Investigating the Impact of Climate Change on Traffic Accidents in Jordan

This research studies the impact of a number of climate change features on the severity of traffic accidents in Jordan. This case study is based on monthly data collected from six meteorological stations. The relationship between climate and accident severity is delicate and hard to observe in the short term; therefore, three validation techniques were used: feature selection, correlation testing, and time series modeling. In the first two techniques, Recursive Feature Elimination with Random Forest and a correlation matrix were employed to select the features with the greatest influence on the target variable. Applying the feature selection algorithm highlighted rainfall as the feature with the greatest influence on the target variable, followed by CO 2 emissions and average temperatures. For a more precise explanation, the selected features were used as inputs for the Autoregressive Distributed Lag (ARDL) approach. Unlike conventional linear models, ARDL can handle a non-linear time series dataset, which is fit for our case study. The developed model has confirmed the positive impact of average rainfalls and mean temperatures on accident risk in the long run and CO 2 emissions in the short run. This finding highlights the importance of implementing adaptive strategies and policies to strengthen resilience against the growing effects of climate change on road safety in Jordan.