Purpose - The purpose of this paper is to analyse the economic benefits of short sea shipping (SSS) in the shape of Motorways of the Sea (MoS) compared to road transport. The study cover a gap in agro-food economics and analyses the economic benefits of sea transport mode compared to road transport in the food trade between Spain and Italy for a specific product: olive oils. \ud \ud Design/methodology/approach - Three different transportation scenarios are considered (road only, road combined with accompanied SSS and road combined with unaccompanied SSS) linking the main olive oil production and consumption areas in Spain and Italy. In each scenario the cost per unit shipped have been calculated. \ud \ud Findings - The results show the road option is about 30 and 34 per cent more costly than the best SSS option available for the exportations from Jaen and Southern Catalonia, respectively. \ud \ud Research limitations/implications - The need of further research is identified, mainly focused in two directions: first, the need for inclusion of new variables to the model (e.g. value of time, seasonality, complexity of the transport chain, potential demand, etc.) to better assess the competitiveness of the sea connection and, second, a study of the environmental impact and socio-economic benefits of SSS implementation for the agri-food sector. \ud \ud Originality/value - The research enriches the current literature on this field and provides a basis for future studies. In particular, it corroborates the strategic decisions taken in the framework of European transport policy demonstrating a greater economic sustainability of SSS, and more specifically MoS, compared to the road transport

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Cruise tourism has grown rapidly worldwide over the past 30 years. This increase in cruise passengers has brought a set of economic, socio‐cultural, and environmental impacts. In a port context, it is reflected in mobility problems with long waiting times and queues. To assess cruise passenger impacts, a mobility analysis was performed. The results of this analysis demonstrate the importance of the disembarkation operation. Passengers have to be moved in short periods of time. It is vital to organize mobility and to have sufficient transport modes to move passengers to their inland destinations quickly, safely, and efficiently.

The accuracy of Human Activity Recognition is noticeably affected by the orientation of smartphones during data collection. This study utilized a public domain dataset that was specifically collected to include variations in smartphone positioning. Although the dataset contained records from various sensors, only accelerometer data were used in this study; thus, the developed methodology would preserve smartphone battery and incur low computation costs. A total of 175 different features were extracted from the pre-processed data. Data stratification was conducted in three ways to investigate the effect of information sharing between the training and testing datasets. After data balancing using only the training dataset, ten-fold and LOSO cross-validation were performed using several algorithms, including Support Vector Machine, XGBoost, Random Forest, Naïve Bayes, KNN, and Neural Network. A very simple post-processing algorithm was developed to improve the accuracy. The results reveal that XGBoost takes the least computation time while providing high prediction accuracy. Although Neural Network outperforms XGBoost, XGBoost demonstrates better accuracy with post-processing. The final detection accuracy ranges from 99.8% to 77.6% depending on the level of information sharing. This strongly suggests that when reporting accuracy values, the associated information sharing levels should be provided as well in order to allow the results to be interpreted in the correct context.