Understanding traffic crash seasonal and spatial variations is crucial for improving road safety. While several studies have explored crash seasonality, there needs to be more comprehensive research focusing on cold regions like North Dakota, which face unique challenges due to extreme weather conditions. This study analyzes how traffic crash variables vary with seasonal and spatial factors and forecasts traffic crash occurrence. The results revealed seasonal variations, with winter weather conditions significantly affecting crash frequency, underscoring the critical influence of environmental factors. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model was the best fit and accurate model. However, the SARIMA with exogenous variables (SARIMAX) model emerged as the superior choice when endogenous variables exist. This highlights the importance of considering relevant predictors in forecasting traffic crashes. The findings could help to implement targeted traffic safety strategies and take proactive measures.
Analyzing Spatial and Temporal Traffic Crash Dynamics across Rural and Urban Areas of North Dakota
International Conference on Transportation and Development 2025 ; 2025 ; Glendale, Arizona
05.06.2025
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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