Patrick Hirsch

Efficient damage detection of trailers is essential for improving processes at inland intermodal terminals. This paper presents an automated damage detection (ADD) algorithm for trailers utilizing ensemble learning based on YOLOv8 and RetinaNet networks. The algorithm achieves 88.33% accuracy and an 81.08% F1-score on the real-life trailer damage dataset by leveraging the strengths of each object detection model. YOLOv8 is trained explicitly for detecting belt damage, while RetinaNet handles detecting other damage types and is used for cropping trailers from images. These one-stage detectors outperformed the two-stage Faster R-CNN in all tested tasks within this research. Furthermore, the algorithm incorporates slice-aided hyper inference, which significantly contributes to the efficient processing of high-resolution trailer images. Integrating the proposed ADD solution into terminal operating systems allows a substantial workload reduction at the ingate of intermodal terminals and supports, therefore, more sustainable transportation solutions.

Motivated by the urgent need to discover innovative and sustainable approaches to address the strain on urban resources, this paper introduces a novel concept proposing the utilization of “sleeping assets”. These sleeping assets encompass three distinct categories of urban infrastructures: neglected routes, idle real estate, and underused resources. By harnessing these assets, cities can alleviate the intense competition for land, traffic infrastructure, and parking areas. The findings presented in this paper are derived from an extensive literature study and expert interviews conducted with relevant stakeholders from Hamburg, Paris, Stockholm, and Vienna. Building upon the most pertinent insights from the literature and a structured content analysis of the interviews, we outline the advantages of employing sleeping assets for urban logistics initiatives. Additionally, we identify seven main categories of implementation barriers that must be taken into account. The results of this study reveal political and legislative barriers as one of the major impeding factors in initiating the utilization of sleeping assets. To address these barriers, this paper points towards crucial leverage for cities and provides an outlook on possible forms of sustainable urban logistics implementation. Thus, this work can support researchers as well as practitioners from city administrations and urban logistics interested in using sleeping assets by encouraging the exploitation of unused potential and avoiding particular implementation pitfalls.