Development of the Traffic Offense Traits Scale (TOTS) for Recidivism Risk Assessment

Masae KOJIMA (Nagoya University)･Kojiro SHOJIMA (National Center for University Entrance Examination)･Kazuko OKAMURA (National Research Institute of Police Science)･Chieko NISHIZAWA･Shota MATSUBAYASHI (Toyota Technical Development)･Kazunori BAN (TOYOTA TECHNICAL DEVELOPMENT CORPORATION / Nagoya University)

This report developed the Traffic Offense Traits Scale (TOTS) to enable early identification of repeat traffic offenders and effective intervention. Exploratory factor analysis (EFA) with general drivers from a web survey and confirmatory factor analysis (CFA) with traffic offenders from a course designed for minor traffic offenders established a 24-item scale with a seven-factor structure. The scale demonstrated good reliability and validity across both samples, confirming its applicability for assessing psychological characteristics underlying repeated traffic offenses.

Analysis of Characteristics of Repeated Traffic Offenders

Shota Matsubayashi (Toyota Technical Development)･Masae Kojima (Nagoya University)･Chieko Nishizawa･Kazunori Ban (Toyota Technical Development)

We analyzed the relationship between the characteristics of repeated minor traffic offenders and the details of their offences. The results showed that characteristics such as lack of cautiousness were related to specific types of offences. Their patterns in these characteristics varied among offenders, suggesting that the presence of qualitatively different groups within the population. We discussed the need to improve the training course based on the offenders' characteristics.

Analysis of Self-evaluation and Traffic Behavior in Traffic Offender Course

Chieko Nishizawa (Toyota Technical Development)･Masae Kojima (Nagoya University)･Shota Matsubayashi･Kazunori Ban･Eisuke Kobayashi･Kazunori Ban (Toyota Technical Development)

To reduce traffic accident risks, it is essential to make drivers recognize these risks as their own concern and encourage behavioral change. In this study, we implemented an educational program incorporating simulator-based training and hazard prediction training in a course designed for minor traffic offenders. This report presents the results of analysis examining changes in self-evaluation and traffic behavior following educational program.

Development of a Method for Evaluating Muscle Burden During Driving Operations
-Steering Emergency Avoidance Maneuver-

Akito Onami･Takeru Higuchi･Naoya Yamakawa (Suwa University of Science)･Masashi Makita (Teikyo University)･Hiroshi Kuniyuki (Suwa University of Science)

In this study, a muscle burden assessment method using dumbbells to stably evaluate driver muscle burden during emergency avoidance maneuvers via steering. The results indicated that EMG measurements during lifting of a fixed-weight dumbbell, which provides a load equivalent to that required for driving operations, can estimate maximum muscle capacity and stably evaluate muscle burden during steering operation.

Survey on Naturalistic Driving Behavior of Motorcycles on Hilly and Mountainous Roads

Shun Nagao･Shion Sato･Yuta Katayama･Hiroshi Kuniyuki (Suwa University of Science)

In this study, the naturalistic driving behavior of riders on hilly and mountainous roads was investigated to examine factors contributing to motorcycle single-vehicle accidents. The results revealed differences in motorcycle body lean angle and rider upper body movement depending on the direction and curvature of curves. The way the center of gravity of the motorcycle body and upper body was balanced against curves was different. This difference in driving behavior is considered one of the factors contributing to accidents.

Risk assessment of collisions between high-velocity runaway vehicles and motorcycles/motorized bicycles on public roads
-Risk assessment based on the personal injury level of motorcycle/motorized bicycle riders-

Yasufumi Se (Fukuyama University)

Accidents caused by high-velocity runaway vehicles include not only vehicle-to-vehicle collisions and pedestrian collisions, but also accidents in which motorcycles and motorized bicycles are victims. This paper analyzes traffic accident statistics data on motorcycles and motorized bicycles and attempts to numerically clarify the risk of accidents caused by high-velocity runaway vehicles based on the relationship between injury severity and collision velocity.