Development of a Data Analytics-Based Decision Support System for Optimizing Public Transportation Services

Abstract

Public transport is essential for sustainable urban mobility, but challenges such as limited capacity, operational inefficiency, and service performance remain. This study develops a Decision Support System (DSS) that integrates operational data, passenger preferences, and performance indicators to provide recommendations for decision-making. The study uses passenger surveys, system performance analysis, and data analysis and prediction techniques. The DSS incorporates information visualization, performance monitoring, and scenario analysis to help decision-makers anticipate potential outcomes and make informed choices. The results demonstrate improvements in service performance, with passenger satisfaction increasing from 76% to 95%, while prediction accuracy exceeded 90%. These findings indicate that the DSS can support more effective decision-making and resource allocation while improving transparency and accountability in public transport services. The system also aligns with efforts to promote technology-driven urban development. Overall, integrating data and decision-support technologies can improve public transport operations, support better resource utilization, and contribute to more sustainable and inclusive urban mobility.