Fast urbanization brings great challenges to sustainable development goals, such as excessive exploitation and population explosion. Classical cellular automata (CA) have been widely used to independently simulate the change of spatial features, i.e. land use, population, economic production, etc. However, most CA models rely on historical data as static driving factors to simulate future scenarios while ignoring the inter-wined influences among multiple features in the development process. To address this issue, this study proposes a spatial cooperative simulation (SCS) approach to simulate the land use, population, and economy changes. The SCS approach starts with a separate CA model to obtain the initial scenes of each feature. Then, the simulation results of each other two features are used as dynamically updated driving factors, rather than the static historical data, to capture the inter-wined influence of multiple features during the development process. This step is iteratively performed until the changes of each feature converge and the final simulation results will be reported. The simulation experiment in Greater Bay Area demonstrates that the SCS approach can well capture the simultaneous development process and outperforms baseline approaches. The SCS approach is capable of forecasting future development scenarios and facilitates spatial planning and infrastructure synergies.

The accurate prediction of travel demand by bus is crucial for effective urban mobility demand management. However, most models of travel demand prediction by bus tend to focus on the bus’s spatiotemporal dependencies, while ignoring the interactions between buses and other transportation modes, such as metros and taxis. We propose a Multiview Spatiotemporal Graph Neural Network (MSTGNN) model to predict short-term travel demand by bus. It emphasizes the ability to capture the interaction dependencies among the travel demand of buses, metros, and taxis. Firstly, a multiview graph consisting of bus, metro, and taxi views is constructed, with each view containing both a local and global graph. Secondly, a multiview attention-based temporal graph convolution module is developed to capture spatiotemporal and cross-view interaction dependencies among different transport modes. Especially, to address the uneven spatial distributions of features in multiview learning, the cross-view spatial feature consistency loss is introduced as an auxiliary loss. Finally, we conduct intensive experiments using a real-world dataset from Shenzhen, China. The results demonstrate that our proposed MSTGNN model performs better than the existing models. Ablation experiments validate the contributions of various modes of transportation to the improvement of the model’s performance.

The coronavirus disease 2019 (COVID-19) epidemic has spread worldwide, posing a great threat to human beings. The stay-home quarantine is an effective way to reduce physical contacts and the associated COVID-19 transmission risk, which requires the support of efficient living materials (such as meats, vegetables, grain, and oil) delivery. Notably, the presence of potential infected individuals increases the COVID-19 transmission risk during the delivery. The deliveryman may be the medium through which the virus spreads among urban residents. However, traditional delivery route optimization methods don't take the virus transmission risk into account. Here, we propose a novel living material delivery route approach considering the possible COVID-19 transmission during the delivery. A complex network-based virus transmission model is developed to simulate the possible COVID-19 infection between urban residents and the deliverymen. A bi-objective model considering the COVID-19 transmission risk and the total route length is proposed and solved by the hybrid meta-heuristics integrating the adaptive large neighborhood search and simulated annealing. The experiment was conducted in Wuhan, China to assess the performance of the proposed approach. The results demonstrate that 935 vehicles will totally travel 56,424.55 km to deliver necessary living materials to 3,154 neighborhoods, with total risk [Formula: see text]. The presented approach reduces the risk of COVID-19 transmission by 67.55% compared to traditional distance-based optimization methods. The presented approach can facilitate a well response to the COVID-19 in the transportation sector.

3-D visualization of location-based services (LBSs) in a geographic information system (GIS) and the Internet-of-Things (IoT) applications could enhance the user experience. Texture is generally the most noticeable visual element in a 3-D indoor model. However, it usually tends to be outdated and becomes inaccurate in complex and dynamic environments. The applications of such 3-D visualizations, especially in indoor environments, are further impeded by a limited capacity to represent frequently changing scenes due to object displacements. To overcome this problem, a novel location and 3-D visual awareness-based method is proposed to automatically register and update a live image onto a 3-D indoor model. In this method, a multisensor fusion-based indoor localization algorithm employs an indoor map to integrate multisource data, including activity detection information, pedestrian dead reckoning (PDR), and 3-D vision-based localization data. Based on the positioning results, we locate the region of the photograph capture within a scene and identify the triangular meshes of the facing wall. The texture is extracted from real-time images through a 3-D indoor understanding technique and automatically fused with the indoor 3-D model. The geometric relationship between texture and the 3-D indoor model is established by calculating the UV coordinates. The experimental results demonstrate that our approach can accurately update the dynamic indoor model textures regardless of the scene. Based on this texture updating technique, users experience an enhanced presentation of LBS information on our 3-D visualization platform.

This paper presents a method to solve the problem of common design structure discovery from a large number of CAD models. First, a CAD model is transformed into a face adjacency graph (FAG) and each node of FAG is mapped to a point in two-dimensional plane after representing face shape information with two coordinates. So the shapes of models are directly compared through the point coordinates of FAGs' nodes. Thus, the common design structures are just the frequent appearing subgraphs of FAGs. Second, we develop an algorithm to discovery frequent subgraphs of FAGs. The main steps of the algorithm include: (1) frequent candidate subgraph generation based on merging of last discovered frequent subgraphs; (2)subgraph matching with graph descriptive code. The experiment shows a reasonable result of the discovered common design structures with our approach.