Traffic Accidents Detection using Geographic Information Systems (GIS)

Abstract

The mission of reducing the number and severity of traffic accidents becomes the dominant target of road traffic safety management worldwide. The main objective of this work is to analyze traffic accidents in temporal and spatial frameworks in the capital city Amman and identify hotspot zones in the study area. Several statistical analyses are conducted using SQL to give insight into the temporal distribution of accidents and to identify the most revealing accidents based on several attributes such as the year of accidents, the severity of accidents, road type, and lighting environment which enables the authors to do further investigations on the more frequent accidents. GIS-based statistical and spatial analysis tools are utilized to examine the spatial pattern of accident distribution in the study area for three successive years, hotspots are identified for clusters of high concentrations. The Nearest Neighbor Index (NNI) is used to analyze the pattern of traffic accident distribution based on selective parameters. This was followed by identifying hotspot zones for regions that showed clustering using the optimal hotspot analysis tool. Experimental results showed clustering for all tested groups, and thus hotspots were detected for these accidents in the study area. The importance of this work is in providing a spatial understanding of accident distribution in the capital city of Amman which can help policymakers of road safety setting out efficient strategies for traffic safety management and find optimal solutions as required for factors causing such accidents.

Identifying Influencing Factors of Road Accidents in Emerging Road Accident Black spots<strong> </strong><strong> </strong>

“…The researcher developed a regression model to predict the occurrence of a fatal accident in Ahmedabad city with statistical analysis by collecting the fatal accident data and traffic volume per lane for Place Shahibaug and Karanj Bhadra, Ahmedabad, for the period of six years from 2005 to 2010. The regression model developed was found to be satisfactory with the goodness of fit and with a successful prediction rate [31]. In order to have an organized city, at the planning stage itself, we have to give special attention to vulnerable road users such as pedestrians and cyclists to reduce road accidents.…”

“…Palayamkottai is the central business center located on the eastern bank of the Thamirabarani River. The town has a number of educational institutions and administrative offices like District police headquarters, medical colleges, commercial shops, and hospitals, attracting more traffic from the outskirts of the city, causing high traffic flow in the city [50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69]. The Municipal Corporation Act of 1994 established a municipal corporation to operate Tirunelveli.…”

Contribution of a Geographic Information System to the Prevention of Crashes Among Vulnerable Road Users in the City of Cotonou: Exploratory Study

“… 17 , 19–22 A GIS is considered a tool to help prioritize interventions and efficiently allocate resources to improve road safety. 11–15 , 23 , 24 The corrective measures for the causes of road crashes then involve changes in the built environment. 25 , 26 …”

“…17,[19][20][21][22] A GIS is considered a tool to help prioritize interventions and efficiently allocate resources to improve road safety. [11][12][13][14][15]23,24 The corrective measures for the causes of road crashes then involve changes in the built environment. 25,26 In Benin, since 2008, financial investments have been made to acquire Global Positioning System (GPS) receivers and train police officers in charge of road crash reports.…”

A GIS and Fuzzy-based Model for Identification and Analysis of Accident Vulnerable Locations in Urban Traffic Management System: A Case Study of Bhubaneswar

“…Among few notable contributions towards traffic data analysis, in [3], using road accident data of UK between 2005 and 2019 for finding risk and predicting severity of accidental injuries, authors have used data analytics strategies. It can found in [4] that spatial pattern of accident distribution in a specific study area can be analyzed and accident hot spots are identified using GIS based spatial and statistical analysis tools. In [5], authors present the performance result of spatial analysis of accident prone traffic location classification using Multi-criteria Decision Making (MCDM) approach.…”