Modelling Urban Logistics Processes Using Artificial Intelligence (AI)

The chapter delves into the transformative potential of artificial intelligence in urban logistics, particularly in optimizing public transport schedules. It begins by examining the increasing demands on the logistics sector due to globalization and the growing role of AI in meeting these challenges. The study investigates the use of AI to model bus timetables, comparing the capabilities of AI tools like ChatGPT-4 with traditional software such as 'Pikas'. The experimental research reveals that while AI has the potential to handle large datasets and optimize schedules, it currently requires extensive data input and manual corrections, making it less efficient than specialized software. The chapter also explores the broader implications of AI in urban logistics, highlighting its potential to enhance efficiency, decision-making, and problem-solving capabilities. It discusses the benefits and challenges of AI in public transport, including improved service quality, scheduling, and user experience, as well as the need to address data quality, resource requirements, and ethical considerations. The research concludes that AI has promising perspectives in urban logistics, but further experiments and refinements are needed to fully realize its potential.