马斯喀特省为例

目的：

道路交通事故（RTC）是全球主要的公共卫生问题，给国民经济和医疗保健造成沉重负担。在中东地区，特别是在阿曼，RTC是导致残疾调整生命年的主要原因，在阿曼，对RTC的地理位置和RTC的空间相关性缺乏系统的了解。本文的总体目标是评估空间和时间维度，使用马斯喀特省的地理编码数据来确定RTC更为频繁的高风险区域或热点区域。数据：本研究基于皇家阿曼警察（ROP）样本iMAAP数据库和由ROP管理的国家道路交通事故（NRTC）数据库得出的数据，这些数据可供阿曼苏丹国研究理事会研究使用。该数据涵盖了从2010年1月1日到2014年11月2日的时间。该研究仅包括马斯喀特省发生的RTC。这项研究基于12438个已记录的事件，但是，由于在道路网络上发现断开连接，断开连接部分上发生的RTC被删除，最终分析仅考虑了9357个事件。

方法：

该分析考虑了使用强大的估算技术将GIS和RTC数据集成在一起的邻接网络分析，包括：一维和二维空间维的核密度估计（KDE），基于网络的最近邻居距离（Net-NND），基于网络的基于K函数，随机森林算法（RF）和时空热区分析。研究结果：该分析突出了在马斯喀特的交叉路口和环形交叉路口划定的长途道路上，RTC热区的空间聚类和复发的证据。研究结果证实，道路交叉口比其他归因于道路几何特征的影响会增加RTC的风险。使用iMAAP数据库生成的样本数据验证了NRTC数据在GIS中的应用结果。

结论：

这项研究的发现提供了统计依据，并证实了研究假说，即与其他道路几何特征相比，道路交叉口（环岛，十字路口和桥梁）代表产生RTC的风险更高。结果还证明了与马斯喀特道路网络不同位置上的碰撞风险相关的时空模式的系统定量证据。更重要的是，这些发现清楚地指出了道路交通相关特征在道路碰撞空间分析中的重要性和影响。