Integrative analytics for advancing decision support systems to support rail-based freight transport services

Abstract

Despite the increasing digitalization of logistics, further development is required to advance comprehensive approaches for monitoring and assessing rail service performance across end-to-end journeys. Recent advancements in Intelligent Rail Transportation Systems (IRTS) leverage big data analytics to enhance railway operations and decision-making. However, there is still a lack of support systems to continuously monitor rail corridors and provide functionality for assessing efficiency, sustainability, and overall service performance in an integrated manner. To this end, key challenges need to be tackled: (i) the integration of diverse data sources, encompassing both transport views at ports and corridors, for holistic analysis, (ii) defining key metric (e.g. capacity shortages, delays, suboptimal loads) that capture service performance variability across time and space, and (iii) ensuring robust machine learning and statistical analysis to enhance observability of spatiotemporal pattern dynamics in the scope of service performance of the different corridors. Considering the above challenges, this research focus on the development of an advanced analytical decision support system (ADS2) for the comprehensive identification and assessment of key performance indicators in rail freight systems such as capacity shortages, travel time, delays, suboptimal load factors. The system is designed to provide actionable insights for each journey by offering: (i) a tailored, multi-dimensional metric system that evaluates service (e.g., average delay times at entry and exit points, capacity, travel time, empty trips, and suboptimal transfer time); (ii) context-sensitive analytics that captures temporal trends and seasonal patterns across various terminal locations; (iii) capabilities to assess service suitability for different types of goods (e.g., hazardous and non-hazardous goods); and (iv) a ranking system that classifies the spatiotemporal features of end-to-end journeys and compares their quality against similar journeys with similar mobility dynamics within the rail service. The case study in this research focuses on the evaluation of services in the national freight system that connects the multimodal Port of Sines with key cargo destinations, including the Sines-Madrid corridor. The proposed tool aims to support an effective transport planning – operational, tactical and strategic – to foster the connection between rail services and the logistics operations covering the entire supply chain.