Cyclist Injury Severity in Spain: A Bayesian Analysis of Police Road Injury Data Focusing on Involved Vehicles and Route Environment

Abstract

This study analyses factors associated with cyclist injury severity, focusing on vehicle type, route environment, and interactions between them. Data analysed was collected by Spanish police during 2016 and includes records relating to 12,318 drivers and cyclist involving in collisions with at least one injured cyclist, of whom 7230 were injured cyclists. Bayesian methods were used to model relationships between cyclist injury severity and circumstances related to the crash, with the outcome variable being whether a cyclist was killed or seriously injured (KSI) rather than slightly injured. Factors in the model included those relating to the injured cyclist, the route environment, and involved motorists. Injury severity among cyclists was likely to be higher where an Heavy Goods Vehicle (HGV) was involved, and certain route conditions (bicycle infrastructure, 30 kph zones, and urban zones) were associated with lower injury severity. Interactions exist between the two: collisions involving large vehicles in lower-risk environments are less likely to lead to KSIs than collisions involving large vehicles in higher-risk environments. Finally, motorists involved in a collision were more likely than the injured cyclists to have committed an error or infraction. The study supports the creation of infrastructure that separates cyclists from motor traffic. Also, action needs to be taken to address motorist behaviour, given the imbalance between responsibility and risk.

Exploiting Machine Learning Algorithms for Predicting Crash Injury Severity in Yemen: Hospital Case Study

“…According to the contributing factors, this study shows that road type, crash type, road user, accident action, characteristics of road type, and collision partners had a similar impact on road accidents in different vehicles in other countries [27,46] The predicted leadings causes (environmental and infrastructure causes): the behavior of road users (cyclist and pedestrians) identified as common causes, the activity during an accident, characteristics of road-type, ways of transport crash injuries to the hospital, collision type, vehicle type (motors are exposing their users to high risks of crash injuries and fatalities) and road shoulder condition; detected to have a significant impact in vehicle crash injury severity. Some of these predicted environmental and infrastructure leading causes, and others are similar to those predicted in the cyclist crash severity in Spain [46]. The factors that are related to the injured situation have an impact on injury severity scoring, and it can be recognized from Tables 1 and 8.…”

Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach

“…Logistic regression has been proven to be effective and widely used in measuring the relationships between binary injury outcomes, as the link function can transfer a linear function into a continuous probability function ranging from 0 to 1 [ 1 , 2 , 3 , 20 , 21 ]. Let denote the outcome of CC severity .…”

“…With the increasing number of traffic crashes occurring in the past decade, both researchers and practitioners in the road safety field have been focusing on topics such as crash risks, severity levels, and safety behaviors to propose prevention measures for alleviating social and economic losses [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. Some scholars are focused on investigating the risk factors and severity levels of crashes in specific scenarios, as the mechanisms of crashes may vary considerably in different situations, e.g., with different traffic conditions, involving different numbers of vehicles, or caused by different at-fault driver groups.…”

Trend analysis and fatality causes in Kenyan roads: A review of road traffic accident data between 2015 and 2020

“…should be included for further analysis. This recommendation is informed by parameters used in analysis by researchers to make statistical inferences and modeling (Yousefzadeh-Chabok et al, 2016), (D. Wang et al, 2019), (Aldred et al, 2020). Further, the data reporting should be standardized to comply with international standards, e.g., following the recommended quantifying RTD as deaths within 30 days.…”