Optimizing Public Transportation Systems through Data Science Techniques

Abstract

Public transportation systems are integral to urban infrastructure, facilitating mobility and contributing to economic growth. However, inefficiencies such as congestion, delays, and underutilization persist, necessitating optimization. This paper explores the application of data science techniques to enhance the efficiency and effectiveness of public transportation systems. By leveraging big data analytics, machine learning algorithms, and predictive modeling, the study identifies patterns and insights that inform decision-making processes. The methodology encompasses data collection from various sources, preprocessing, feature extraction, and the deployment of predictive models to forecast demand and optimize routing. The results demonstrate significant improvements in operational efficiency, passenger satisfaction, and resource allocation. This research underscores the potential of data science in transforming public transportation, offering scalable solutions for urban mobility challenges.