Logistical demand-supply forecasting that evaluates the alignment between projected supply and anticipated demand, is essential for the efficiency and quality of on-demand food delivery platforms and serves as a key indicator for scheduling decisions. Future order distribution information, which reflects the distribution of orders in on-demand food delivery, is crucial for the performance of logistical demand-supply forecasting. Current studies utilize spatial-temporal analysis methods to model future order distribution information from serious time slices. However, learning future order distribution in online delivery platform is a time-series-insensitive problem with strong randomness. These approaches often struggle to effectively capture this information while remaining efficient. This paper proposes an innovative spatiotemporal learning model that utilizes only two graphs (ongoing and global) to learn future order distribution information, achieving superior performance compared to traditional spatial-temporal long-series methods. The main contributions are as follows: (1) The introduction of ongoing and global graphs in logistical demand-supply pressure forecasting compared to traditional long time series significantly enhances forecasting performance.

The past decades witness the significant advancements in time series forecasting (TSF) across various real-world domains, including e-commerce and disease spread prediction. However, TSF is usually constrained by the uncertainty dilemma of predicting future data with limited past observations. To settle this question, we explore the use of Cross-Future Behavior (CFB) in TSF, which occurs before the current time but takes effect in the future. We leverage CFB features and propose the CRoss-Future Behavior Awareness based Time Series Forecasting method (CRAFT). The core idea of CRAFT is to utilize the trend of cross-future behavior to mine the trend of time series data to be predicted. Specifically, to settle the sparse and partial flaws of cross-future behavior, CRAFT employs the Koopman Predictor Module to extract the key trend and the Internal Trend Mining Module to supplement the unknown area of the cross-future behavior matrix. Then, we introduce the External Trend Guide Module with a hierarchical structure to acquire more representative trends from higher levels. Finally, we apply the demand-constrained loss to calibrate the distribution deviation of prediction results. We conduct experiments on real-world dataset. Experiments on both offline large-scale dataset and online A/B test demonstrate the effectiveness of CRAFT. Our dataset and code is available at https://github.com/CRAFTinTSF/CRAFT.

On-demand Food Delivery (OFD) services have become very common around the world. For example, on the Ele.me platform, users place more than 15 million food orders every day. Predicting the Real-time Pressure Signal (RPS) is crucial for OFD services, as it is primarily used to measure the current status of pressure on the logistics system. When RPS rises, the pressure increases, and the platform needs to quickly take measures to prevent the logistics system from being overloaded. Usually, the average delivery time for all orders within a business district is used to represent RPS. Existing research on OFD services primarily focuses on predicting the delivery time of orders, while relatively less attention has been given to the study of the RPS. Previous research directly applies general models such as DeepFM, RNN, and GNN for prediction, but fails to adequately utilize the unique temporal and spatial characteristics of OFD services, and faces issues with insufficient sensitivity during sudden severe weather conditions or peak periods. To address these problems, this paper proposes a new method based on Spatio-Temporal Transformer and Memory Network (STTM). Specifically, we use a novel Spatio-Temporal Transformer structure to learn logistics features across temporal and spatial dimensions and encode the historical information of a business district and its neighbors, thereby learning both temporal and spatial information. Additionally, a Memory Network is employed to increase sensitivity to abnormal events. Experimental results on the real-world dataset show that STTM significantly outperforms previous methods in both offline experiments and the online A/B test, demonstrating the effectiveness of this method.

Russian air defence systems have taken down three unmanned aerial vehicles (UAV) that tried to attack Moscow, the latest raid on Russia's capital by combat drones that authorities have accused Ukraine of launching. Russia's defence ministry said one drone was jammed electronically and crashed into a building in central Moscow early on Wednesday morning, and two more were shot down by air defence systems outside the capital. Moscow's Mayor Sergei Sobyanin said on the Telegram messaging app that one downed drone had hit a building that was under construction in central Moscow, and another was shot down in a district to the west of the city. The second UAV hit a building under construction in the City," Sobyanin said on Telegram. Russia's defence ministry said that the third drone was shot down in the Khimki district of Moscow.

May Mobility launched its first low-speed autonomous shuttle service in Detroit this summer. By March, the Ann Arbor, Michigan-based company will be operating in at least three U.S. cities. The company, which just announced plans to expand to Columbus, Ohio, is planning to add another route in Grand Rapids, Michigan. It's a rapid acceleration for a company that was founded less than two years ago. May Mobility is different from other companies racing to deploy autonomous vehicles at a commercial scale.

Big data, Internet of Things, Machine Learning, and Artificial Intelligence are some of the most talked about innovative technologies that are all set to reshape our future in more ways than one can dream of. AI or Artificial Intelligence refers to any type of intelligent activity, which is performed by mechanical devices rather than humans. We often come across such intelligent devices in our daily lives. Any machine that is capable of acting in response to human speech can be considered to exhibit Artificial Intelligence. Artificial Intelligence is being leveraged to make machines perform actions based on the previous experience.