Our data scientists presented their papers at the first IEEE International Conference on Data Mining (ICDM) workshop on Data-driven Intelligent Transportation last week. The workshop featured 10 papers that sought to explore how large-scale city data can be used in developing a more intelligent transport system.
Data Scientist Jodi Chiam authored the first of two papers that DataSpark presented:
A comparative study of urban mobility patterns using large-scale spatio-temporal data, By The Anh Dang, Jodi Chiam, and Ying Li, accepted to Workshop on Data-driven Intelligent Transportation, IEEE International Conference on Data Mining (ICDM), 2018
The large scale spatio-temporal data brought about by the ubiquitous wireless networks, mobile phones, and GPS devices present a fertile ground for studying human mobility. These data sources come with high coverage and resolution that enable studies of mobility patterns for human populations at large that other conventional methods such as surveys are not capable of. In this paper, we study anonymized spatio-temporal data from telco networks to understand the variability in human mobility behavior across different geographical regions. We present methodologies for extracting trips and other mobility features from large-scale spatio-temporal data. We also look into daily activity patterns of the populations in two specific cities, Singapore and Sydney. Our results include measures of distance and frequency of people’s travel, as well as their purpose of travel, mode of transport, and route choice. We extract mobility patterns known as motifs. We also define a mobility index to assess the mobility level of individuals and compare it among different regions and demographic groups. This work contributes to a more comprehensive understanding of urban dynamics, supporting smart city development and sustainable urbanization.
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