Abstract
Traffic flow analysis is essential for improving transportation services, campus accessibility, road safety, and congestion management. Previous studies have widely applied statistical analysis, probability models, machine learning, and traffic visualization to understand vehicle movement and predict congestion; however, limited small-scale studies have examined campus-related traffic routes using simple probability-based methods that can directly support student safety and service operations. This study aims to analyze traffic flow and estimate congestion probability on the road section between the Jl. Ki Hajar Dewantara and Mekarmukti–Lemah Abang Junction and the Center Point Jababeka roundabout, specifically along the route from the Student Boarding House to President University. Vehicle count data, mainly cars, were manually collected over time and analyzed using Python. Descriptive statistics, including mean, variance, standard deviation, maximum, and minimum values, were calculated, while congestion probability was determined using a threshold of 12 vehicles per minute. Matplotlib was used to visualize traffic flow patterns, vehicle arrival frequency, and probability distribution. The results showed an average traffic flow of 7.78 vehicles per minute, variance of 13.85, standard deviation of 3.72, maximum of 15 vehicles, minimum of 1 vehicle, and a congestion probability of 16.67%. These findings indicate moderately stable traffic conditions with occasional congestion. The study contributes a practical probability-based approach for campus traffic monitoring, although it is limited to one road section and one observation period. Future research should use broader datasets and predictive modeling to improve traffic management and student safety services.
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