This study aims to determine the types of hazards and crash risks facing courier riders during delivery trips by recording the riding scenarios on their actual delivery route. A digital camera and a hands-free camera harness were used to hold the camera at the chest level to record the riding scenarios. Fifteen courier riders in the Klang Valley, Malaysia participated in the study. The final analysis reveals that a courier rider encounters 30 hazardous riding events and 5 near misses on average for each hour of delivery trips. Two-thirds of all hazardous riding events were instigated by road users, including the participants themselves. Interestingly, the participants' own riding behaviours contributed to almost a third (29%) of the total near misses. Obstruction of view was found to increase the odds of causing a near miss by 4.61 times compared to hazards related to driving behaviours of other motorists. Further, incidents related to lane changing or overtaking manoeuvres were found to have 7.81 times higher odds of causing a near miss compared to incidents related to braking or sudden stopping. The classification of hazards and risk assessment presented in this study should be seriously considered for better operation management and defensive driving training.

This review article aimed to analyse existing literature regarding the roles and performance of professional driving instructors (PDIs) in novice driver education (DE). A systematic classification scheme was adopted to analyse identified articles to determine the study context of PDIs in novice DE, the competency level of PDIs in relation to experienced and learner drivers and the contributions of PDIs to the novice driver learning process. A total of 14 original research articles were identified, with no systematic reviews or meta-analyses available. Overall, all of the articles were found to be inadequate in providing an in-depth understanding of the roles and performance of PDIs in novice DE. There is an urgent need to improve current understanding of the roles of PDIs in novice DE and to work towards an internationally recognised PDI management approach.

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Road traffic fatality is a burden towards low- and middle-income countries including Malaysia. Seeing that Selangor has the highest number of road traffic fatalities in Malaysia for the year 2019, therefore the state is selected as a case study. The aim of the article is 1) to understand the road traffic crash pattern and road traffic fatality pattern in Selangor 2) to determine the ability of 16 road traffic features in classifying road traffic fatality occurrence. The preliminary data screening shows that road traffic crash patterns and road traffic fatality patterns in Selangor have many similarities. However, both of them also have few dissimilarities such as crash time of occurrence, day of occurrence, number of vehicles involved in a crash, and type of vehicle first hit for the crash. Supervised machine learning algorithm in Orange data mining software was considered in this analysis. The analysed algorithms among others are neural network, random forest, decision tree, logistic regression, naïve Bayes, and support vector machine. Neural network was seen as the best algorithm to classify road traffic fatality occurrence with 97.0% classification accuracy outperform other algorithms. The result of the article can be used by the relevant traffic stakeholders to execute safety intervention in a more focused manner in Selangor to reduce the number of road traffic fatalities.

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