This paper addresses the main logistics challenges in used car maritime traffic from Europe to West Africa. Thus, the methodology (quantitative and qualitative) analyses data from the International Organization of Motor Vehicle Manufacturers (OICA), from 2015 to 2023 of government and port authorities to show the importance of used car market for mobility and socioeconomic activities. This is supplemented by surveys based on direct observation in the field, questionnaires and interviews involving in Europe 55 stakeholders and 127 in Africa. The results demonstrate that cars used and their parts, but not wrecks, are essential for motorization in West Africa. A pre-export process needs to be set up to ensure that exported vehicles are parked in better condition to meet the required common environmental standards for sustainable mobility.
Cyber-physical Systems (CPS) have revolutionized urban transportation worldwide, but their implementation in developing countries faces significant challenges, including infrastructure modernization, resource constraints, and varying internet accessibility. This paper proposes a methodological framework for optimizing the implementation of Cyber-Physical Urban Mobility Systems (CPUMS) tailored to improve the quality of life in developing countries. Central to this framework is the Dependency Structure Matrix (DSM) approach, augmented with advanced artificial intelligence techniques. The DSM facilitates the visualization and integration of CPUMS components, while statistical and multivariate analysis tool such as Principal Component Analysis (PCA) and artificial intelligence methods such as K-means clustering enhance complex system the analysis and optimization of complex system decisions. These techniques enable engineers and urban planners to design modular and integrated CPUMS components that are crucial for efficient, and sustainable urban mobility solutions. The interdisciplinary approach addresses local challenges and streamlines the design process, fostering economic development and technological innovation. Using DSM and advanced artificial intelligence, this research aims to optimize CPS-based urban mobility solutions, by identifying critical outliers for targeted management and system optimization.
Efficient access to tourist spots is necessary for enhancing the overall travel experience, especially in urban environments. This study investigates the accessibility of key tourist spots in Budapest through different transportation modes (e.g., walking, cycling, and public transport) across various time intervals. Using spatial-temporal travel time maps and detailed statistical analysis, the research highlighted significant differences in how these modes connect tourists to their attractions. Cycling stands out as the most efficient transportation option, providing rapid access to a wide range of tourist spots, while public transport ranks second. However, the study also reveals disparities in accessibility, with central areas being well-served, while outer ones, especially in the northwest, remain less accessible. These findings highlight the need for targeted transportation improvements to ensure that all areas of the city are equally reachable. The results offer valuable insights for urban planners and policymakers aiming to enhance tourism infrastructure and improve the visitor experience in Budapest.
