Panoramic Analysis of PATHAO Ride-Sharing and Commuter Audit Using Advanced Machine Learning Methods: Dhaka Transportation Scenario

Scrutinizing the operational dynamics of PATHAO, a prominent player in Dhaka’s ride-sharing landscape, entails leveraging advanced machine learning techniques to conduct comprehensive commuter audits and investigate transportation patterns. Despite its rapid urban expansion and intricate mobility challenges, Dhaka serves as a dynamic canvas for exploring the transformable impact of PATHAO on urban transportation dynamics. This research employs sophisticated computational algorithms to dissect extensive ride data, unveiling nuanced insights into commuter audit strategies, service efficiency optimizations, and geographical demand patterns across Dhaka’s diverse zones. Predictive modeling facilitates real-time demand forecasting and optimal user-active deployment strategies, enhancing service reliability and customer satisfaction in this area. Moreover, the study integrates a holistic perspective on user experiences, service perceptions, and operational challenges. This qualitative depth enriches the quantitative analysis, offering actionable insights for policymakers and urban planners striving to improve transportation efficiency and accessibility in growing urban environments.