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Exploring the cause and effect of hazardous events such as traffic accident is vital to the society. Statistical analyses have been a great help in terms of understanding and making inference on the cause-effect analysis and also predicting the occurrence of the accident in the future. One of the issues that could not be handled by the conventional way of statistical modelling is the interrelationships exist between the variables in the data set. With the advent of technology and the wide application of machine learning algorithm, this goal can be achieved through the Bayesian network analysis, in which it is a directed acyclic probabilistic graphical model. By using Hill Climb (HC) and Tabu algorithms, the structure of the data was learnt and their relationship is estimated through the conditional probability based on the Bayes’ theorem. We found that that weather does impact on the accident occurred through the lighting condition and the traffic system. It is also learnt that fatality accidents have a higher likelihood to occur in head-on, turn over and out of control accidents. The use of Bayesian network allows for the probability queries which is very important estimates needed as we want to know what is the risk that we face given the information that we have in hand.  

In its bid to become a developed nation in a few years’ time, Malaysia has to consider various prevailing socio-economic and sociotechnical issues in the country. In the transportation sector per se, the ELV policy and initiative is one of the lacking parts in the country’s automotive ecosystem – in which a successful ELV program will not only cater the environmental concern but also help the safer car initiative for road users. This particular paper discusses what is regarded as the preliminary findings on the ELV policy from the Malaysia’s automotive ecosystem study database. From a total of 484 respondents, 268 or 55.4% had agreed to the proposal to introduce an age limit for passenger vehicles in Malaysia. The majority of those who gave their nod to the policy choose 10 years of vehicle age as the limit (38.9%), and a staggering 79.8% of them supposed that the age limit should be between 5 to 10 years. Further analysis based on the Multiple Logistic Regression found out that from a total of nine important variables related to car users’ profile and ownership status, the significant predictors to “the agreement to introduce vehicle age limit” were age, income and car status (new or used). Thus, this finding might be beneficial for the policymakers to strategize the ELV policy that sooner or later should be implemented in the Malaysia’s “developed country” environment.

Exploring the cause and effect of hazardous events such as traffic accident is vital to the society. Statistical analyses have been a great help in terms of understanding and making inference on the cause-effect analysis and also predicting the occurrence of the accident in the future. One of the issues that could not be handled by the conventional way of statistical modelling is the interrelationships exist between the variables in the data set. With the advent of technology and the wide application of machine learning algorithm, this goal can be achieved through the Bayesian network analysis, in which it is a directed acyclic probabilistic graphical model. By using Hill Climb (HC) and Tabu algorithms, the structure of the data was learnt and their relationship is estimated through the conditional probability based on the Bayes’ theorem. We found that that weather does impact on the accident occurred through the lighting condition and the traffic system. It is also learnt that fatality accidents have a higher likelihood to occur in head-on, turn over and out of control accidents. The use of Bayesian network allows for the probability queries which is very important estimates needed as we want to know what is the risk that we face given the information that we have in hand.

Background Little is known about the prevalence and factors influencing red light running in Malaysia and the relation to the growing number of intersection related crashes. Objective To examine the prevalence and identify the factors associated with red light running at selected intersections in Malaysia. Methods Four intersections with high rate of accidents were selected as observation sites. Observations were conducted during peak hour (7:00–21:00) and off-peak (14:00–16:00) on a randomly selected day of the week excluding the weekends. Traffic volumes, traffic light violations, type of vehicles, time of day and cycle length of the traffic light were recorded. Results In total, out of 5090 vehicles observed, 12.04% (n=613) violated the red light. It was found that drivers facing short cycle length (less than 120 s) were more likely to run red lights. Intersections with fixed-timed traffic lights recorded 1.5 times more cases of red light running compared to intersections with vehicle-actuated traffic lights. Motorcyclists were 4.32 times more likely to run the red light compared to other drivers. No significant differences were observed in the number of red light running during peak hour and off-peak. Conclusion It was found that red light running were significantly related to the cycle length (p<0.05), types of traffic light (p<0.01) and types of vehicle (p<0.01). This study suggests the implementation of suitable engineering countermeasures and automated enforcement to reduce the number of red light running in Malaysia.

In Malaysia, wheremotorcycles are often used as a family vehicle, children tend to travel as pillion riders at an early age, most commonly sat in front of the rider, either on the fuel tank or in the carrying basket, or alternatively behind the rider. This study aims to determine the possible mismatches between individual lower limb dimensions of Malaysian primary students and motorcycle pillion seat. An anthropometric survey was carried out on a sample of male and female school children aged 7-9 years (N=233), to elucidate on the anthropometric parameters of Malaysian children. A set of seen body dimensions covering most of the lower extremity, relevant to the design of riding pillion on a motorcycle were considered. Additionally, an investigation of foot-foot peg gap and knee opening length on a static motorcycle test rig was also measured. There is a significant vertical difference between child pillion riders’ feet relative to motorcycle foot pegs. The maximum height of students who were not able to reach the foot pegs was 1263 mm with a mean of 1137 mm, which is similar with student volunteers’ age 7 years old (mean = 1160 mm). Stature influences the centre of gravity and stability of motorcycle, especially during cornering. This anthropometric analysis could be used to design ergonomic-oriented motorcycles which will not only suit the small stature of child pillion riders, but also improve the level of comfort.