Impacts of Climate Changes on Traffic Flows Using Geospatial Data Analysis
Abstract
This chapter explores the complex interplay between climate change and the accuracy of traffic flow predictions, focusing on the crucial use of geospatial data analysis. The potential effects of extreme weather events, such as heavy precipitation, storms, and heat waves, on traffic patterns should be considered to improve the robustness of traffic management systems. In this study, the authors demonstrate the effectiveness of geospatial data analysis in considering climatic and environmental variables to improve the accuracy of traffic flow forecasts. By integrating data into predictive models, we provide tangible evidence of the impacts of climate change on urban traffic patterns. The results obtained from data and simulations on machine learning models such as Lasso regression, random forest, XGboost and LTSM gave us very good results. prediction performance on the random forest with a correlation coefficient of 0.94; an RMSE of 265 and a MAE of 279 thus demonstrating its effectiveness for predicting traffic flow.