Analyzing the impact of motorcycle traffic on road congestion and vehicle flow

Abstract

Urban traffic systems are increasingly burdened by the rising prevalence of motorcycles, particularly in cities like Marrakech where they significantly influence traffic dynamics and congestion. This paper investigates the impact of motorcycle positioning on start-up lost time at signalized intersections, employing a comprehensive methodology that integrates real-world data collection and advanced simulation techniques. Using mobile phone cameras, traffic data were captured at key intersections, and the positioning and movements of motorcycles were analyzed using the YOLOv10 deep learning algorithm. These empirical data informed simulations carried out with the simulation of urban mobility (SUMO) tool to explore various motorcycle positioning strategies. The study reveals that motorcycles positioned close to cars exacerbate congestion, extending travel times and increasing queue lengths. Conversely, scenarios with dedicated motorcycle lanes demonstrate reduced congestion and smoother traffic flows. These findings highlight the critical role of strategic motorcycle positioning in enhancing urban traffic efficiency and suggest that dedicated motorcycle lanes could significantly improve overall traffic management.