Taxi Services Revenue Optimization through Reinforcement Learning: A Deep Q Network Approach with Advanced Data Visualization

This study investigates the burgeoning challenges faced by urban taxi services, focusing on optimizing the operational efficiency of taxi drivers amidst the increasing urban congestion and pollution. The primary aim is to enhance drivers' earnings while promoting sustainable urban mobility. The research introduces an innovative framework that assimilates real-world taxi trip data to provide drivers with strategic insights on trip selection, thereby augmenting their income and operational efficiency. The methodology harnesses advanced data processing techniques to translate trip data into actionable insights. A comprehensive analysis is conducted to evaluate various operational parameters, such as trip distances, financial transactions, and service patterns. The findings are presented using sophisticated visualization tools, which illustrate the efficacy of the recommended strategies in improving the overall taxi service framework. The outcome of the research is a set of data-driven recommendations that empower taxi drivers with knowledge to make informed decisions. This not only promises a direct enhancement of their livelihood but also contributes to alleviating traffic and pollution by streamlining taxi operations. The implications of this research extend to informing policy-making, with the potential to shape future urban transport strategies that align with the global objectives of sustainable development and environmental preservation.