The increase of consumer income has resulted in the rapid development of the retail industry in China, which provides high market potential for retail companies worldwide. However, site selection for retail shops has been a confusing business issue in practical business decisions. In this study, a two-step hybrid model in site selection for small retail shops was proposed. The two steps were spatial accessibility evaluation and market potential estimation. The spatial accessibility of target regions was evaluated based on the improved gravity model to determine regions that lack retail shops. Then, a PCA (principal component analysis)–BP (backpropagation network) model was established to estimate the market potential in the target regions. The two-step model could determine sites with the most market potential and low competition. We conducted the experiment in Guiyang, China and considered 18 socioeconomic factors to make the site selection convincing. Through the experiment, 42 locations were determined with high business value; the locations were recommended to the new retail shops. The accuracy of the PCA–BP model was then proven satisfactory by comparing it with other regression methods. The proposed model could guide retail chains in enhancing business location planning and formulating regional development policies.

Lampposts, traffic lights, traffic signs, utility poles and so forth are important road furniture in urban areas. The fast and accurate localization and extraction of this type of furniture is urgent for the construction and updating of infrastructure databases in cities. This paper proposes a pipeline for mobile laser scanning data processing to locate and extract road poles. The proposed method is based on the vertical continuity with isolation feature of the pole part and the overall roughness feature of the attachment part of road poles. The isolation feature of the pole part is analysed by constructing two concentric cylinders from bottom to top and there should be no or a limited number of, points between these two cylinders. After splitting up the pole part and the attachment part of a road pole, the roughness of the candidate attachment points is computed and the attachment is obtained by performing region growing method based on roughness values. By applying the proposed pipeline to different situations in two datasets, the proposed method proves to be efficient not only in simple scenes but also in cluttered scenes.

Nowadays, mobile laser scanning is widely used for understanding urban scenes, especially for extraction and recognition of pole-like street furniture, such as lampposts, traffic lights and traffic signs. However, the start-of-art methods may generate low segmentation accuracy in the overlapping scenes, and the object classification accuracy can be highly influenced by the large discrepancy in instance number of different objects in the same scene. To address these issues, we present a complete paradigm for pole-like street furniture segmentation and classification using mobile LiDAR (light detection and ranging) point cloud. First, we propose a 3D density-based segmentation algorithm which considers two different conditions including isolated furniture and connected furniture in overlapping scenes. After that, a vertical region grow algorithm is employed for component splitting and a new shape distribution estimation method is proposed to obtain more accurate global shape descriptors. For object classification, an integrated shape constraint based on the splitting result of pole-like street furniture (SplitISC) is introduced and integrated into a retrieval procedure. Two test datasets are used to verify the performance and effectiveness of the proposed method. The experimental results demonstrate that the proposed method can achieve better classification results from both sites than the existing shape distribution method.

The Chinese retail industry is expected to grow dramatically over the next few years, owing to the rapid increase in purchasing power of Chinese consumers. Retail managers should analyze the market demands and avoid dull sales to promote the sustainable development of the retail industry. Economic sustainability in the retail industry, which refers to a suitable return of investment, requires the implementation of precise product allocation strategies in different regions. This study proposed a hybrid model to evaluate economic sustainability in the preparation of goods of retail shops on the basis of market demand evaluation. Through a grid-based convolutional neural network, a regression model was first established to model the relationship between consumer distribution and the potential market demand. Then, another model was proposed to evaluate the sustainability among regions based on their supply-demand analysis. An experiment was conducted based on the actual sales data of retail shops in Guiyang, China. Results showed an immense diversity of sustainability in the entire city and three classes of regions were distinguished, namely, high, moderate, and limited. Our model was proven to be effective in the sustainability evaluation of supply and demand in the retail industry after validation showed that its accuracy reached 92.8%.

Locality descriptions are generally communicated using reference objects and spatial relations that reflect human spatial cognition. However, uncertainty is inevitable in locality descriptions. Positioning locality with locality description, with a mapping mechanism between the qualitative and quantitative data, is one of the important research issues in next-generation geographic information sciences. Spatial relations play an important role in the uncertainty of positioning locality. In indoor landmark reference systems, the nearest landmarks can be selected when describing localities by using direction relations indoors. By using probability operation, we combine a set of uncertainties, that is, near and direction relations to positioning locality. Some definitions are proposed from cognitive and computational perspectives. We evaluate the performance of our method through indoor cognitive experiments. Test results demonstrate that a positioning accuracy of 3.55 m can be achieved with the semantically derived direction relationships in indoor landmark reference systems.

Transportation systems often use agent-to-agent communication technologies to enhance control performance by transmitting information across wireless networks to keep a reasonable inter-distance between agents. The agent-to-agent data transmission is influenced by the fading channel and the limited communication bandwidth. Denial of Service attack model is used to describe the attack’s influence on control system. Thus, the communication model is built as Bernoulli distribution, Markovian distribution and so on. But in practical situations, it is more complicated than we imagine. To further push the theoretical results to practical situations, we investigate the jamming attack with non-stationary stochastic process in the leader-follower systems and introduce hidden Markovian distribution model (HMM) to describe this special jamming attack. Furthermore, the controller design method is presented to achieve stochastic stability of the leader-follower system. The structure of the control algorithm is a special MPC method, which is divided into a state feedback part and a modification part. The state feedback part is a typical LMIs controller to guarantee the system’s stability, while the modification part is used to improve the influence of the model mismatch and uncertainty. The highlights of the results are the following two points. Firstly, the more complex and precise transportation system model is founded by the hidden Markovian distribution. Secondly, the controller design method is also presented for the multi-agent systems with this HMM transportation system model. The existing results always find the worst case scenario, and design the controller in this scenario. Usually, the controller is very conservative and even unstable. The results in this paper can change the controller in different scenarios to adapt to the corresponding jamming stochastic distribution model, which is the novelty of this paper. We use two examples to illustrate the proposed results. The first one is used to show the ineffectiveness of the results by the simple stochastic distribution model method and the effectiveness of the proposed results in this paper. The second one is used to show that the method can be used in a 3-vehicle-agent system. Note to Practitioners —The paper’s results have some potential applications in logistics industry, automated factory, and flexible automated manufacture systems. In these scenarios, the feed flow mobile robots are widely used to exchange a mass of manufacturing materials. On one hand, usually, single vehicle control theory does not have the group intelligence ability. Its actions only consider the safety and efficiency itself, which may not be the best actions for the whole system. In this paper, we only consider that the vehicles form a platooning, which is the most time of working conditions for mobile robots. On the other hand, usually, the network data exchange environment in manufacturing industry is complex, which is main reason to cause instability of the networked platooning system and also the bottleneck limiting the network data into the closed loop of the control system. In this paper, the new transmission interference model (HMM) is built, whose parameters can be confirmed by the algorithm and the observation data. Based on this new model, the corresponding unique controller is also designed accordingly. The challenge of this method is to find a method that can suppress large disturbances to the maximum extent and maintain system stability. But if the big interference lasts long enough, it means that the system lasts long enough in the open-loop condition, and the system eventually becomes unstable, which is the limitation of our research in theory.