The time series dataset analyzed in the below assignment is Traffic Crash Reports recorded in the event of a Cincinnati Police Department response to a traffic crash, which includes records of data, injury and non-injury crashes. The dataset is updated on a daily basis and is available to the public through the city’s Open Data portal.
The data-generation process is performed by recording the event of CPD responding to a traffic crash. The variation in variable (number of crashes) is reliant on a number of factors - traffic patterns, weather, attention of the rider, road conditions and many more. In my opinion, this is a difficult variable to forecast, owing to the complexity of traffic dynamics and the influence of external factors. Furthermore, having different types of crashes makes it more difficult to predict an identifiable pattern.
Several models - ARIMA, Meta-Prophet, Seasonal ARIMA were developed and it was concluded that the ARIMA model performs the best based on RMSE, MAPE and MAE values.
ARIMA PROPHET TREND ANALYSIS