2026-04-17T02:19:30Z https://meral.edu.mm/oai

oai:meral.edu.mm:recid/00009064 2024-05-27T03:54:56Z 1582963436320 1582963436320:1582965742757

Statistical Modelling of Road Traffic Accidents In Yangon (Khin Thet Tun, 2023) Khin Thet Tun Road traffic accidents constitute one of the most pressing concerns for governments worldwide. Thousands of people are fatal and injured on the roads due to accidents. This study aims to analyze and predict road traffic accidents and casualties in Yangon using data from the No. (2) Office of Traffic Police for the period from January 2013 to December 2022. Descriptive statistics show that the number of accidents increased from 2013 to 2014, but it has significantly decreased starting from 2015. The analysis of the binary logistic regression model reveals that the risk factors for traffic casualties mainly include gender, place of accident, type of vehicle, time of accident, and immediate causes of accidents. Furthermore, the bestfitting model for predicting traffic accidents was found to be ARIMAX-TFM (0, 1, 1). Similarly, ARIMAX-TFM (1, 0, 1) and ARIMAX-TFM (1, 0, 1) were the best-fitting models for traffic injury and fatality data. The forecasted number of traffic accidents and injuries is steadily decreasing, while the number of fatalities is steadily increasing for January 2023 to March 2023. Additionally, the analysis of ARIMAX-TFM confirms a significant impact of road safety measures on the reduction of the number of accidents and casualties in Yangon. To reduce road traffic accidents, traffic authorities should focus on upgrading safer driving behaviors, improving the safety features of vehicles, enforcing laws related to key risks, conducting public awareness campaigns to better understand the risks, and establishing a comprehensive strategy. 2023-09-01 https://meral.edu.mm/records/9064