Prediction of Fatal and Major Injury of Drivers, Cyclists, and Pedestrians in Collisions

Shanshal, Dalia; Babaoglu, Ceni; Başar, Ayşe

doi:10.7307/ptt.v32i1.3134

Cited by 6 publications

(2 citation statements)

References 33 publications

(36 reference statements)

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“…Land use also significantly influences cyclists' safety; bicycle accidents are more likely to happen in areas with a high population density, a variety of land uses, and lots of pedestrian traffic [13,14]. Furthermore, areas with high concentrations of cyclists and motorists can result in conflicts that lead to accidents [15,16]. Considering these threats when planning cities and neighborhoods helps to lessen the negative impact of cycling.…”

Section: Introductionmentioning

confidence: 99%

Towards a Sustainable and Safe Future: Mapping Bike Accidents in Urbanized Context

Jaber,

Csonka

2023

Safety

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This manuscript presents a study on the spatial relationships between bike accidents, the built environment, land use, and transportation network characteristics in Budapest, Hungary using geographic weighted regression (GWR). The sample period includes bike crash data between 2017 and 2022. The findings provide insights into the spatial distribution of bike crashes and their severity, which can be useful for designing targeted interventions to improve bike safety in Budapest and be useful for policymakers and city planners in developing effective strategies to reduce the severity of bike crashes in urban areas. The study reveals that built environment features, such as traffic signals, road crossings, and bus stops, are positively correlated with the bike crash index, particularly in the inner areas of the city. However, traffic signals have a negative correlation with the bike crash index in the suburbs, where they may contribute to making roads safer for cyclists. The study also shows that commercial activity and PT stops have a higher impact on bike crashes in the northern and western districts. GWR analysis further suggests that one-way roads and higher speed limits are associated with more severe bike crashes, while green and recreational areas are generally safer for cyclists. Future research should be focused on the traffic volume and bike trips’ effects on the severity index.

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Section: Introductionmentioning

confidence: 99%

Towards a Sustainable and Safe Future: Mapping Bike Accidents in Urbanized Context

Jaber,

Csonka

2023

Safety

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“…In Canada, important research is conducted on severe collisions examining its causes and impacts. The research in [15] analysed all traffic collision events in which at least one person was killed or seriously injured in Toronto. The spatio-temporal and behavioural patterns are examined using the Killed or Seriously Injured dataset covering the years from 2007 to 2017.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Fatalities in Vehicle Collisions in Canada

Babaoglu

2021

PROMET

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Traffic collisions affect millions around the world and are the leading cause of death for children and young adults. Thus, Canada’s road safety plan is to reduce collision injuries and fatalities with a vision of making the safest roads in the world. We aim to predict fatalities of collisions on Canadian roads, and to discover causation of fatalities through exploratory data analysis and machine learning techniques. We analyse the vehicle collisions from Canada’s National Collision Database (1999–2017.) Through data mining methodologies, we investigate association rules and key contributing factors that lead to fatalities. Then, we propose two supervised learning classification models, Lasso Regression and XGBoost, to predict fatalities. Our analysis shows the deadliness of head-on collisions, especially in non-intersection areas with lacking traffic control systems. We also reveal that most collision fatalities occur in non-extreme weather and road conditions. Our prediction models show that the best classifier of fatalities is XGBoost with 83% accuracy. Its most important features are “collision configuration” and “used safety devices” elements, outnumbering attributes such as vehicle year, collision time, age, or sex of the individual. Our exploratory and predictive analysis reveal the importance of road design and traffic safety education.

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