English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Traffic crashes involving vehicles are mainly caused by illegal driving behaviors. It is of paramount importance to mitigate traffic violation occurrences. This study positions itself to characterize the effects of contributing factors on traffic violation severity. Considering different traffic violation outcomes caused by various factors, this study selects 17 factors from the spatiotemporal, road-traffic, vehicle-driver, and environment characteristics based on 55,997 valid traffic violations. A model comparison as well as the elasticity for the optimal model (partial proportional odds model) is applied to facilitate the related interpretation. The results evidenced the significant roles of time of day, vehicle type, driver age, interference, road type, weather, lighting condition, and speed limit. The findings revealed that higher-grade roads, higher speed limits, lower visibility, more interference, and increasing traffic volumes are significantly associated with a reduction in the slight probabilities but an increase in the more severe probabilities. Older drivers with more experience are correlated with a substantial increase in the slight probabilities yet an obvious decrease in the mild probabilities. The findings could provide meaningful insights to prioritize effective related countermeasures.

Determinants of traffic violations in China: A case-study with a partial proportional odds model