Human error in motorcycle crashes: A methodology based on in-depth data to identify the skills needed and support training interventions for safe riding

OBJECTIVE: Human error by either rider or other vehicle driver is the primary contributing factor in crashes involving powered-two-wheelers. A human-factor-based crash analysis methodology is key to enhancing the road safety effectiveness of rider training interventions. Our aim is to define a methodology that uses in-depth data to identify the skills needed by riders in the highest risk crash configurations to reduce casualty rates. METHODS: The methodology is illustrated through a case study using in-depth data of 803 powered-two-wheeler crashes. Seven types of high-risk crash configuration based on pre-crash trajectories of the road-users involved were considered to investigate the human errors as crash contributors. Primary crash contributing factor, evasive maneuvers performed, horizontal roadway alignment and speed-related factors were identified, along with the most frequent crash configurations and those with the greatest risk of severe injury. RESULTS: Straight Crossing Path/Lateral Direction was the most frequent crash configuration and Turn Across Path/Opposing Direction was identified as that with the highest risk of serious injury. Multi-vehicle crashes cannot be considered as a homogenous category of crashes to which the same human failure is attributed, as different interactions between motorcyclists and other road users are associated with both different types of human error and different rider reactions. Human error in multiple-vehicle crashes differed between those configurations related to crossroads and those related to rear-end and head-end crashes. Both single-vehicle crashes and multi-vehicle head-on crashes frequently occur along curves. The involved collision avoidance maneuvers of the riders differed significantly among the highest risk crash configurations. The most relevant lack of skills are identified and linked to their most representative context. In most cases a combination of different skills was required simultaneously to avoid the crash. CONCLUSIONS: The results contribute to understand the motorcyclists' responses in high-risk crash scenarios. The findings underline the need to group accident cases, beyond the usual single-vehicle versus multi-vehicle collision approach. The different interactions with other road users should be considered to identify the competencies of the motorcyclists needed to reduce crash risks. Our methodology can be applied to increase the motorcyclists' safety by supporting preventive actions based on riders' training and eventually ADAS design.