Efficient crowd management is crucial for municipalities to ensure public safety and enhance visitor experience, particularly in tourist-centric areas, such as Scheveningen Beach. Scheveningen Beach faces challenges because of the limited precision of visitor count data and the lack of accurate forecasts. Currently, crowd safety managers use their professional experience to forecast based on factors such as weather, events, and holidays, leading to inaccuracies, highlighting the need for accessible data and advanced analytics to enhance crowd management strategies. This study evaluates machine learning and deep learning models for multi-horizon hourly pedestrian crowd count forecasting, addressing the limitations of current manual prediction methods. Historical crowd data, weather, and holidays were integrated to train eXtreme gradient boosting, categorical boosting (CatBoost), light gradient boosting machine (LightGBM), long short-term memory (LSTM), and Temporal Fusion Transformer models for short-term (1-day), mid-term (7-day), and long-term (30-day) horizons. Models were developed for individual locations and as a unified multilocation approach. Performance was assessed using the coefficient of determination, root mean square error, normalized root mean square error, symmetric mean absolute percentage error, mean absolute error, and normalized mean absolute error metrics. The results showed that CatBoost was best for short-term forecasts, CatBoost and LightGBM for mid-term forecasts, and LSTM and LightGBM for long-term forecasts. Forecast performance decreases over longer time horizons in many locations, suggesting different applications: short-term forecasts for immediate operational decisions and long-term predictions for general trend analysis and strategic planning. Individual location models generally outperformed the unified approach, but at a higher computational cost. This study reveals significant spatial and temporal variability in crowd dynamics, which is crucial for optimizing resource allocation and enhancing preparedness in crowd management at Scheveningen Beach and similar tourist destinations.

Are you using tools like ChatGPT in your daily life to help write an email or even draft a construction plan? Just ten years ago, these kinds of capabilities would have seemed unimaginable. Today, they’re becoming part of everyday life for ordinary people. Behind these powerful tools are technologies known as Large Language Models (LLMs)—AI systems that can understand and generate human-like text and now even create images and videos. But what exactly are LLMs? Could they help transform fields like transportation and traffic management? Can they really do everything, or are there still limitations? In this article, we’ll walk you through a general introduction to LLMs: what they are, how they work, and what opportunities—and challenges—they bring to the transportation sector.

Large Language Models zijn AI-systemen die menselijke taal begrijpen en zich er ook in kunnen uiten. Ze zijn de basis onder populaire applicaties als ChatGPT, Gemini en Copilot. Maar inmiddels is de technologie zó breed inzetbaar dat ze ook doordringt in de mobiliteitssector. Hoe werken de Large Language Models? Hoe kunnen ze van nut zijn in ons vakgebied? En wat zijn de mitsen en maren

The conventional butterfly identification method is based on their different morphological characters namely wing-venation, color, shape, patterns and through the dissection studies and molecular techniques which are tedious, expensive and highly time-consuming. To overcome the above aforesaid challenges, a new butterfly identification system using butterfly images has been designed to instantly identify the butterfly with high accuracy. In this study, we construct a new butterfly dataset with 34,024 butterfly images belonging to 315 species from India. We propose and prove the effectiveness of new data augmentation techniques on our dataset. To identify butterflies using photographic images, we built eleven new Deep Convolutional Neural Network (DCNN) butterfly classifier models using eleven pre-trained architectures namely ResNet-18, ResNet-34, ResNet-50, ResNet-121, ResNet-152, Alex-Net, DenseNet-121, DenseNet-161, VGG-16, VGG-19 and SqueezeNet-v1.1. The different model's classification results were compared and the proposed technique achieved a maximum top-1 accuracy(94.44%), top-3 accuracy(98.46%) and top-5 accuracy(99.09%) using ResNet-152 model, followed by DenseNet-161 model achieved the top-1 accuracy(94.31%), top-3 accuracy (98.07%) and top-5 accuracy (98.66%). The results suggest that models can be assertively used to identify butterflies in India.