Application of geographical information system (GIS) in reducing accident blackspots and in planning of a safer urban road network: A review

Singh, Nishant; Katiyar, Sunil Kumar

doi:10.1016/j.ecoinf.2021.101436

Cited by 15 publications

(13 citation statements)

References 11 publications

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“…The key factors that influence the frequency and severity of road accidents are poor transport (road) infrastructure, driver education, natural and built environmental features such as trees and buildings leading to visibility issues, car speed and vehicle overloading (such as trucks carrying too much weight), and poor road safety management (such as lack of accurate speed limits and inadequate numbers of road safety officers) [3].…”

Section: B Factors Influencing Road Accidentsmentioning

confidence: 99%

“…GIS techniques can be applied to find hotspots, which can be further categorized as emerging, sporadic, persistent, consecutive, intensifying or diminishing depending on their characteristics [9]. Identification of such hotspots is vital for road planners to reduce the number of potential road accidents [3,[10][11][12].…”

Section: Blackspots Blind Spots and Hotspotsmentioning

confidence: 99%

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Geospatial Factors Applied to Road Accidents: A Review

Watson,

Ryan

2024

JAIT

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Road accidents are a major source of trauma worldwide due to increasing numbers of vehicles and drivers. Considerable data has been collected on road accidents and this is frequently published as open data on the internet. These datasets include parameters such as accident type and number of fatalities as well as environmental variables such as road type, demographics, and area infrastructure. Geospatial analysis provides a means of understanding spatial factors that influence road accidents such as the built infrastructure, natural environment features such as hills and vegetation, traffic volume, and road design and construction. Geospatial visualization techniques can also help identify hotspots and blackspots. The Moran I, Getis-Ord, and Kernel Density Estimation techniques are the most commonly-used geospatial tests and their application is reported in many papers. This paper provides a review of geospatial factors that are relevant to road accidents.

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Section: B Factors Influencing Road Accidentsmentioning

confidence: 99%

Section: Blackspots Blind Spots and Hotspotsmentioning

confidence: 99%

Geospatial Factors Applied to Road Accidents: A Review

Watson,

Ryan

2024

JAIT

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“…In [4], GIS for transport (GIS-T) applications integrated with mathematical models are used to analyze the public transportation in Oradea (Romania) and its impact on urban traffic. GIS-based software is commonly employed for land-use suitability analysis [5], [6], and for road network generation and analysis [7], [8]. In the field of sustainable mobility, and particularly for bicycle transport infrastructure, the use of GIS is consolidated.…”

Section: Related Workmentioning

confidence: 99%

GIS-Based Geospatial Data Analysis: the Security of Cycle Paths in Modena

Bachechi

Degliangeli

2022

2022 IEEE International Smart Cities Conference (ISC2)

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The use of fossil fuels is contributing to the global climate crisis and is threatening the sustainability of the planet. Bicycles are a vital component of the solution, as they can help mitigate the effects of climate change and improve the quality of life for all. However, cities need to be equipped with the necessary infrastructure to support their use guaranteeing safety for cyclists. Moreover, cyclists should plan their route considering the level of security associated with the different available options to reach their destination. The paper tests and presents a method that aims to integrate geographical data from various sources with different geometries and formats into a single view of the cycle paths in the province of Modena, Italy. The Geographic Information System (GIS) software functionalities have been exploited to classify paths in 5 categories: from protected bike lanes to streets with no bike infrastructure. The type of traffic that co-exists in each cycle path was analysed too. The main outcome of this research is a visualization of the cycle paths in the province of Modena highlighting the security of paths, the discontinuity of the routes, and the less covered areas. Moreover, a cycle paths graph data model was generated to perform routing based on the security level.

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“…Currently, road accidents are one of the world's most critical problems, being a cause of concern and an object of study in many countries [1][2][3][4][5]. According to the World Health Organization (WHO), road accidents are the eighth leading cause of death in the world (https: //www.who.int/violence_injury_prevention/road_safety_status/2018/en/, accessed on 30 January 2023), and Brazil has the fifth most violent traffic globally (https://apps.who.int/ iris/bitstream/handle/10665/276462/9789241565684-eng.pdf?ua=1, accessed on 30 January 2023).…”

Section: Introductionmentioning

confidence: 99%

“…Several studies are addressing this problem since it does not affect only Brazil, making the accident data analysis field more popular [3,4,6,7]. The main objectives of these studies include understanding the risk factors contributing to accidents and creating measures to reduce such accidents.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for Classifying Road Accident Hotspots

Amorim

Firmino

Baptista

et al. 2023

IJGI

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Road accidents are a worldwide problem, affecting millions of people annually. One way to reduce such accidents is to predict risk areas and alert drivers. Advanced research has been carried out on identifying accident-influencing factors and potential highway risk areas to mitigate the number of road accidents. Machine learning techniques have been used to build prediction models using a supervised classification based on a labeled dataset. In this work, we experimented with many machine learning algorithms to discover the best classifier for the Brazilian federal road hotspots associated with severe or nonsevere accident risk using several features. We tested with SVM, random forest, and a multi-layer perceptron neural network. The dataset contains a ten-year road accident report by the Brazilian Federal Highway Police. The feature set includes spatial footprint, weekday and time when the accident happened, road type, route, orientation, weather conditions, and accident type. The results were promising, and the neural network model provided the best results, achieving an accuracy of 83%, a precision of 84%, a recall of 83%, and an F1-score of 82%.

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Application of geographical information system (GIS) in reducing accident blackspots and in planning of a safer urban road network: A review

Cited by 15 publications

References 11 publications

Geospatial Factors Applied to Road Accidents: A Review

Geospatial Factors Applied to Road Accidents: A Review

GIS-Based Geospatial Data Analysis: the Security of Cycle Paths in Modena

A Machine Learning Approach for Classifying Road Accident Hotspots

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