Abstract:
Road traffic accidents have become a serious concern, causing millions of deaths and severe injuries annually. In the analysis of road accidents in Sri Lanka, Colombo contributes significantly. This study aims to identify and quantify the factors influencing road accidents and accident severity in the Colombo Municipal Council area using a binary logistic regression model, spatial statistics and circular statistics. The accident records were obtained from the City Traffic Police Station, Fort. The fitted model assesses the probability of a binary level of accident severity (minor, severe) based on predictors such as “Type of Day”, “Area Type”, “Day of the Week”, “Weather Condition”, “Location Type”, “Number of Vehicles Involved”, “Light Conditions”, “Weekday or Weekend”, “Time Factor”. The model was optimized using stepwise AIC selection and assessed via odds ratios and p-values. The number of vehicles involved in a collision, “Day or Night” and “Weekend or Weekday” variables are showing a positive association with the accident severity. Odds ratios suggest that accidents that occurred at night are 52.2% more likely to be serious than those during the day, and accidents on weekends are 48.1% more likely to be serious compared to weekdays. Hence, we can conclude that accident severity tends to be higher during nighttime, weekends, and weekday mornings compared to other times.
Circular statistics indicated that the frequency of the accidents is high between 7:00-8:00 a.m., 1:00-2:00 p.m., and 4:00-5:00 p.m. in a day, which may be due to the high volume of traffic before and after school hours and the end of the typical workday. Moran’s I statistic showed a positive moderate spatial autocorrelation of 0.4383, which indicates that accident counts are clustered and not randomly spread, suggesting that there is a high accident count near other high counts or a low accident count near other low counts. Spatial maps show that the highest number of accidents occurred in the Cinnamon Garden Grama Niladhari Division. These findings aim to facilitate decision-making regarding infrastructure and safety interventions that can be implemented to improve traffic safety and ultimately contribute to mitigating accidents and enhancing overall road safety in the region.
