Akademik Veri Yönetim Sistemi
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, cilt.26, sa.8, ss.12600-12614, 2025 (SCI-Expanded)
The increasing depletion of fossil fuels and growing environmental concerns are increasing the need for energy efficient and sustainable solutions, particularly in transport. At this point, especially in public transport, electric vehicles (EVs) offer a promising alternative; however, issues such as range anxiety and energy efficiency require comprehensive solutions. This study introduces the Gauss-based Trolleybus Energy Consumption Model (GTECM) for electric buses, harnessing real-time big data to mitigate range anxiety and enhance energy efficiency. This model employs Gaussian Process Regression to a large-scale dataset including 100,000 entries collected over six months in Türkiye. With an overall Root Mean Square Error (RMSE) of 0.013905, GTECM substantially outperforms linear approaches across Türkiye’s primary routes, exhibiting route-specific RMSE values between 0.28117 and 0.30540. Empirical findings suggest potential energy savings of up to 50%, alongside a 10% extension in driving range, thereby mitigating an estimated 4,220 tons of CO2 and 129.88 tons of NO2 emissions annually. Moreover, the projected amortization period for diesel-to-electric bus conversion stands at 6.83 years, underscoring GTECM’s pragmatic utility for sustainable urban transit optimization. The findings of the study can form the basis for future research and guide policy makers and urban planners in the development of more efficient and sustainable transport networks.