An Ontology-Based Traffic Accident Risk Mapping Framework

Wang, Jing; Wang, Xin

doi:10.1007/978-3-642-22922-0_3

Cited by 17 publications

(11 citation statements)

References 22 publications

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“…Numbers of strategies have evolved, proposed and adopted, including spatial autocorrelation methods and kernel analysis methods. For its global acceptability and ease of computation, the Kernel analysis method was adopted in this work (Wang and Wang, 2011).…”

Section: Methodsmentioning

confidence: 99%

“…Where, = density estimation value at location (x,y); n = is the number of observations; r = is the smoothing parameter called bandwidth (is the search radius, only points within r are used to estimate); K = is the kernel function, it is the distance between the location (x,y) and location of the ith observation (Wang, 2012). The raster cells with high values indicate the accident concentration areas.…”

Section: Methodsmentioning

confidence: 99%

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Spatial Analysis of Accident Spots Using Weighted Severity Index (WSI) and Density-Based Clustering Algorithm

Olusina¹,

Ajanaku²

2017

Journal of Applied Sciences and Environmental Management

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This study is based on evaluation of various factors that cause road accidents in Lagos Metropolis. Fourteen (14) factors/drivers were identified which tend to influence an occurrence of accident on Lagos roads. In order to study the pattern of accidents in the Metropolis, several different spatial and non-spatial datasets were collected, processed and analysed. Weighted Severity Index (WSI) was created based on these factors/drivers. Also, Density-based Clustering for Traffic Accident Risk (DBCTAR) was carried out to assist in ascertaining the distribution of Black Spots Severity (BSS). Results obtained include: shortestpath analysis, service area analysis, accident spot severity and vulnerability level across the Metropolis based on the Weighted Severity Index (WSI). The study reveals that, accidents are not always caused as a result of bad roads but by the smoothness of roads such as expressway with accident spots distance band of 300m-500m and 3.6% of the accidents is as a result of pedestrians avoiding the use of pedestrian bridges/aid even when they are available. © JASEM https://dx.doi.org/10.4314/jasem.v21i2.22

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Spatial Analysis of Accident Spots Using Weighted Severity Index (WSI) and Density-Based Clustering Algorithm

Olusina¹,

Ajanaku²

2017

Journal of Applied Sciences and Environmental Management

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“…It estimates over 1 million people are killed each year in road collisions, which is equal to 2.1% of the annual global mortality, resulting in an estimated social cost of $518 billion [2]. In Canada, approximately 3,000 people are killed every year on the roads [3]. The previous traffic safety studies show that, in most accident cases, the occurrences of traffic accidents are rarely random in space and time.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Accident Severity Rules Mining Using Fuzzy Granular Decision Tree

Moghaddam

Wang

2014

Lecture Notes in Computer Science

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Road accident is a disaster that vocalizes a major cause of disability, untimely death and the loss of human lives. Therefore, investigating the condition of road accidents for prediction and prevention purposes on highways is significant. In this paper, we propose a new fuzzy granular decision tree to generate the road accident rules applying the discrete and continuous data stored in accident databases. Among all critical factors in the occurrence of traffic accidents, environmental factors and road design (geometry) are considered in this study. This method establishes an optimized fuzzy granular decision tree with the minimum redundancy and road accident severity classification using fuzzy reasoning. California highways were considered as the case study to examine the proposed approach. The experimental results demonstrate that the proposed method is approximately 16% more accurate than the fuzzy ID3 method with less redundancy in constructing the decision tree.

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“…A few other solutions proposed for traffic accident modeling and prediction are clustering techniques such as K-nearest neighbors [66], [48], C-means clustering [66], DBSCAN [67], case-base reasoning [68], [69] and ontologies [67].…”

Section: Other Techniques For Traffic Accident Modeling and Predictionmentioning

confidence: 99%

“…An improved version of density-based spatial clustering of applications with noise (DBSCAN) that takes into consideration both the number of accidents and their severity level is proposed in [67]. The authors also propose an ontology-based traffic accident risk mapping framework, in which the ontology represents the domain knowledge related to the traffic accidents and supports the data retrieval based on users' requirements.…”

Section: Other Techniques For Traffic Accident Modeling and Predictionmentioning

confidence: 99%

A Framework for Traffic Collision Prediction Using Historical Accident Information and Real-Time Sensor Data: A Case Study for the City of Ottawa

Yanes¹,

Enrique²

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According to recent studies, beyond being a major worldwide problem with huge economic impact, traffic collisions are poised to become as well one of the most important leading causes of death. Proactive traffic enforcement and intervention should be based on a thorough analysis on the collision data available to identify leading causes of accidents, the most prone locations as well as to predict the conditions for collision occurrence. This thesis presents a novel framework for collision prediction that takes into consideration historical and real-time factors, such as weather, geospatial information and social event data that can be obtained with existing sensor technology. A prototype is proposed, implemented and evaluated for the city of Ottawa, Canada, to predict: (1) accident frequency (collision vs nocollisions) and (2) accident severity (in terms of fatal, injury and property damage only accidents). The best performance was achieved in both cases using gradient boosted trees.iii

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An Ontology-Based Traffic Accident Risk Mapping Framework

Cited by 17 publications

References 22 publications

Spatial Analysis of Accident Spots Using Weighted Severity Index (WSI) and Density-Based Clustering Algorithm

Spatial Analysis of Accident Spots Using Weighted Severity Index (WSI) and Density-Based Clustering Algorithm

Vehicle Accident Severity Rules Mining Using Fuzzy Granular Decision Tree

A Framework for Traffic Collision Prediction Using Historical Accident Information and Real-Time Sensor Data: A Case Study for the City of Ottawa

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