Centralized agent-based model for efficient dispatching of autonomous taxis in smart cities

Abstract

Transforming cities into smart cities is an important and aspirational goal in the rapidly evolving technological landscape. A key aspect of this transformation is the emergence of autonomous taxis (ATs), which hold the potential to mitigate traffic congestion and environmental pollution while enhancing urban residents’ overall quality of life. The effectiveness of taxi services significantly relies on efficient dispatching systems, which enhance service quality by minimizing waiting times and travel distances. Managing demand and supply dynamically within urban transportation presents a significant challenge. However, traditional approaches, prioritizing driver earnings and localized optimization, often fail to address the dynamic fluctuations in city-wide demand, leading to longer waiting times and travel distances. Addressing these challenges within the context of ATs, we propose a centralized agent-based model to optimize the dispatching of ATs, with an emphasis on maximizing fleet profitability while considering both minimizing distance traveled and reducing passenger waiting time. The model employs proactive decision-making to predict demand patterns and dynamically reposition vacant ATs to high-demand areas, efficiently adapting to demand fluctuations for improved dispatching. Our model is evaluated against various approaches, including random assignment, modified ant colony optimization, proximity greedy heuristic, hill climbing, and beam search models. The proposed model is illustrated through the simulation by using real-world scenarios of the demand pattern in Bangkok, Thailand. Results show that the proposed model outperforms other approaches, achieving higher fleet profitability, reduced waiting times, and shorter travel distances. This study contributes significantly to intelligent transportation systems (ITs) and supports the vision of sustainable smart cities.