A machine learning approach to predicting and mitigating traffic congestion

Abstract

Traffic congestion has notable effects on urban mobility, impacting thousands of people on a daily basis which hampers economic productivity and environmental sustainability. This research represents an extensive approach to address the multifaceted affairs of traffic jams through data analysis, machine learning modeling and prediction analysis. This research emphasizes four key dimensions. Such as traffic patterns, data preprocessing, model implementation and result analysis. This research starts by diving deep into the complex dynamics of the urban traffic jam, recognizing the crucial challenges such as outdated infrastructure, suboptimal traffic signal synchronization, and the unstable navigation system exemplified by Google Maps. Through diligent data exploration, data preprocessing, temporal features which we fetched from the dataset which enable a deeper understanding of traffic congestion patterns and temporal dependencies. By developing a robust machine learning model, leveraging the Random Forest Regressor, we have predicted the number of vehicles across four junctions. The Model class framework summarizes deferent preprocessing steps, model training, evaluation metric calculation and prediction abilities. The prediction capabilities of the model extend to forecasting future traffic volumes for the coming four months which empowers the stakeholders with proactive decision-making insights. Among the key takeaways that we can have from the research are the model’s versatility, adaptability to deferent traffic prediction scenarios, and its ability to capture temporal patterns and predict future outcomes. To conclude, the research presents a holistic framework for better comprehension, forecasting and optimization of the traffic patterns with effects which extend to the urban planning, infrastructure management and traffic management strategies.