Abstract
Public transport performance not only directly depicts the convenience of a city’s public transport, but also indirectly reflects urban dwellers' life quality and urban attractiveness. Understanding why some regions are easier to get around by public transport helps to build a green transport friendly city. This paper initiates a new perspective and method to investigate how public transport network’s morphology contributes significantly to its performance. We investigate the significance of morphology from the perspective of graph classification – by extracting the typical local structures, called ``motifs'', from the multi-modal public transport multigraph. A motif is seen as a certain connectivity pattern of different transport modes at a local scale. Combinations of various motifs decide the output of graph classification, particularly, public transport performance. We invent an innovative method to extract motifs on complex spatial multigraphs. The proposed method is adaptable to unify complex factors into one simple form for swift coding, and depends less on observational data to handle data unavailability. In the study area of Beijing, we validate that the measure can infer varied public transport efficiencies and access equalities of different regions. Some typical areas with undeveloped or unevenly distributed public transport are further discussed.
Di Zhu
Professors
I am an Assistant Professor of Geographic Information Science in the Department of Geography, Environment, and Society at the University of Minnesota, Twin Cities. My research interests center around Geospatial Artificial Intelligence (GeoAI), Spatial Analytics, Social Sensing, and Urban Complexities.
Ganmin Yin
PhD Student
2020 - present
My research interests include Human Mobility, Transportation, Urban Data Mining, Social Sensing and GeoAI.
