Time-space and cognition-space transformations for transportation network analysis based on multidimensional scaling and self-organizing map

Zixuan Hong; Fuling Bian

doi:10.1117/12.812870

5 November 2008 Time-space and cognition-space transformations for transportation network analysis based on multidimensional scaling and self-organizing map

Zixuan Hong, Fuling Bian

Author Affiliations +

Proceedings Volume 7144, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics; 71443C (2008) https://doi.org/10.1117/12.812870
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China

Abstract

Geographic space, time space and cognition space are three fundamental and interrelated spaces in geographic information systems for transportation. However, the cognition space and its relationships to the time space and geographic space are often neglected. This paper studies the relationships of these three spaces in urban transportation system from a new perspective and proposes a novel MDS-SOM transformation method which takes the advantages of the techniques of multidimensional scaling (MDS) and self-organizing map (SOM). The MDS-SOM transformation framework includes three kinds of mapping: the geographic-time transformation, the cognition-time transformation and the time-cognition transformation. The transformations in our research provide a better understanding of the interactions of these three spaces and beneficial knowledge is discovered to help the transportation analysis and decision supports.

Citation Download Citation

Zixuan Hong and Fuling Bian "Time-space and cognition-space transformations for transportation network analysis based on multidimensional scaling and self-organizing map", Proc. SPIE 7144, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics, 71443C (5 November 2008); https://doi.org/10.1117/12.812870

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