BINARY LOGISTIC REGRESSION MODELING OF MOTORCYCLE RIDER BEHAVIOR ON THE IMPLEMENTATION 0F ELECTRONIC TRAFFIC LAW ENFORCEMENT IN PADANG CITY

Abstract

Traffic violations by motorcyclists remain a persistent problem in Indonesian cities. Padang City officially implemented an Electronic Ticketing (ETLE) system on March 23, 2021, at five priority intersections. However, violations continued to occur, indicating that the system's presence alone has not been sufficient to change motorcyclist behavior. This study aims to: 1) determine the characteristics of violations at five ETLE locations in Padang City, 2) identify factors that influence motorcyclist behavior toward ETLE implementation, and 3) obtain a binary logistic regression equation model for analyzing motorcyclist behavior toward ETLE implementation. Data were collected through observation, interviews, questionnaires, and documentation from 400 respondents (80 per location). Descriptive and binary logistic regression analyses were applied. Results show that violators were predominantly female, aged over 17 years, held a Class C driving license for more than five years, did not take the practical exam to obtain it, were in less stable emotional conditions, had never received ETLE socialization, and had never received an electronic ticket sanction. Binary logistic analysis identified two significant factors: the length of time to obtain a Class C driving license (X1) and whether the respondent took the theory/practical exam (X2), with a combined influence of 32.3% on ETLE compliance (Nagelkerke R² = 0.323). The resulting regression model is: Y = −1.137 + 1.502X1 + 1.738X2. These findings suggest that licensing quality and driver education are key levers for improving compliance with electronic traffic enforcement systems.