Taxi Revenue Optimization with Deep Q-Learning and Enhanced 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 driver’s earnings and livelihood while promoting sustainable urban mobility. The research introduces an innovative framework that assimilates comprehensive real-world taxi trip data to provide drivers with strategic insights on trip selection. 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 is a set of data-driven recommendations that empower taxi drivers with knowledge to make informed decisions using Deep Q-Learning. This contributes to alleviating traffic and pollution by streamlining taxi operations, with further implications extending to global policy-making for shaping sustainable transport policies and environmental preservation.