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The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy. Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras. As these images typically have a high number of overlaps and cut-offs of vehicles, we applied transfer learning to YOLOv5 to investigate whether the front cabin and the rear are suitable features for heavy goods vehicle recognition. Our results show that the trained algorithm can detect the front cabin of heavy goods vehicles with high confidence, while detecting the rear seems more difficult, especially when located far away from the camera. In conclusion, we firstly show an improvement in detecting heavy goods vehicles using their front and rear instead of the whole vehicle, when winter conditions result in challenging images with a high number of overlaps and cut-offs, and secondly, we show thermal network imaging to be promising in vehicle detection.

The primary goal of this study was to examine the applicability of preference-based segmentation for a broad array of meals in the context of teenagers. A representative sample of 1,168 Norwegian schoolchildren provided an evaluation of 20 common dinner meals in terms of preference (liking). Cluster analysis was used to establish four distinct preference-based food segments. These four segments were termed Food Lovers, Fish Haters, Fish Lovers and Dislikers. The relationship between these four preference-based segments and demographic, consumption, attitude and lifestyle variables was finally modelled using a multinomial logit analysis. The results highlight the fact that social and family-related attitude and lifestyle variables have a greater ability to profile the segments and explain segment membership than demographic variables. The findings indicate that there are distinct and interesting differences between these segments. The findings have some implications for marketers within the industry in terms of effectively targeting the different market segments.

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