A novel approach to set driving simulator experiments based on traffic crash data

Bobermin, Mariane Paula; Ferreira, Sara

doi:10.1016/j.aap.2020.105938

Cited by 8 publications

(4 citation statements)

References 32 publications

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“…Several studies have been launched to investigate traffic accident data using various approaches. In 2021, M. Bobermin et al suggested a novel framework based on Clustering Analysis for the definition of driving simulator experiments [2]. Amir Mohammadi Amiri et al (2021) used five different hotspot identification algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Step 2: Employing Clustering Analysis: Clustering analysis is a technique for categorizing cases into comparable important groups based on their unique characteristics. The agglomerative mode of hierarchical clustering algorithms divides every cluster into small sub clusters or assembles them into super clusters on a regular basis [2]. In a hierarchical architecture known as a dendrogram, the connection between each pair of clusters is determined by the medium of dissimilarity or similarity.…”

Section: Responsible Factorsmentioning

confidence: 99%

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Identify Discriminatory Factors of Traffic Accidental Fatal Subtypes using Machine Learning Techniques

Loskor¹,

Ahamed²

2022

IJACSA

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In today's world, traffic accidents are one of the main reasons of mortality and long-term injury. Bangladesh is no exception in this case. Several vehicle accidents each year have become an everyday occurrence in Bangladesh. Bangladesh's largest highway, the Dhaka-Banglabandha National Highway, has a significant number of accidents each year. In this work, we gathered accident data from the Dhaka-Banglabandha highway over an eight-year period and attempted to determine the subtypes present in this dataset. Then we tested with various classification algorithms to see which ones performed the best at classifying accident subtypes. To describe the discriminatory factors among the subtypes, we also used an interpretable model. This experiment gives essential information on traffic accidents and so helps in the development of policies to reduce road traffic collisions on Bangladesh's Dhaka-Banglabandha National Highway.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Responsible Factorsmentioning

confidence: 99%

Identify Discriminatory Factors of Traffic Accidental Fatal Subtypes using Machine Learning Techniques

Loskor¹,

Ahamed²

2022

IJACSA

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“…Several crash-based models have been applied to investigate factors affecting safety performance of roads and associated intersections: Examples of these crash models are: Poisson's regression, negative binomial regression, nested logit regression, and multiple linear regression [8][9][10][11][12][13]. These crash-based models investigate how certain factors, such as geometric road and operational conditions, influence the safety performance of road intersections or the road itself [41,42].…”

Section: The Model Employedmentioning

confidence: 99%

“…Several theoretical models have been employed in the previous studies to help identify the factors that affect the safety performance of roads. Examples of theoretical models are: Poisson's regression, negative binomial regression, Nested Logit regression, and multiple linear regression [8][9][10][11][12][13]. These models are collectively known as crash-based models, and they can predict crashes on highways and intersections [14][15][16][17][18].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

The Effect of Geometric Road Conditions on Safety Performance of Abu Dhabi Road Intersections

Alghafli

Mohamad

Ahmed

2021

Safety

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Abu Dhabi’s government has taken several initiatives to improve the safety of the city’s highways, such as reducing traffic accident occurrence, red light violations, and property damage associated with traffic accidents. However, the intersections are still associated with many severe accidents, property damage associated with accidents, and red light violations. To help authorities pinpoint the major contributors to the deterioration of the safety performance of the signalized junctions, this study employed a negative binomial regression model to investigate the effect of geometric road conditions (in terms of the number of lanes of streets found in four-leg, three-leg, and different types of intersection) on property damage, red light violations, and severe accidents as safety performance indicators. The research found that, in both three-leg and four-leg intersections, most accidents and traffic light violations occur when minor streets pass through the intersection. This can be solved by converting these minor streets into major streets by increasing the number of lanes. This way, the capability of the minor street’s handling of increased traffic from the major streets significantly increases. The results also show that traffic speed is a major contributor to safety performance deterioration in these intersections. This can be solved by posting speed limits at these intersections. Results have also shown that, in Direction 1, when the main street passes through the intersection, the property damage type of accident is significantly high. This may be due to the presence of a potentially hazardous property or road design flaws on that side of the road. This effect needs further investigation to determine the hazardous property or the road design flaws causing these accidents.

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Calculating Driving Behaviour Score Based on Driving Background

Nadimi,

Sheikh Hosseini Lori,

Arun

et al. 2024

Int J Civ Eng

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A novel approach to set driving simulator experiments based on traffic crash data

Cited by 8 publications

References 32 publications

Identify Discriminatory Factors of Traffic Accidental Fatal Subtypes using Machine Learning Techniques

Identify Discriminatory Factors of Traffic Accidental Fatal Subtypes using Machine Learning Techniques

The Effect of Geometric Road Conditions on Safety Performance of Abu Dhabi Road Intersections

Calculating Driving Behaviour Score Based on Driving Background

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