Optimization of Istanbul Urban Traffic Data with Grey Wolf Optimization-Assisted Machine Learning Techniques: Comparative Analysis of Long Short-Term Memory and eXtreme Gradient Boosting

Abstract

This study examines machine learning approaches enhanced by the Grey Wolf Optimization (GWO) algorithm for analyzing Istanbul’s complex urban traffic patterns. The GWO was employed due to its capability to efficiently handle complex nonlinear traffic patterns and its robustness in achieving global optimization, outperforming several conventional metaheuristics. Using hourly traffic density data from June–December 2024 obtained from Turkey’s Ministry of Environment database, researchers developed a Unified Traffic Density Index combining average speed (40%), vehicle count (40%), and speed variation (20%) to identify the busiest traffic zones. The
dataset was obtained from the Ministry of Environment, Urbanization, and Climate Change and covers the period from June 1, 2024, to December 31, 2024. The dataset contains hourly data for 2462 geohash regions within the boundaries of Istanbul province. The methodology compared long short-term memory (LSTM) and eXtreme Gradient Boosting (XGBoost) algorithms using both standard and GWO-optimized hyperparameters. Time series analysis separated trend, seasonality, and randomness components while examining hourly and daily periodicity patterns in traffic data. Results demonstrated that GWO optimization significantly enhanced both algorithms’ performance. The standard LSTM model’s systematic deviations and wave-like patterns were substantially reduced
through GWO optimization. The XGBoost performed consistently in both versions, with the GWO-XGBoost combination achieving superior prediction accuracy. Performance metrics revealed that GWO-XGBoost attained the lowest mean squared error (1.6209) and mean absolute error (0.9082) values while achieving the highest coefficient of determination (R2 percentage bias = ±0.8486%, outperforming other configurations. These findings indicate that the GWO-XGBoost combination shows significant potential as a highly accurate solution for traffic density prediction applications for traffic management systems within critical high-density zones of metropolitan areas like Istanbul, particularly for traffic density prediction applications. The study concludes that advanced optimization techniques are essential for addressing traffic management challenges in rapidly urbanizing cities with increasing vehicle density.

Cite this article as: N. Subaşi, "Optimization of Istanbul urban traffic data with Grey wolf optimization-assisted machine learning techniques: comparative analysis of long short-term memory and eXtreme gradient boosting," Electrica, 25, 0196, 2025. doi:10.5152/electrica.2025.25196.