Understanding the behaviour of motorcycle riders: An objective investigation of riding style and capability

Abstract

Human errors are the primary cause of powered two-wheeler crashes worldwide due to the demanding control required and the often ineffective rider-training programs. Literature on rider behaviour is limited, partly due to the lack of standard investigation methodologies.This work investigated the differences in riding style and capability of a diverse set of riders. It explored the impact of familiarisation and riding instruction through objective metrics. Correlation with experience was a particular focus.Seven riders of various experience levels performed trials on an instrumented motorcycle, following three riding instructions: ‘Free Riding’, ‘Handlebar Riding’, and ‘Body Riding’. Objective metrics assessed rider familiarisation, capability and willingness to excite motorcycle dynamics, riding style, and input preference.Results indicated that riders asymptotically converged to their motorcycle dynamics intensity level after a specific distance; both intensity and distance were positively correlated with experience. Experienced riders achieved higher longitudinal acceleration and utilised combined dynamics to a higher degree. The negative longitudinal jerk during braking varied greatly among riders and correlated with experience. A clustering approach identified two prominent trial groups concerning the motorcycle response intensity. Higher diversity emerged in the inputs, leading to five clusters with distinct riding style meanings. Instructions influenced behaviour, particularly regarding input usage.The unsupervised approach and metrics proposed should make rider behaviour research more straightforward and objective. It could be applied to naturalistic riding sessions for more conclusive evidence of inter-driver differences. The diversity that emerged concerning the command inputs used warrants a revision of training practices to promote riding safety.

Keywords

• Powered two-wheeler rider behaviour
• Riding profiles
• Motorcycle dynamics
• G-g diagram
• Data mining
• Research methods