Unveiling Motorcyclist Behaviour Using Naturalistic Riding Study

Abstract

Motorcyclists represent a significant portion of road users in Malaysia, where motorcycle-related accidents contribute disproportionately to road fatalities and injuries. Traditional data collection methods, such as crash reports and simulations, often lack the contextual detail and behavioural nuance necessary to understand rider risk exposure. This study employs the Naturalistic Riding Study (NRS) approach to collect and analyze the riding behaviour of motorcyclists in urban and suburban areas in the Klang Valley region, Malaysia. To facilitate data collection, an Internet-of-Things (IoT) system is developed to detect real-time riders' behaviour. This paper illustrates the development of the system and presents some data collected during the pilot test.