TY  - JOUR
PY  - 2019//
TI  - Analysis of vehicle accidents using spatio-temporal tools in ArcGIS; a case study of Hayatabad, Peshawar
JO  - International journal of engineering works
A1  - Rabbani, Muhammad Babar Ali
A1  - Khan, Prof Dr Sher Afzal
A1  - Iqbal, Dr Qaiser
A1  - Zaman, Engineer Qamar
SP  - 339
EP  - 344
VL  - 6
IS  - 12
N2  - Identification of traffic accident spots play a pivotal role in planning of roads and application of effective strategies in order to minimize the traffic accidents. This study puts into use the spatial distribution of the traffic accidents scattered throughout the area using spatial analysis and statistical approaches. The purpose of this research study is to analyze the traffic accidents occurring in the Hayatabad area of Peshawar. The fundamental objective of this study is to detect accidents hotspot in an observed area by a complex statistical algorithm. A methodology was developed in ArcGIS 10.2 to analyze the spatial patterns of traffic accidents and to identify hotspots. This study has conducted NNHA spatial clustering method in CrimeSTAT for the identification of hotspot clusters for accidents points in ArcGIS. Moreover, based on the detected hotspots, spatio-temporal tool like Kernel Density Estimation (KDE) analysis was performed in Crime STAT to create a temporal map of RTAs hotspots in ArcGIS. A geostatistical method known as Kriging Interpolation method (KI) was also used to assess the results computed by KDE. The results indicated that the roundabouts located in this area are the major hotspot of accidents, which includes Bagh-e-Naran roundabout, Phase-6 roundabout, Tatara Park roundabout and Jamrud road. Comparison of KDE and KI was performed and it was found that KI outperforms KDE in identifying hotspots. It has been concluded that these hotspots lacked the basic traffic controlling devices, which are necessary for controlling the speed and converging or merging of vehicles at these locations.     Keywords: Accident hotspots, spatial analysis, clustering, kernel density estimation, kriging interpolation<p />  <p>Language: en</p>
LA  - en
SN  - 2521-2419
UR  - http://dx.doi.org/10.34259/ijew.19.612439444
ID  - ref1
ER  -