Review of road accident analysis using GIS technique

Shahzad, Monib

doi:10.1080/17457300.2020.1811732

Cited by 23 publications

(6 citation statements)

References 44 publications

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“…According to Giang Le et al [11], SI and Comap analyses determined the relatively similar hotspots on their study, but they also found that rankings of some hotspots might be different due to the integration of accident SI, easily investigated, which is also approved in this study. Early studies pointed out several multidimensional techniques and methods, and particularly the Kernel density estimation, that have been implemented for GIS-based road accident analysis [10]. However, our analysis indicated that the Kernel density estimation as a standalone method for road accident mapping might face some sort of uncertainty and ambiguity regarding the precise location of a road crash, which is also acknowledged in some studies, e.g., [11,59].…”

Section: Discussionmentioning

confidence: 70%

“…It is widely known that the number of road crashes is significantly growing worldwide. In this domain, within the transportation sector, road accident outcomes increasingly result in the loss of lives and injuries [10]. Road traffic accidents are known as one of the most complicated issues all over the world [11].…”

Section: Introductionmentioning

confidence: 99%

“…leads us to develop spatiotemporal patterns of road accidents efficiently. Early studies pointed out that the trend of using data assessment and GIS spatial and network analysis as an effective methodology for road traffic mapping [10]. It ought to be indicated that it is growing tremendously and this trend of evaluating traffic accidents is more often employed by world researchers [24,25].…”

Section: Introductionmentioning

confidence: 99%

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A GIS-Based Spatiotemporal Modelling of Urban Traffic Accidents in Tabriz City during the COVID-19 Pandemic

et al. 2022

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The main aim of the present study was to investigate the spatiotemporal trends of urban traffic accident hotspots during the COVID-19 pandemic. The severity index was used to determine high-risk areas, and the kernel density estimation method was used to identify risk of traffic accident hotspots. Accident data for the time period of April 2018 to November 2020 were obtained from the traffic police of Tabriz (Iran) and analyzed using GIS spatial and network analysis procedures. To evaluate the impacts of COVID-19, we used the seasonal variation in car accidents to analyze the change in the total number or urban traffic accidents. Eventually, the sustainability of urban transport was analyzed based on the demographic and land use data to identify the areas with a high number of accidents and its respective impacts for the local residences. Based on the results, the lockdown measures in response to the pandemic have led to significant reductions in road traffic accidents. From the perspective of urban planning, the spatiotemporal urban traffic accident analysis indicated that areas with high numbers of elderly people and children were most affected by car accidents. As we identified the hotspots of urban traffic accidents and evaluated their spatiotemporal correlation with land use and demography characteristics, we conclude that the results of this study can be used by urban managers and support decision making to improve the situation, so that fewer accidents will happen in the future.

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Section: Discussionmentioning

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A GIS-Based Spatiotemporal Modelling of Urban Traffic Accidents in Tabriz City during the COVID-19 Pandemic

et al. 2022

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“…GIS can be used for crash analysis to examine spatial characteristics of accident locations. Shahzad (2020) proposed GIS techniques to display statistical data in geographic form and identify road crashes hotspots. Budzyński et al (2018) described the use of GIS for visualization of road parameter data to identify highrisk sections.…”

Section: Literature Reviewmentioning

confidence: 99%

GIS techniques to analyze factors associated with crash occurrence

Sagar¹,

Stamatiadis

2022

TSR

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Fatality rates in Kentucky have been higher than the national average for the past decade. Prior research postulated that the region’s unique socioeconomic conditions could provide a compelling explanation of the higher cash rates of the southeast in the US. This study examines the relationship between safety and socioeconomic characteristics using an extensive spatial analysis of crashes in Kentucky. Quasi-induced exposure technique was utilized to determine the crash propensity of different driver groups. Through a series of GIS techniques and spatial analysis, the relative accident involvement ratios were calculated for each group of drivers. The findings of the study concur with the previous findings between driving behavior and demographic factors such as age and gender. The study also attempted to explain the regional disparities in crash occurrence across the state in terms of economic status. The study concluded that the drivers residing in the Appalachian regions have a higher propensity to cause a crash, regardless of the age and gender of the driver. The findings of the study can be used to identify high risk counties for the Safety Circuit Rider (SCR) program in Kentucky.

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“…For this purpose, the geographic information system (GIS) is a suitable platform. This system provides the most demanding tools required to analyze RTA and road design that can be noteworthy in achieving road safety [41], manages different types of databases [42], includes data analysis methods [43], provides a suitable platform for big data management [10], and has been widely used as the base platform of many road safety research so far [44]. Generally, application of GIS in road safety analysis includes spatial modeling of accident risk [45,46], spatial and spatiotemporal analyzing of accidents [47,48], extraction of accident hotspots [44,49], preparing accidentrisk map [10,50], identifying spatiotemporal patterns of accidents [51], spatiotemporal clustering of road accidents [52], and exploring the relationships between affective factors and accident rates [53,54].…”

Section: Paper Machine Learning Applicationmentioning

confidence: 99%

Evaluation of Tree-Based Machine Learning Algorithms for Accident Risk Mapping Caused by Driver Lack of Alertness at a National Scale

et al. 2021

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Drivers’ lack of alertness is one of the main reasons for fatal road traffic accidents (RTA) in Iran. Accident-risk mapping with machine learning algorithms in the geographic information system (GIS) platform is a suitable approach for investigating the occurrence risk of these accidents by analyzing the role of effective factors. This approach helps to identify the high-risk areas even in unnoticed and remote places and prioritizes accident-prone locations. This paper aimed to evaluate tuned machine learning algorithms of bagged decision trees (BDTs), extra trees (ETs), and random forest (RF) in accident-risk mapping caused by drivers’ lack of alertness (due to drowsiness, fatigue, and reduced attention) at a national scale of Iran roads. Accident points and eight effective criteria, namely distance to the city, distance to the gas station, land use/cover, road structure, road type, time of day, traffic direction, and slope, were applied in modeling, using GIS. The time factor was utilized to represent drivers’ varied alertness levels. The accident dataset included 4399 RTA records from March 2017 to March 2019. The performance of all models was cross-validated with five-folds and tree metrics of mean absolute error, mean squared error, and area under the curve of the receiver operating characteristic (ROC-AUC). The results of cross-validation showed that BDT and RF performance with an AUC of 0.846 were slightly more accurate than ET with an AUC of 0.827. The importance of modeling features was assessed by using the Gini index, and the results revealed that the road type, distance to the city, distance to the gas station, slope, and time of day were the most important, while land use/cover, traffic direction, and road structure were the least important. The proposed approach can be improved by applying the traffic volume in modeling and helps decision-makers take necessary actions by identifying important factors on road safety.

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Review of road accident analysis using GIS technique

Cited by 23 publications

References 44 publications

A GIS-Based Spatiotemporal Modelling of Urban Traffic Accidents in Tabriz City during the COVID-19 Pandemic

A GIS-Based Spatiotemporal Modelling of Urban Traffic Accidents in Tabriz City during the COVID-19 Pandemic

GIS techniques to analyze factors associated with crash occurrence

Evaluation of Tree-Based Machine Learning Algorithms for Accident Risk Mapping Caused by Driver Lack of Alertness at a National Scale

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