Intelligent smart city parking facility layout optimization based on intelligent IoT analysis Abstract   Research based on the utilization of big data has led to new opportunities and developments in contemporary urban planning and smart city construction. This research is based on open big data of networks, such as the website Where to Go and public comments, spatial syntax theory and technical methods using the current layout of urban parking facilities in Shinan District as a case study of the lack of parking facilities and their unreasonable layout. Based on a quantitative analysis of the distribution of the traffic network and parking facilities on the urban scale and the block scale of the Shinan District of Qingdao, we found that the thermal density of Baidu is closely related to the spatial syntactic parameters of the urban area on the large scale and local scale. A field investigation was performed to analyze the traffic network of the Southern District of Qingdao and propose comprehensive optimization strategies to provide technical support for the construction of intelligent shared parking facilities in cities. This study solves a series of problems, such as unreasonable parking facility allocation around scenic spots in tourist cities, and can also be used as a reference for the optimization of parking facility layouts in similar cities.

Intelligent urban planning on smart city blocks based on bicycle travel data sensing Abstract   The block is an important spatial unit of urban builtup areas. The investigation of the relationship between bicycle travel mode and land use characteristics at the scale of the block can effectively address the contradiction between the supply and demand for transportation under the background of stock characteristics. Herein, 21 typical blocks in Xixe2x80x99an are applied as the research object, Mobike bicycle data, mobile phone signaling data and traditional survey data are employed. The correlation analysis and multiple linear regression analysis methods are used to identify the relationship between bicycle travel and land use. As a result, land use characteristics under the influence of bicycle travel are obtained. The results show that the land use indicators significantly related to street bicycle travel are composed of building mixing degree, floor area ratio and riding connectivity. Each index has a positive effect on street bicycle travel. The building mixing degree has the greatest impact on bicycle travel, followed by the floor area ratio and riding connectivity. In different periods, the impact of three indicators on bicycle travel varies. Especially, the impact on weekend bicycle travel is more obvious. This study can provide a reference for the optimization and transformation of land use in blocks under the influence of bicycle travel.