Traffic Prediction System for Avenida Cero Using Fuzzy Logic and Parallel Computing
Keywords:
Fuzzy logic, parallel computing, vehicular traffic, prediction, intelligent systems, Python, scikit-fuzzyAbstract
Vehicular congestion in border cities such as Cúcuta represents a significant urban challenge due to its direct impact on mobility, travel time, productivity, and environmental pollution. Traditional prediction models based on statistical techniques often present limitations when dealing with the uncertain, nonlinear, and highly variable nature of traffic, especially in contexts influenced by dynamic factors such as weather conditions, day type, and time-of-day variations. This study proposes the design and implementation of an intelligent system capable of predicting traffic levels on Avenida Cero in Cúcuta by integrating fuzzy logic and parallel computing, two complementary approaches that enable the modeling of uncertainty and the optimization of execution times. A Mamdani-type Fuzzy Inference System (FIS) was developed using Python and the scikit-fuzzy library, incorporating input variables such as time of day, weather, and day type, and producing as output the traffic level categorized as low, medium, or high. Membership functions were defined according to representative local traffic patterns, and a rule base was constructed to capture linguistic relationships among the influencing factors. To enhance computational efficiency, the rule evaluation process was parallelized using Python’s multiprocessing module, allowing operations to be distributed across multiple processor cores. Test cases based on real and simulated scenarios demonstrated that the system generates coherent and stable predictions, while the parallel version reduced execution time by approximately 30% to 65%, depending on the number of rules and processes employed, achieving speedup behavior consistent with Amdahl’s Law. These results show that combining fuzzy logic with parallel computing constitutes a robust, efficient, and viable approach for vehicular traffic prediction in urban environments where uncertainty, variability, and the demand for rapid response render traditional methods insufficient. Furthermore, the proposed approach highlights the potential of intelligent systems to support decision-making processes related to mobility management, traffic control, and urban planning, offering a scalable and adaptable solution for cities facing congestion-related challenges.
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