Prototype development of mobile application in assessing commuting accident risk amongst commuters in the Klang Valley, Malaysia

Introduction: Globally, commuting accident risks are always neglected in an organization while most of companies are advancing in Industry 4.0, where technology has enabled the development of innovative products and services that enhance the efficiency and enjoyment of our daily lives. In conjunction with that, there is a need to assess the impact of commuting accidents based on sociodemographic, human, vehicle, road, and environmental factors and to find suitable and effective mitigation strategies to alleviate the associated undesirable outcomes. Methodology: This research was designed to develop a mobile application to assess commuting accident risk levels using artificial intelligence principles, as we are now in the 21st-century technology era. The app was developed by Android Studio and natively used Java languages. A total of 216 respondents from private and government industries participated in this study. The sample population in this study was commuters at Klang Valley from various industries and companies, including private and government sectors, based on Schedule 1, stated in OSHA 1994 using a simple random sampling. Respondents were provided with questionnaires and a link to download the application. The mobile application was tested for indoor testing by relevant organizations to ensure the mobile application function accordingly to all categories of commuters. The mobile application's effectiveness and correlation between each factor's subelement with the risk levels have been analysed. Results: There was a significant difference between with and without mobile applications in determining the level of commuting risks, and the effectiveness was proven with a (p-value = 0.001) at a 95% confidence interval. Chi-square analysis showed that some variables such as driving skill, time-travelling, and travelling distance were insignificant where the p-value was greater than alpha level (0.05). Besides, working industry, e-hailing status, lifestyle, medication intake, alcohol consumption, health status, driving behaviour, type of transportation, vehicle service timing, tyre condition, travelling in bad weather, road condition, road obstacles, and traffic variables shows that the was a significant difference with commuting of risk levels where p-value smaller than α value (0.05) at 95% confidence interval. Conclusion: A mobile application is successfully developed and it showed there is a significant difference between risk predicted and risk calculated by the mobile application. Therefore, the mobile application is provenly effective to reduce commuting accidents. In addition, the Commurisk mobile application can be an indicator for an organization to monitor exposure risk levels amongst the workers as well as to propose effective solutions.

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