Exploring risky riding behaviors and road safety outcomes among motorcycle taxi riders in Dar es Salaam, Tanzania

• MRBQ adapted and validated for motorcycle taxi riders in Dar es Salaam, Tanzania. • Five-factor structure identified, including a novel contextual risk behavior domain. • The multivariate probit model jointly predicts crashes, near-crashes, and fines. • Contextual risky behaviors were the strongest predictors of all safety outcomes. • Findings support tailored interventions for LMIC urban motorcycle transport. Motorcycle taxi riders (MTRs) are central to urban mobility in low and middle-income countries (LMICs) but experience a disproportionate burden of traffic injuries. In Dar es Salaam (DSM), Tanzania, motorcycle-related crashes remain persistently high. Yet, empirical studies on behavioral risk factors among MTRs are limited. This study aimed to adapt and validate the Motorcycle Rider Behavior Questionnaire (MRBQ) and examine associations between self-reported risky riding behaviors and road safety outcomes among MTRs in DSM. A cross-sectional survey was conducted with 463 MTRs using an interviewer-administered, Swahili-translated version of the MRBQ, expanded with nine context-specific items. Principal component analysis with varimax rotation identified five behavioral domains: stunt behaviors, safety equipment use, traffic errors, traffic violations, and contextual risky behaviors. A multivariate probit model showed that traffic errors and contextual risky behaviors were strongly associated with crashes and near-crash involvement. Stunt behaviors were also positively associated with crash risk, while consistent use of safety equipment was linked to a reduced likelihood of being fined. Descriptive findings revealed near-universal helmet use but inconsistent adoption of other protective gear. Riders frequently engaged in locally common behaviors such as overloading passengers and riding on unauthorized roads. These practices appear to reflect adaptive responses to poor infrastructure, limited enforcement, and economic pressure. The findings support targeted, context-sensitive interventions aligned with Safe System principles. Future research should replicate this approach in other LMIC urban settings and explore behavior change strategies to reduce crash risk.