A Drive Cycle for Electric Motorcycle Taxis in Nairobi, Kenya using Genetic Algorithm Micro-Trip Selection

​Drive cycles are standardised speed-time profiles used to quantify fuel consumption, tailpipe emissions, and energy efficiency across vehicle powertrains. For electric vehicles, they also underpin battery sizing, range estimation, and drivetrain design. Standardised cycles such as the WLTP and WMTC, however, do not represent the stop-and-go conditions of electric motorcycle taxis operating in sub-Saharan African cities. This paper presents a data-driven methodology for constructing a representative drive cycle from 2.45 million telemetry records collected from 293 electric motorcycles operating in Nairobi, Kenya over eight days. A strict micro-trip segmentation scheme is used in which every stationary event defines a micro-trip boundary, while macro-trips are delineated using a stop threshold of T_stop = 600s, yielding 4,904 macro-trips with a median duration of 30.0 minutes and a fleet median idle fraction of 24.4%. A 70-element Speed-Acceleration Frequency (SAF) matrix captures fleet-wide driving behaviour without cluster pre-processing. A Genetic Algorithm (GA) selects and sequences real micro-trips to minimise a composite of SAF matrix error and mean scalar error across seventeen kinematic, elevation, and energy metrics, with idle-fraction-aware target adjustments ensuring physically consistent comparison. The GA is run ten times with different random seeds; the best run achieves a mean scalar error of 0.8%, with twelve of the seventeen metrics reproduced within 2% of fleet targets. The resulting Nairobi Electric Boda-Boda Drive Cycle (NEBDC) is proposed as a paired speed-elevation specification, enabling more realistic testing of electric two-wheelers in hilly urban environments than a speed-only profile.