Strategies for real-time spatial analysis using massively parallel SIMD computers: an application to urban traffic flow analysis

Abstract

The current research focuses upon the development of a methodology for undertaking real-time spatial analysis in a supercomputing environment, specifically using massively parallel SIMD computers. Several approaches that can be used to explore the parallelization characteristics of spatial problems are introduced. Within the focus of a methodology directed toward spatial data parallelism, strategies based on both location-based data decomposition and object-based data decomposition are proposed and a programming logic for spatial operations at local, neighborhood and global levels is also recommended. An empirical study of real-time traffic flow analysis shows the utility of the suggested approach for a complex, spatial analysis situation. The empirical example demonstrates that the proposed methodology, especially when combined with appropriate programming strategies, is preferable in situations where critical, real-time, spatial analysis computations are required. The implementation of this example in a parallel environment also points out some interesting theoretical questions with respect to the theoretical basis underlying the analysis of large networks.