A predictive model of motorcycle accident involvement using structural equation modeling considering driver personality and riding behavior in Metro Manila

• Main Authors: Bathan, Aaron Paul, De Ocampo, James Carlo, Ong, Jasper Neill
• Format: text
• Language: English
• Published: Animo Repository 2017
• Subjects: Motorcycling accidents > Philippines > Metro Manila; Motorcyclists—Philippines—Psychology; Operations Research, Systems Engineering and Industrial Engineering; Personality and Social Contexts
• Online Access: https://animorepository.dlsu.edu.ph/etd_bachelors/18643
• Institution: De La Salle University

Road traffic accidents specifically ones involving motorcycles have been seen to have an upward trend wherein the studies and research on it have not. A previous study on the topic by Flores, Gotohio and Paras (2008) was the first and only study that considered linking motorcycle accidents with environmental and personal factors: age, lighting conditions, traffic movement, weather conditions, road character, junction type, time, day, surface conditions and driver behavior. The study, however, fails to expound on the concept of driving behavior as well as failed to include the personality of the driver. Multiple studies abroad have taken these factors into account and thus serves as the basis for the factor structure of the study. The independent variables of the study are driver personality and riding behavior while the dependent variable is accident involvement. The components for driver personality are altruism, anger, anxiety, normlessness, and sensation seeking, while the components for riding behavior are self-assertiveness, speeding and rule violations. The chosen method to analyze the data is Structural Equation Modeling (SEM) as it is considered the prime tool in assessing latent variables. The scope of the study would only be Metro Manila. The purpose of the study is to determine the relationships of driver personality and riding behavior factors as well as to predict accident involvement using the same factors. The model, from 8 variables, was reduced down to 5 variables in an attempt to better the model fit. The remaining variables are Anger, Normlessness, Self-Assertiveness, Speeding and Rule-violations. Tue findings of the study suggest that Normlessness has an inverse relationship with accident involvement, while Self-assertiveness, speeding, rule-violations and anger all exhibit a direct relationship. The interrelationship of the factors suggest that the following relationship have inverse relationships: Anger <-> Normlessness, Normlessness <-> Self-Assertiveness and Normlessness <-> Speeding, while the others have a direct relationship.