A Comprehensive Study of Deep Learning Approaches for Predicting Reciprocal Traffic Dynamics and Climate Variability - Abrar Ali; Wadhah R. Baiee (Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ)
Climate change requires innovative solutions to improve traffic management and safety in transportation systems. This paper tries to expand on the complex relationship between weather, traffic, climate, and an integrated approach to traffic data. In the round, the general aim is to come up with the entire understanding of the relationship between weather and traffic, analyzing the way weather interacts with traffic and the way that traffic interacts with weather. It attempts to consider climate change through the consideration of the variables of extreme temperatures and precipitation, among others. It also looks into the work of traffic forecasting in the traffic models by highlighting the emissions and environmental impacts on environment-related accidents, the impacts of transportation systems on the nature of all living organisms, and a detailed table that aids in the clear comparison of different methods with the literature reviewed to understand the details of the interactions of the complex elements. That includes the elements of importance, such as the data sources, methods, and the advantages, along with some of the shortcomings or limitations that were found in them. Finally, it explains the challenges that the researchers had to face. The main findings from our study also suggest that the traffic forecasting patterns by deep learning models may contribute to 15% improvement over the traditional statistical method. In addition, the significant impact of extreme weather events on traffic flow was found; for instance, heavy precipitation events can lead to a 30% decrease in speed and increased accident rates up to 20%. Climate variability integrated into traffic models increases the prediction of long-term traffic trends by 12%, justifying the significance of the influence of climate factors in traffic management systems