We study the forecasting problem for traffic with dynamic, possibly periodical, and joint spatial-temporal dependency between regions. Given the aggregated inflow and outflow traffic of regions in a city from time slots 0 to $t - 1$ , we predict the traffic at time $t$ for any region. Prior arts in the area often considered the spatial and temporal dependencies in a decoupled manner, or were rather computationally intensive in training with a large number of hyper-parameters which needed tuning. We propose ST-TIS, a novel, lightweight and accurate Spatial-Temporal Transformer with information fusion and region sampling for traffic forecasting. ST-TIS extends the canonical Transformer with information fusion and region sampling. The information fusion module captures the complex spatial-temporal dependency between regions. The region sampling module is to improve the efficiency and prediction accuracy, cutting the computation complexity for dependency learning from $O(n^{2})$ to $O(n\sqrt{n})$ , where $n$ is the number of regions. With far fewer parameters than state-of-the-art deep learning models, ST-TIS's offline training is significantly faster in terms of tuning and computation (with a reduction of up to $90\%$ on training time and network parameters). Notwithstanding such training efficiency, extensive experiments show that ST-TIS is substantially more accurate in online prediction than state-of-the-art approaches (with an average improvement of $9.5\%$ on RMSE, and $12.4\%$ on MAPE compared to STDN and DSAN).

Covid-19 is primarily spread through contact with the virus, which may survive on surfaces with a lifespan of hours or even days if not sanitized. To curb its spread, it is hence of vital importance to detect those who have been in contact with the virus for a sustained period of time, the so-called close contacts . Most of the existing digital approaches for contact tracing focus only on direct face-to-face contacts. There has been little work on detecting indirect environmental contact, which is to detect people coming into a contaminated area with the live virus, i.e., an area last visited by an infected person within the virus lifespan. In this work, we study automatic Internet of Things (IoT) contact tracing when the virus has a lifespan, which may depend on the disinfection frequency at a location. Leveraging the ubiquity of WiFi signals, we propose vContact, a novel, private, pervasive, and fully distributed WiFi-based IoT contact tracing approach. Users carrying an IoT device (phone, wearable, dongle, etc.) continuously scan WiFi access points (APs) and store their hashed IDs. Given a confirmed case, the signals are then uploaded to a server for other users to match in their local IoT devices for virus exposure notification. vContact is not based on device pairing, and no information of other users is stored locally. The confirmed case does not need to have the device for it to work properly. As WiFi data are sampled sporadically and asynchronously, vContact uses novel and effective signal processing approaches and a similarity metric to align and match signals at any time. We conduct extensive indoor and outdoor experiments to validate vContact performance. Our results demonstrate that vContact is effective and accurate for contact detection. The precision, recall, and F1-score of contact detection are high (up to 90%) for close contact proximity (2 m). Its performance is robust against AP numbers, AP changes, and phone heterogeneity. Having implemented vContact as an Android software development kit and installed it on phones and smart watches, we present a case study to demonstrate the validity and implementability of our design in notifying its users about their exposure to the virus with a specific lifespan.

Traditional urban design is time-consuming and laborious. We propose a computer-generated architecture (CGA)-based workflow in this work, with the goal of allowing designers to take advantage of a high level of automation. This workflow is based on procedural modeling. A three-step CGA rule was applied to implement 3D urban procedural modeling, (1) parcel subdivision and clustering, (2) building extrusion, and (3) texture mapping. Parcel subdivision and clustering is the key step of layout modeling, giving the modeler flexibility to adjust the placement and size of the inner building lots. Subsequently, a land-use-based combination of eight common building types and layouts was used to generate various urban forms for different urban functional zones. Finally, individual buildings were decorated by creating texture maps of a planar section of the building facade or, alternatively, decomposing facades into sets of repeating elements and texture maps. We employed the proposed workflow in the H-village urban redevelopment program and an air–rail integration zone development program in Guangzhou. Three design proposals were generated for each project. The results demonstrated that this workflow could generate multiple layout proposals and alternative facade textures quickly and, therefore, address most of the collaborative issues with its analysis functions, including a flexible adjustment mechanism and real-time visualization.

Automated external defibrillators (AEDs) are regarded as the most important public facility after fire extinguishers due to their importance to out-of-hospital cardiac arrest (OHCA) victims. Previous studies focused on the location optimization of the AED, with little attention to inequity among different social groups. To comprehensively investigate the spatial heterogeneity of the AED inequity, we first collected AED data from a WeChat applet. Then, we used the geographically weighted regression (GWR) model to quantify the inequity level and identify the socio-economic status group that faced the worst inequity in each neighborhood. Results showed that immigrants of all ages suffer a more severe AED inequity than residents after controlling population and road density. Immigrants face more severe inequity in downtown, while residents face more severe inequity in the peripheral and outer suburbs. AED inequity among youngsters tends to be concentrated in the center of each district, while inequity among the elderly tends to be distributed at the edge of each district. This study provides a new perspective for investigating the inequity in public facilities, puts forward scientific suggestions for future AED allocation planning, and emphasizes the importance of the equitable access to AED.