Factors affecting the accident size of motorcycle-involved crashes: a structural equation modeling approach

Hazardous traffic scenarios for motorcyclists in Indonesia: a comprehensive insight from police accident data and self-reports

Motorcycle safety remains a concern in low- and middle-income countries. This study addresses this issue by identifying hazardous scenarios for motorcyclists in Indonesia. We conducted a two-step cluster analysis and injury analysis to examine motorcycle accidents based on the police accident dataset (2020-2021) of Brebes Regency, Indonesia. We integrated the findings with accident self-reports from 104 young motorcyclists using a joint display to obtain a more comprehensive insight. As a result, we identified four hazardous traffic scenarios: motorcycle-to-vehicle collisions on median roads, motorcycle-to-vehicle collisions on non-median roads, motorcycle-to-pedestrian collisions, and single-motorcycle collisions. We suggest countermeasures for each scenario and propose a public transport policy as a safer mobility solution. Applying a two-step cluster analysis on accident data and integrating the findings of accident data and self-report analysis proved beneficial in this study. Therefore, we encourage the use of this novel approach in future studies.

Investigating speed-safety association: Considering the unobserved heterogeneity and human factors mediation effects

The relationship between mean speed and crash likelihood is unclear in the literature. The contradictory findings can be attributed to the masking effects of the confounding variables in this association. Moreover, the unobserved heterogeneity has almost been criticized as a reason behind the current inconclusive results. This research provides an effort to develop a model that analyzes the mean speed-crash frequency relationship by crash severity and type. Also, the confounding and mediation effects of the environment, driver, and traffic-related attributes have been considered. To this end, the loop detector and crash data were aggregated daily for rural multilane highways of Tehran province, Iran, covering two years, 2020-2021. The partial least squares path modeling (PLS-PM) was employed for crash causal analysis along with the finite mixture partial least squares (FIMIX-PLS) segmentation to account for potential unobserved heterogeneity between observations. The mean speed was negatively and positively associated with the frequency of property damage-only (PDO) and severe accidents, respectively. Moreover, driver-related variables, including tailgating, distracted driving, and speeding, played key mediation roles in associating traffic and environmental factors with the crash risk. The higher the mean speed and the lower the traffic volume, the higher odds of distracted driving. Distracted driving was, in turn, associated with the higher vulnerable road users (VRU) accidents and single-vehicle accidents, triggering a higher frequency of severe accidents. Moreover, lower mean speed and higher traffic volume were positively correlated with the percentage of tailgating violations, which, in turn, predicted multi-vehicle accidents as the main predictor of PDO crash frequency. In conclusion, the mean speed effects on the crash risk are entirely different for each crash type through distinct crash mechanisms. Hence, the distinct distribution of crash types in different datasets might have led to current inconsistent results in the literature.

Analyzing traffic violations among motorcyclists using structural equation modeling

In developing countries, the motorcycle is one of the main modes of transportation, especially in the downtown area. Motorcyclists' safety is a common issue since they have more vulnerability for severe crashes. Thus, it is necessary to decrease the probability of encountering high-risk situations. The main reason for motorcycle crashes is human errors in the form of traffic violations. This article aims to identify the role of seven factors on traffic violations, including motorcyclist characteristics, road conditions, environmental circumstances, riding skills, cycling statuses, motorcycle age and previous negative experiences such as fines and accidents. The structural equation modeling was applied to evaluate the impact of these factors pertinent to motorcyclists' crashes. To assess these factors, 600 motorcyclists have been interviewed in Kerman, Iran. The results indicated that motorcyclists' characteristics are the most effective factor in violation commitment. Since most motorcyclists are young, with low income and education, it is necessary to pay more attention to their training and education before giving a cycling license. In addition, those with more previous crashes and violations are more susceptible to committing violations. This relates to the lack of enough control and enforcement in developing cities; also, it shows that the current traffic fines are not deterrent enough. Finally, considering the results of this research can help to minimize traffic violations among motorcyclists, which is a step towards safer roads.