Computational intelligence-based model for passenger demand forecasting and fleet management of autonomous taxis in smart cities
Abstract
Transforming cities into smart cities is an essential and aspirational goal in the rapidly evolving technology landscape. An integral part of this change is the emergence of Autonomous Taxis (ATs), which represents a significant development in the taxi service industry. These ATs systems hold the potential to transform urban mobility by mitigating traffic congestion and reducing environmental pollution. Beyond their environmental benefits, they promise to enhance urban residents’ overall quality of life. Despite their potential, the effective management of ATs, particularly in passenger demand forecasting and dispatching, poses significant challenges due to privacy concerns, data sharing limitations, and the dynamic nature of urban mobility demands. This study addresses these challenges by focusing on two key areas: forecasting ATs demand and optimizing ATs fleet management. In this study, we proposed a model that integrates computational intelligence approaches to enhance the operational efficiency of ATs in smart cities. The proposed model integrates a novel collaborative privacy-preserving Federated Learning (FL) approach for demand forecasting. It enables ATs across various city regions to refine their forecasting models collaboratively without direct data sharing, thereby reducing privacy risks and communication overheads. It integrates a centralized agent-based model to optimize the dispatching of ATs, with an emphasis on maximizing fleet profitability while…
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