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Crash blackspots significantly impact and, to some extent, determines the entire road network's safety level. Therefore, it is imperative to identify these blackspots and investigate the contributing factors. This becomes particularly crucial for low-income countries facing financial constraints in implementing road safety measures. Methodologically multiple studies utilised random parameter negative binomial models to predict vehicle crashes due to their ability to address unobserved heterogeneity in crash data, surpassing conventional models. However, the potential of this promising method in investigating factors influencing crash blackspots remains underutilised. This study aims to identify crash blackspots and investigates the roadway factors of such segments using the random parameters negative binomial modelling method. A three-year (2017–2019) crash data collected from the Ethiopian capital, Addis Ababa, with traffic volumes and various geometric characteristics were utilised. The model estimation results demonstrate the superiority of the random parameter negative binomial model over conventional models, showcasing its ability to reveal unobserved heterogeneity associated with road condition factors in crash blackspots. The study finds that horizontal curves and access density are significant road condition-related contributors to crash blackspots, characterised as random parameters. On the other hand, fixed-parameter influence factors include average annual daily traffic, vertical gradient, vertical curve, median width, and traffic control devices. The study highlights the need to further explore horizontal curvatures and access control as potential random parameters in crash blackspot locations. The findings may assist transportation planners/agencies in prioritising road maintenance, enhancing design standards, and implementing targeted safety interventions to improve road safety effectively. [ABSTRACT FROM AUTHOR]

Deaths and injuries resulted from road traffic crashes remain a serious problem globally, and current trends suggest that this will continue to be the case in the foreseeable future mainly in developing countries. Among diverse cause of traffic safety challenges, traffic violation has been considered as one of the noticeable contributing factors. The main aim of the study is to identify and evaluate the major traffic violation with related risk factors using multinomial logit model. Traffic violation data of Luzhou were collected from Sichuan Province Public Security Department, China. The study result revealed six major traffic violations, including traffic light violation, illegal parking, wrong-way driving, speeding, and NOT wearing a seat belt. Urban roads classified with congested driving and severe weather conditions were the major risk factors. Among different vehicle types and use, those small car/automobile categories with private purpose use exhibit statistically significant association (p-value < 0.05) with the aforementioned traffic violations. Taking into consideration these risky contributing factors during the development of traffic regulations and enforcement will help to reduce traffic violations and create a smooth/healthy driving condition with improved traffic safety and will also increase the performance of driving in general. [ABSTRACT FROM AUTHOR]