A Smart Adaptive Transportation Planning Model Including Real-time Drivers’ Knowledge using Answer Set Programming and Knowledge Graphs
Mohammad Yaser Mofatteh, Reyan Abbas, Nicholas Desire Seddoh, S. Sedhumadhavan, Mintra Thunyaluck, Omid Fatahi Valilai
Abstract
The rapid advancement of Artificial Intelligence (AI) has catalysed transformative developments across various domains, including tour planning. This paradigm shift has unlocked new opportunities for leveraging AI-driven solutions to optimize tour itineraries, enhance user experiences, and streamline logistical operations. In particular, Artificial General Intelligence (AGI) techniques have been instrumental in revolutionizing tour planning by offering innovative approaches to route optimization, resource allocation, and personalized itinerary generation. This abstract explores the burgeoning landscape of AGI applications in tour planning, highlighting its potential to reshape the tourism industry and improve the efficiency and effectiveness of tour management processes. This research proposes an adaptive tour planning model aimed at addressing the evolving needs and challenges of modern tour operators. Focusing on the Vehicle Routing Problem (VRP), the proposed model integrates novel constraints, including weather and traffic conditions, to enhance its robustness and applicability in real-world scenarios. By incorporating these dynamic factors, the proposed model offers more resilient and responsive routing solutions, capable of adapting to changing environmental conditions and unforeseen disruptions. This innovative approach lays the foundation for the development of more efficient and reliable tour planning systems, paving the way for enhanced user satisfaction and operational efficiency in the tourism sector.