Understanding spatio-temporal mobility patterns for seniors, child/student and adult using smart card data
Commutes in urban areas create interesting travel patterns that are often stored in regional transportation databases. These patterns can vary based on the day of the week, the time of the day, and commuter type. This study proposes methods to detect underlying spatio-temporal variability among thre...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2014-11-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-1/167/2014/isprsarchives-XL-1-167-2014.pdf |
| Summary: | Commutes in urban areas create interesting travel patterns that are often stored in regional transportation databases. These patterns
can vary based on the day of the week, the time of the day, and commuter type. This study proposes methods to detect underlying
spatio-temporal variability among three groups of commuters (senior citizens, child/students, and adults) using data mining and
spatial analytics. Data from over 36 million individual trip records collected over one week (March 2012) on the Singapore bus and
Mass Rapid Transit (MRT) system by the fare collection system were used. Analyses of such data are important for transportation
and landuse designers and contribute to a better understanding of urban dynamics.
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Specifically, descriptive statistics, network analysis, and spatial analysis methods are presented. Descriptive variables were proposed
such as density and duration to detect temporal features of people. A directed weighted graph G ≡ (N , L, W) was defined to
analyze the global network properties of every pair of the transportation link in the city during an average workday for all three
categories. Besides, spatial interpolation and spatial statistic tools were used to transform the discrete network nodes into structured
human movement landscape to understand the role of transportation systems in urban areas. The travel behaviour of the three
categories follows a certain degree of temporal and spatial universality but also displays unique patterns within their own specialties.
Each category is characterized by their different peak hours, commute distances, and specific locations for travel on weekdays. |
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| ISSN: | 1682-1750 2194-9034 |