The logistics landscape in e-commerce is undergoing a profound transformation toward sustainability and autonomy. This paper explores the implementation of autonomous maritime and last-mile transportation solutions to optimize the entire logistics chain from factory to customer. Building on the lessons learned from the maritime industry’s digital transformation, the study identifies key features and proposes a forward-looking autonomous maritime and last-mile transportation system. Emphasizing the role of geospatial technologies, the proposed system employs GIS-based electronic route optimization for efficient goods delivery, integrating onboard and ashore GIS-based sensors for enhanced location precision. A case study was built to analyze the implementation of autonomous means of transport along the route of a product from factory to customer. The integration of autonomous systems shows substantial improvements in logistics performance. Synchromodal logistics and smart steaming techniques can be utilized to optimize transportation routes, resulting in reduced fuel consumption and emissions. The findings reveal that autonomous maritime and last-mile transport systems can significantly enhance the efficiency, flexibility and sustainability of e-commerce logistics. The study emphasizes the need for advanced technological integration and provides a comprehensive framework for future research and practical applications in the logistics industry.

This chapter presents an in-depth exploration of the maritime industry’s transition toward autonomous operations, focusing on the significant advancements in geospatial technologies and artificial intelligence (AI). It examines the evolution of maritime transport, emphasizing the integration of innovative IT solutions, including GIS-based sensors and route optimization techniques. The chapter discusses the emergence and impact of autonomous surface vessels (ASVs), highlighting the challenges they bring in cybersecurity and the necessity for comprehensive standards. Additionally, it explores the potential of a vessel traffic system architecture, with a central node based on an ashore operations center to efficiently manage autonomous maritime logistics. By integrating cutting-edge sensors and AI into ASV systems, the chapter showcases the industry’s shift toward a more sustainable, efficient, and technologically advanced future, while addressing the critical issues of safety, security, and environmental impact in this rapidly evolving landscape.

Use of the geospatial technologies and its implications in the maritime transport and logistics

The integration of AI tools like ChatGPT in education has sparked debates on their benefits and limitations, particularly in subjects requiring region-specific knowledge. This study examines ChatGPT’s ability to generate accurate and contextually rich responses to assignments in Romanian history and geography, focusing on topics with limited digital representation. Using a document-based analysis, this study compared ChatGPT’s responses to local archival sources, monographs, and topographical maps, assessing coverage, accuracy, and local nuances. Findings indicate significant factual inaccuracies, including misidentified Dacian tribes, incorrect historical sources, and geographic errors such as misplaced landmarks, elevation discrepancies, and incorrect infrastructure details. ChatGPT’s reliance on widely digitized sources led to omissions of localized details, highlighting a fundamental limitation when applied to non-digitized historical and geographic topics. These results suggest that while ChatGPT can be a useful supplementary tool, its outputs require careful verification by educators to prevent misinformation. Future research should explore strategies to improve AI-generated educational content, including better integration of regional archives and AI literacy training for students and teachers. The study underscores the need for hybrid AI-human approaches in education, ensuring that AI-generated text complements rather than replaces verified academic sources.

The metropolitan agglomeration in and around Bucharest, Romania’s capital and largest city, has experienced significant growth in recent decades, both economically and demographically. With over two million residents in its metropolitan area, Bucharest faces urban mobility challenges characterized by congested roads, overcrowded public transport routes, limited parking, and air pollution. This study evaluates the potential of AI-driven adaptive traffic signal control to address these challenges using an agent-based simulation approach. The authors focus on Bucharest’s north-western part, a critical congestion area. A detailed road network was derived from OpenStreetMap and calibrated with empirical traffic data from TomTom Junction Analytics and Route Monitoring (corridor-level speeds and junction-level turn ratios). Using the MATSim framework, the authors implemented and compared fixed-time and adaptive signal control scenarios. The adaptive approach uses a decentralized, demand-responsive algorithm to minimize delays and queue spillback in real time. Simulation results indicate that adaptive signal control significantly improves network-wide average speeds, reduces congestion peaks, and flattens the number of en-route agents throughout the day, compared to fixed-time plans. While simplifications remain in the model, such as generalized signal timings and the exclusion of pedestrian movements, these findings suggest that deploying adaptive traffic management systems could deliver substantial operational benefits in Bucharest’s urban context. This work demonstrates a scalable methodology combining open geospatial data, commercial traffic analytics, and agent-based simulation to rigorously evaluate AI-based traffic management strategies, offering evidence-based guidance for urban mobility planning and policy decisions.

Environmental monitoring of air quality is essential to understanding the impact of pollution on human health and the environment. In urban areas, air quality is a significant concern due to high levels of pollutants emitted by transportation, industry, and energy production. Traditional air quality monitoring methods are often limited in their spatial and temporal resolution and can be expensive to maintain. Recent advances in artificial intelligence (AI) and geospatial analysis provide an opportunity to revolutionize the way we monitor air quality in urban areas. This paper explores the potential use of AI and geospatial analysis in air quality monitoring in the city of Bucharest. We present an overview of a specific supervised AI algorithm and its application in air quality monitoring. We also discuss the use of geospatial data, such as satellite imagery and GIS data, to enhance the accuracy and effectiveness of air quality monitoring. To demonstrate the potential of AI and geospatial analysis in air quality monitoring, this paper presents a case study of air quality monitoring in City of Bucharest. We use machine learning algorithms to analyze data collected from air quality sensors (state-owned and private) and geospatial data, such as traffic density and land use. Our results demonstrate the effectiveness of AI and geospatial analysis in predicting air quality parameters, such as particulate matter and nitrogen oxides, with a high degree of accuracy. Overall, the study highlights the potential of AI and geospatial analysis in air quality monitoring in urban areas. By integrating these technologies into air quality monitoring programs, we can improve our understanding of the sources and impact of pollutants on air quality in cities. This, in turn, can help policymakers and urban planners make better-informed decisions to reduce pollution and protect public health.