Over the past two decades, researchers have made significant steps in simulating agent-based human crowds, yet most efforts remain focused on low-level tasks such as collision avoidance, path following, and flocking. Realistic simulations, however, require modeling high-level behaviors that emerge from agents interacting with each other and with their environment over time. We introduce Generative Crowds (Gen-C), a generative framework that produces crowd scenarios capturing agent-agent and agent-environment interactions, shaping coherent high-level crowd plans. To avoid the labor-intensive process of collecting and annotating real crowd video data, we leverage large language models (LLMs) to bootstrap synthetic datasets of crowd scenarios. We propose a time-expanded graph representation, encoding actions, interactions, and spatial context. Gen-C employs a dual Variational Graph Autoencoder (VGAE) architecture that jointly learns connectivity patterns and node features conditioned on textual and structural signals, overcoming the limitations of direct LLM generation to enable scalable, environment-aware multi-agent crowd simulations. We demonstrate the effectiveness of Gen-C on scenarios with diverse behaviors such as a University Campus and a Train Station, showing that it generates heterogeneous crowds, coherent interactions, and high-level decision-making patterns consistent with real-world crowd dynamics.

As urban populations continue to grow, cities face numerous challenges in managing parking and determining occupancy. This issue is particularly pronounced in university campuses, where students need to find vacant parking spots quickly and conveniently during class timings. The limited availability of parking spaces on campuses underscores the necessity of implementing efficient systems to allocate vacant parking spots effectively. We propose a smart framework that integrates multiple data sources, including street maps, mobility, and meteorological data, through a spatial join operation to capture parking behavior and vehicle movement patterns over the span of 3 consecutive days with an hourly duration between 7AM till 3PM. The system will not require any sensing tools to be installed in the street or in the parking area to provide its services since all the data needed will be collected using location services. The framework will use the expected parking entrance and time to specify a suitable parking area. Several forecasting models, namely, Linear Regression, Support Vector Regression (SVR), Random Forest Regression (RFR), and Long Short-Term Memory (LSTM), are evaluated. Hyperparameter tuning was employed using grid search, and model performance is assessed using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Coefficient of Determination (R2). Random Forest Regression achieved the lowest RMSE of 0.142 and highest R2 of 0.582. However, given the time-series nature of the task, an LSTM model may perform better with additional data and longer timesteps.

Pretesting involves a small-scale trial of data collection procedures, aiming to assess them. It is a standard practice in both academic and applied research (Grimm 2010), and the output of the pretest is usually the feedback offered by interviewers on how to improve procedures and questions. The rapid advancements in generative artificial intelligence (GAI) have opened up new avenues for enhancing various aspects of research, including the design and evaluation of survey questionnaires. AI technologies like large language models (LLMs) have demonstrated remarkable potential in generating human-like text, offering a promising approach to pretesting survey instruments. This article explores the use of GPT models as a tool for pretesting survey questionnaires. Illustrated with two applications, it suggests incorporating GPT feedback as an additional stage before human pretesting, potentially reducing successive iterations. However, the article emphasizes the indispensable role of researchers' judgment in implementing AIgenerated feedback. GPT is an LLM that utilizes advanced algorithms to generate texts that mimic the syntax, semantics, and grammar of human writing, which are approximated by statistical patterns learned from training data (for a technical review, see OpenAI 2023). Like most of the LLMs, GPT models predict the next word in a sequence based on the preceding words.

The modern power grid is facing increasing complexities, primarily stemming from the integration of renewable energy sources and evolving consumption patterns. This paper introduces an innovative methodology that harnesses Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to establish robust time series forecasting models for grid frequency. These models effectively capture the spatiotemporal intricacies inherent in grid frequency data, significantly enhancing prediction accuracy and bolstering power grid reliability. The research explores the potential and development of individualized Convolutional LSTM (ConvLSTM) models for buildings within a university campus, enabling them to be independently trained and evaluated for each building. Individual ConvLSTM models are trained on power consumption data for each campus building and forecast the grid frequency based on historical trends. The results convincingly demonstrate the superiority of the proposed models over traditional forecasting techniques, as evidenced by performance metrics such as Mean Square Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Additionally, an Ensemble Model is formulated to aggregate insights from the building-specific models, delivering comprehensive forecasts for the entire campus. This approach ensures the privacy and security of power consumption data specific to each building.

As part of a major new study led by the Centre for Connected and Autonomous Vehicles (CCAV) and insight and strategy specialist BritainThinks, Aurrigo has embarked on a four-day trial of its self-driving vehicles. Based in the heart of the UK's traditional Midlands automotive home in Coventry, Aurrigo claims to be a leader in the development of "first and last mile" transport solutions. Its self-driving pods are designed to provide mobility within urban areas, shopping malls, airports, university campuses, science parks and other areas that are poorly served by traditional transport providers. The Great Self Driving Exploration allowed residents local to Alnwick Castle and Alnwick Gardens in Northeast England see the first time that self-driving vehicles have been tested in rural communities. The trial saw Aurrigo's Auto-Pod carry up to two passengers on a shared 500-metre path that connects Alnwick Gardens to Alnwick Castle.

Imagine a robot riding the elevator up your tower block, knocking on your door, and delivering the package you ordered an hour earlier: That's the future of e-commerce in China. Tech giant Alibaba Group sees a fleet of robots as a fast, reliable and relatively cheap way to fulfill the burgeoning demand for online shopping across China, the world's largest consumption market. In a big step towards hitting this goal, Alibaba is deploying 1,000 delivery bots across Chinese university campuses and urban communities this year. The bots, called Xiaomanlv or "small donkey" in Mandarin, can deliver about 50 packages at a time and as many as 500 boxes in one day, covering 100 kilometers on a single charge. The bots pick up parcels at a local courier drop-off point and make their way to your building, trundling along sidewalks and in bicycle lanes.

Universities worldwide are experiencing a surge in enrollments, therefore campus estate managers are seeking continuous data on attendance patterns to optimize the usage of classroom space. As a result, there is an increasing trend to measure classrooms attendance by employing various sensing technologies, among which pervasive WiFi infrastructure is seen as a low cost method. In a dense campus environment, the number of connected WiFi users does not well estimate room occupancy since connection counts are polluted by adjoining rooms, outdoor walkways, and network load balancing. In this paper, we develop machine learning based models to infer classroom occupancy from WiFi sensing infrastructure. Our contributions are three-fold: (1) We analyze metadata from a dense and dynamic wireless network comprising of thousands of access points (APs) to draw insights into coverage of APs, behavior of WiFi connected users, and challenges of estimating room occupancy; (2) We propose a method to automatically map APs to classrooms using unsupervised clustering algorithms; and (3) We model classroom occupancy using a combination of classification and regression methods of varying algorithms. We achieve 84.6% accuracy in mapping APs to classrooms while the accuracy of our estimation for room occupancy is comparable to beam counter sensors with a symmetric Mean Absolute Percentage Error (sMAPE) of 13.10%.

The effective deployment of connected vehicular networks is contingent upon maintaining a desired performance across spatial and temporal domains. In this paper, a graph-based framework, called SMART, is proposed to model and keep track of the spatial and temporal statistics of vehicle-to-infrastructure (V2I) communication latency across a large geographical area. SMART first formulates the spatio-temporal performance of a vehicular network as a graph in which each vertex corresponds to a subregion consisting of a set of neighboring location points with similar statistical features of V2I latency and each edge represents the spatio-correlation between latency statistics of two connected vertices. Motivated by the observation that the complete temporal and spatial latency performance of a vehicular network can be reconstructed from a limited number of vertices and edge relations, we develop a graph reconstruction-based approach using a graph convolutional network integrated with a deep Q-networks algorithm in order to capture the spatial and temporal statistic of feature map pf latency performance for a large-scale vehicular network. Extensive simulations have been conducted based on a five-month latency measurement study on a commercial LTE network. Our results show that the proposed method can significantly improve both the accuracy and efficiency for modeling and reconstructing the latency performance of large vehicular networks.

If you are a US university student, you will soon have the option of getting your takeaways delivered by robot thanks to the ambitious expansion plans of Estonian startup Starship. Started in Estonia by ex-Skype entrepreneurs Ahti Heinla and Janus Friis, the company about to dramatically increase the number 6-wheeled delivery bots roaming the streets, following a $40m Series-A funding round. While questions are being raised about the economics of such a robot delivery business, the company is unabashed. Lex Bayer, chief executive, tells Sifted that the company plans to expand to 100 US university campuses over the next two years. With a typical fleet of robots being somewhere between 25 and 50, that could mean more than 5000 robots in service by 2021.

From humanoid robots being launched into space to medical mechanisms with "dexterous 3D-printed fingers", the world of robotics is ripe with opportunity. It's a prosperous technology, with insight from the International Federation of Robotics (IFR) revealing that "More than 3 million industrial robots will be in use in factories around the world by 2020." Examining the results of its global survey of 7,000 employees in seven countries, IFR also adds that "Nearly 70 per cent of employees believe that robotics and automation offer the opportunity to qualify for higher-skilled work." Without a doubt, the progress of robotics vs the progress of graduates is questionable. While futurists and philosophers believe robots are coming for people's jobs, some believe that they will harmoniously live alongside workers in peace, enhancing roles rather than ruining them.