Influential factors of spatial performance in metro-led urban underground public space:A case study in Shanghai

Spatial performance measures the space usage of each underground segment in metro-led urban underground public space(UUPS).It usually varies in different UUPS segments and at different periods.Many environmental factors and space attractors can influence spatial performance in UUPS including spatial configurations,transportation facilities,space design characteristics,and commercial and work-ing facilities.This study intends to figure out the temporal and spatial distribution patterns of spatial performance in UUPS and then reveal the main influential factors and their impact mechanisms.The UUPS in Jiangwan–Wujiaochang Sub-center was selected as the study case.Cordon counting methods and multiple regression models were employed to collect the pedestrian data and quantitatively analyze the correlations between pedestrian flows and candidate influential factors.The study verified that spatial configurations were the most important factors instead of underground or surface attractors.There existed an interactive effect among pedestrian move-ments,spatial configurations,and commerce distribution in metro-led UUPS.Walkway width and the distribution of metro stations could partly affect spatial performance.The influential mechanisms of metro stations were different on weekdays and at weekends.Underground segments belonging to shopping malls in UUPS had a negative impact on spatial performance only on weekdays.Results of this study can provide insights for more efficient layout planning and design of metro-led UUPS in Chinese metropolitan cities.

A spatial decomposition approach for accelerating buffer analysis of vector data

Parallel vector buffer analysis approaches can be classified into 2 types:algorithm-oriented parallel strategy and the data-oriented parallel strategy.These methods do not take its applicability on the existing geographic information systems(GIS)platforms into consideration.In order to address the problem,a spatial decomposition approach for accelerating buffer analysis of vector data is proposed.The relationship between the number of vertices of each feature and the buffer analysis computing time is analyzed to generate computational intensity transformation functions(CITFs).Then,computational intensity grids(CIGs)of polyline and polygon are constructed based on the relative CITFs.Using the corresponding CIGs,a spatial decomposition method for parallel buffer analysis is developed.Based on the computational intensity of the features and the sub-domains generated in the decomposition,the features are averagely assigned within the sub-domains into parallel buffer analysis tasks for load balance.Compared with typical regular domain decomposition methods,the new approach accomplishes greater balanced decomposition of computational intensity for parallel buffer analysis and achieves near-linear speedups.

Memory access optimization for particle operations in computational fluid dynamics-discrete element method simulations

Computational Fluid Dynamics-Discrete Element Method is used to model gas-solid systems in several applications in energy,pharmaceutical and petrochemical industries.Computational performance bot-tlenecks often limit the problem sizes that can be simulated at industrial scale.The data structures used to store several millions of particles in such large-scale simulations have a large memory footprint that does not fit into the processor cache hierarchies on current high-performance-computing platforms,leading to reduced computational performance.This paper specifically addresses this aspect of memory access bottlenecks in industrial scale simulations.The use of space-flling curves to improve memory access patterns is described and their impact on computational performance is quantified in both shared and distributed memory parallelization paradigms.The Morton space flling curve applied to uniform grids and k-dimensional tree partitions are used to reorder the particle data-structure thus improving spatial and temporal locality in memory.The performance impact of these techniques when applied to two benchmark problems,namely the homogeneous-cooling-system and a fluidized-bed,are presented.These optimization techniques lead to approximately two-fold performance improvement in particle focused operations such as neighbor-list creation and data-exchange,with~1.5 times overall improvement in a fluidization simulation with 1.27 million particles.