A novel hybrid MAX-MIN Ant System and Artificial Bee Colony algorithm for generating representative bus driving cycles: A case study for Modena and comparison with Markov chain Monte Carlo

Standardized test cycles often misrepresent real-world vehicle performance by neglecting unique regional driving patterns. Localized cycles are created to solve this, but their effectiveness is dictated by the generation technique itself. This paper introduces a novel hybrid algorithm to improve upon these techniques, demonstrating its application by developing and validating a representative driving cycle for urban buses in Modena, Italy, from actual GPS data. Two distinct stochastic methodologies were implemented for this purpose: the established Markov Chain Monte Carlo (MCMC) technique and a novel hybrid metaheuristic, termed MMAS-ABC, which to the authors' knowledge, represents the first-ever integration of the MAX-MIN Ant System (MMAS) and the Artificial Bee Colony (ABC) algorithm. The representativeness of the generated cycles was rigorously evaluated against the original aggregated driving data using ten key performance parameters, speed-acceleration distributions, and total trip distance. Results indicate that while both methods produced cycles closely reflecting the original data, the hybrid MMAS-ABC approach demonstrated superior accuracy, achieving an overall average percentage difference of just 0.76 % across ten key performance parameters compared to 1.16 % for the MCMC method. The developed Modena Bus Driving Cycle (MBDC) offers a more precise basis for future local vehicle energy consumption and emission studies, and the proposed hybrid MMAS-ABC methodology presents an effective and adaptable framework for driving cycle generation in other urban contexts.