An analysis of the novice motorcyclist crashes in Taiwan

OBJECTIVE: Motorcycles comprised over 60% of motor vehicles in Taiwan. There were still many motorcycle crashes in Taiwan, especially among young riders. This study investigated the characteristics of novice motorcyclist crashes in Taiwan over the period January 2011 to December 2016. Various risk factors affecting the severity of novice motorcyclist crashes, such as the rider characteristics, licensing conditions, and the environment, were examined.

METHODS: To model the count data with multiple crash severities, several regression models were considered. The multinomial logit (MNL) model, ordered logit (OL) model, and partial proportional odds (PPO) model were chosen and investigated for the relationships between the severity of novice motorcyclist crashes and potential risk factors.

RESULTS: The results showed that the novice rider who was underage or unlicensed had a higher probability of a fatal crash. Male sex, helmet use, drinking, college student, frontal impact, urban or dry road, and daytime all played significant roles in novice motorcyclist crashes.

CONCLUSIONS: Taiwan traffic safety needs further policy adjustments and public education toward novice motorcycle crashes. Adequate driving training and providing a user-friendly environment for novice riders could help. Taiwan should consider graduated driver licensing systems for skill-building and riding supervision for new motorcyclists.

Medienart:

E-Artikel

Erscheinungsjahr:

2022

Erschienen:

2022

Enthalten in:

Zur Gesamtaufnahme - volume:23

Enthalten in:

Traffic injury prevention - 23(2022), 3 vom: 22., Seite 140-145

Sprache:

Englisch

Beteiligte Personen:

Jou, Rong-Chang [VerfasserIn]
Chao, Ming-Che [VerfasserIn]

Links:

Volltext

Themen:

Journal Article
Multinomial logit model
Novice motorcyclist
Ordered logit model
Partial proportional odds model

Anmerkungen:

Date Completed 01.04.2022

Date Revised 01.05.2022

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1080/15389588.2022.2026937

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM337235546