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Navigation safety is a priority both at European and global level. Despite the important progress made over the years, sea accidents remain a major concern and much work is still needed to enhance maritime safety. Knowing the causes and precursors of past accidents is essential to identify the elements on which to intervene to improve safety and reduce the possibility of an accident to occur again. In this study, 1.079 sea accidents from the International Maritime Organization (IMO) database are analyzed using Semi-supervised Recursively Partitioned Mixture Models in an attempt to identify and categorize causal themes from accident data. Special attention is devoted to the human element, which is widely recognized as a primary or precursory cause in most accidents.

The great potential of the Mediterranean area, as yet not fully tapped due to the lack of the integrated management of its ports, calls for innovative management policies for achieving competitiveness within the Mediterranean port system. To this end, the current regime of intra-port competition has proven highly unproductive and needs to be rethought, implementing new cooperation policies.

Urban freight consolidation centres (UFCCs) can provide a significant contribution to reducing the negative impacts of freight transport to city centres whilst at the same time providing a more seamless, higher-value logistics experience for their users. The paper draws on the experiences of the Bristol-Bath freight consolidation centre (BBFCC), established in 2002 to serve Bristol city centre and uniquely extended in 2011 to cover Bath, each served by electric lorries; it appraises the benefits of shared ‘final mile’ freight services, presenting a model for the evaluation of the reduction in traffic and polluting emissions based on Bristol, with a view to optimising future UFCC design. Data about the number of deliveries made by heavy goods vehicles (HGVs) of different types to the BBFCC and the number of deliveries made from the BBFCC to the two shopping centres covering a period of 17 months are analysed. The correlation between the type and number of HGVs delivering to the BBFCC and the number of deliveries made to the retailers by the BBFCC is explicated by means of a multiple linear regression model. Its development is based on analysing parameters as R Square value (total and adjusted), F-statistics and p-values for each coefficient. An estimation of the number of HGVs re-routing to the BBFCC and the pollutant emissions avoided in the urban centre is appraised. The pollutant emissions reduction is based on factors drawn from the UK National Atmospheric Emissions Inventory. Results suggest that the proposed approach may yield HGV movements avoided in Bristol city centre of 75.5% on average. Also, by considering the whole study period, reductions amount calculated is equivalent to 28,677 Kg of CO2, 122.29 Kg of NOx, 2.31 Kg of PM10, 20.32 Kg of CO and 9,854 Kg of fuel. Nevertheless, emissions reductions are significant, but currently limited by small scale, due to the low number of participants. Emissions reductions in the host cities are identified as a result of sharing delivery vehicles for the final leg. The regression model showed high correlation coefficient values (over 85%) for deliveries to the Bristol city centre thanks to the BBFCC. The linear regression models developed provide a useful tool for local authorities and logistics/transport planners in optimising the planning of UFCCs to reduce freight traffic, associated emissions and to improve logistics and transport performance.