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.

在中低收入国家，摩托车骑行安全始终是备受关注的公共安全议题。本研究聚焦印度尼西亚境内摩托车骑行者的危险场景识别，以解决该领域的安全问题。本研究基于印度尼西亚布雷贝斯摄政区2020—2021年的警方交通事故数据集，通过两步聚类分析（two-step cluster analysis）与伤害分析，对摩托车交通事故展开研究。本研究采用联合展示法，将上述分析结果与104名年轻摩托车骑行者的事故自报告数据进行整合，以获取更全面的研究洞察。最终，本研究识别出四类高危交通场景：中央分隔带道路上的摩托车与机动车碰撞事故、无中央分隔带道路上的摩托车与机动车碰撞事故、摩托车与行人碰撞事故，以及单摩托车碰撞事故。针对每类场景，本研究提出了相应的干预对策，并建议将公共交通政策作为更安全的出行解决方案。本研究证实，对事故数据开展两步聚类分析，并整合事故数据与自报告分析结果的研究方法具备良好应用价值。因此，本研究呼吁在未来的相关研究中采用该创新性研究方法。