APPLICATION OF BINARY LOGISTIC REGRESSION WITH TIME BASED SAMPLING IN ANALYSIS OF RISK FACTORS FOR MOTORCYCLE TRAFFIC VIOLATIONS IN MEDAN CITY

Graceya Zagita Manik(1*), Irgie Attaurrazaq(2), Donni Ramadhan Siregar(3), Katrin Jenny Sirait(4)


(1) Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Sumatera Utara
(2) Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Sumatera Utara
(3) Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Sumatera Utara
(4) Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Sumatera Utara
(*) Corresponding Author

Abstract


Traffic violations by motorcyclists are a major contributor to accidents in Medan City. This research sought to examine the frequency of violations and the factors affecting the likelihood of such violations among riders at three intersections in Medan City: Dr. Mansyur, Setia Budi, and Fly Over Jamin Ginting. A cross-sectional quantitative design and time-based sampling was used. Observations were conducted over three days in two sessions (daytime 02:00–03:00 PM and afternoon 04:00–05:00 PM WIB), with a sample of 540 motorcyclists. The dependent variable was violation status; independent variables included gender, motorcycle type, rider status, and observation time. Binary logistic regression was applied. Results showed a violation rate of . Simultaneously, all independent variables had a significant effect (p<0.001). Partially, only rider status was significant (p<0.001; OR=2.728), meaning riders with a passenger were 2.789 times more likely to violate than solo riders. Gender, motorcycle type, and observation time were not significant. The model fitted well (Hosmer–Lemeshow test, ). In conclusion, rider status is the main factor, so supervision should focus on riders with passengers.

Keywords


traffic violations; binary logistic regression; Time-Based Sampling; motorcycle; Medan City

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DOI: https://doi.org/10.26714/jsunimus.14.1.2026.32-42

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