Optimizing Taxi Services through AI-Driven Customer Behavior Modeling and Geo-fenced Hotspot Recommendations for Drivers

Abstract

The shift in consumer behavior in Zambia, from traditional in-store shopping to the rapidly growing demand for delivery and ride-hailing taxi services, has created an urgent need for more efficient and time-saving transportation solutions. Urban centers such as Lusaka, Ndola, and Kitwe have experienced significant increases in ride-hailing activity, driven by the convenience and accessibility of digital platforms. While these services improve customer access and transparency in pricing, they have also introduced new operational challenges for drivers. Longer wait times for customers, increased travel distances to reach riders, and frequent cancellations have led to rising operational costs, particularly in fuel expenditure. Drivers often absorb these costs without guaranteed revenue, reducing profitability and discouraging long-term sustainability in the taxi industry. This research investigates the application of Artificial Intelligence (AI) to address these inefficiencies through a customer request behavioral model that identifies demand patterns, creates geo-fenced hotspots, and provides drivers with intelligent recommendations on optimal positioning. By leveraging Zambia-specific datasets and simulating ride request trends, cancellation behavior, and fuel costs, the study demonstrates how AI can reduce idle driving, improve service efficiency, and enhance driver profitability. Statistical tools, including line graphs, histograms, scatter plots, and pie charts, are applied to consolidate findings and provide empirical evidence of the model’s effectiveness. The results indicate that AI-driven geo-fencing has the potential to significantly minimize operational costs, increase customer satisfaction through shorter waiting times, and create a scalable framework for optimizing both taxi and delivery services in Zambia’s evolving transport sector.