One humid afternoon in July, José Ramírez-Garofalo drove his large Toyota truck through the lush new hills, valleys, and meadows of Freshkills Park, a twenty-two-hundred-acre green space that the city is constructing on Staten Island. Ramírez-Garofalo, a young man with dark hair, large forearms, and the beginnings of a goatee, drove and talked fast. "It's an impermeable geotextile membrane," he said, referring to the thick plastic that was used, starting in the mid-nineties, to cap the four giant trash mounds of the old Fresh Kills landfill. "On top there is playground soil." The process of capping and terraforming the four mounds that once made up the country's largest dump is complete, but the park won't be fully open until at least 2036.

The heatwave currently blanketing two-thirds of the United States with record-setting temperatures is straining the nation's power system. On Monday, Con Edison, New York City's power provider, urged residents to conserve electricity. It reduced power voltage to the borough of Brooklyn by 8 percent as it made repairs; it did the same to areas in the boroughs of Staten Island and Queens yesterday. Thousands also lost power as the grid could not handle the strain. Comparable outages have been felt around much of the East Coast and Midwest including in the states of Virginia and New Jersey.

Hosted by The Alan Turing Institute, AI UK is a yearly event that brings together representatives from government, academia and industry to showcase data science and AI research and innovation in the UK. This year, the two-day conference featured talks, panel discussions, and hands-on workshops, and participants could attend in-person or remotely. One of the sessions focussed on an on-going case study in a London borough whereby the local council is using data and AI to help inform their decision making, and to improve what they do. Tariq set the scene by describing the borough of Camden, an area that not only houses institutions such as University College London and the Francis Crick Institute, and companies such as Google, but also some of the poorest communities in Europe. The council wants to tackle inequality and sees the use of data as one potential avenue.

Data stream forecasts are essential inputs for decision making at digital platforms. Machine learning algorithms are appealing candidates to produce such forecasts. Yet, digital platforms require a large-scale forecast framework that can flexibly respond to sudden performance drops. Re-training ML algorithms at the same speed as new data batches enter is usually computationally too costly. On the other hand, infrequent re-training requires specifying the re-training frequency and typically comes with a severe cost of forecast deterioration. To ensure accurate and stable forecasts, we propose a simple data-driven monitoring procedure to answer the question when the ML algorithm should be re-trained. Instead of investigating instability of the data streams, we test if the incoming streaming forecast loss batch differs from a well-defined reference batch. Using a novel dataset constituting 15-min frequency data streams from an on-demand logistics platform operating in London, we apply the monitoring procedure to popular ML algorithms including random forest, XGBoost and lasso. We show that monitor-based re-training produces accurate forecasts compared to viable benchmarks while preserving computational feasibility. Moreover, the choice of monitoring procedure is more important than the choice of ML algorithm, thereby permitting practitioners to combine the proposed monitoring procedure with one's favorite forecasting algorithm.

With the increasing effects of climate change, the urgency to step away from fossil fuels is greater than ever before. Electric vehicles (EVs) are one way to diminish these effects, but their widespread adoption is often limited by the insufficient availability of charging stations. In this work, our goal is to expand the infrastructure of EV charging stations, in order to provide a better quality of service in terms of user satisfaction (and availability of charging stations). Specifically, our focus is directed towards urban areas. We first propose a model for the assignment of EV charging demand to stations, framing it as a maximum flow problem. This model is the basis for the evaluation of user satisfaction with a given charging infrastructure. Secondly, we incorporate the maximum flow model into a mixed-integer linear program, where decisions on the opening of new stations and on the expansion of their capacity through additional outlets is accounted for. We showcase our methodology for the city of Montreal, demonstrating the scalability of our approach to handle real-world scenarios. We conclude that considering both spacial and temporal variations in charging demand is meaningful when solving realistic instances.

Accurate Urban SpatioTemporal Prediction (USTP) is of great importance to the development and operation of the smart city. As an emerging building block, multi-sourced urban data are usually integrated as urban knowledge graphs (UrbanKGs) to provide critical knowledge for urban spatiotemporal prediction models. However, existing UrbanKGs are often tailored for specific downstream prediction tasks and are not publicly available, which limits the potential advancement. This paper presents UUKG, the unified urban knowledge graph dataset for knowledge-enhanced urban spatiotemporal predictions. Specifically, we first construct UrbanKGs consisting of millions of triplets for two metropolises by connecting heterogeneous urban entities such as administrative boroughs, POIs, and road segments. Moreover, we conduct qualitative and quantitative analysis on constructed UrbanKGs and uncover diverse high-order structural patterns, such as hierarchies and cycles, that can be leveraged to benefit downstream USTP tasks. To validate and facilitate the use of UrbanKGs, we implement and evaluate 15 KG embedding methods on the KG completion task and integrate the learned KG embeddings into 9 spatiotemporal models for five different USTP tasks. The extensive experimental results not only provide benchmarks of knowledge-enhanced USTP models under different task settings but also highlight the potential of state-of-the-art high-order structure-aware UrbanKG embedding methods. We hope the proposed UUKG fosters research on urban knowledge graphs and broad smart city applications. The dataset and source code are available at https://github.com/usail-hkust/UUKG/.

Predicting traffic incident risks at granular spatiotemporal levels is challenging. The datasets predominantly feature zero values, indicating no incidents, with sporadic high-risk values for severe incidents. Notably, a majority of current models, especially deep learning methods, focus solely on estimating risk values, overlooking the uncertainties arising from the inherently unpredictable nature of incidents. To tackle this challenge, we introduce the Spatiotemporal Zero-Inflated Tweedie Graph Neural Networks (STZITD-GNNs). Our model merges the reliability of traditional statistical models with the flexibility of graph neural networks, aiming to precisely quantify uncertainties associated with road-level traffic incident risks. This model strategically employs a compound model from the Tweedie family, as a Poisson distribution to model risk frequency and a Gamma distribution to account for incident severity. Furthermore, a zero-inflated component helps to identify the non-incident risk scenarios. As a result, the STZITD-GNNs effectively capture the dataset's skewed distribution, placing emphasis on infrequent but impactful severe incidents. Empirical tests using real-world traffic data from London, UK, demonstrate that our model excels beyond current benchmarks. The forte of STZITD-GNN resides not only in its accuracy but also in its adeptness at curtailing uncertainties, delivering robust predictions over short (7 days) and extended (14 days) timeframes.

Franck Bohbot grew up in France, and when he arrived in Los Angeles in 2018 he found the sprawl hard to navigate. One reference point for him was the city's video game bars, whose atmosphere he recognised from favourite adolescent films, including Terminator 2 and Ferris Bueller's Day Off. Having wandered into his first one, Blipsy's in Koreatown, he started chasing their escapist gloom. Several of the bars were bathed in light reminiscent of painter Edward Hopper's lonely Nighthawks. In nearly all cases his camera found images whose timeframe was hard to locate: the arcade bars were rooted in 1980s gaming culture – Pac-Man, Space Invaders, Track & Field were staple machines – but their regulars, as here in Barcade in the northeast suburb of Highland Park, often referenced styles from the 1950s onwards.