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|dc.title||Understanding individual mobility patterns from urban sensing data: A mobile phone trace example|
|dc.contributor.author||Di Lorenzo, G.|
|dc.identifier.citation||Calabrese, F., Diao, M., Di Lorenzo, G., Ferreira, J., Ratti, C. (2013-01). Understanding individual mobility patterns from urban sensing data: A mobile phone trace example. Transportation Research Part C: Emerging Technologies 26 : 301-313. ScholarBank@NUS Repository. https://doi.org/10.1016/j.trc.2012.09.009|
|dc.description.abstract||Large-scale urban sensing data such as mobile phone traces are emerging as an important data source for urban modeling. This study represents a first step towards building a methodology whereby mobile phone data can be more usefully applied to transportation research. In this paper, we present techniques to extract useful mobility information from the mobile phone traces of millions of users to investigate individual mobility patterns within a metropolitan area. The mobile-phone-based mobility measures are compared to mobility measures computed using odometer readings from the annual safety inspections of all private vehicles in the region to check the validity of mobile phone data in characterizing individual mobility and to identify the differences between individual mobility and vehicular mobility. The empirical results can help us understand the intra-urban variation of mobility and the non-vehicular component of overall mobility. More importantly, this study suggests that mobile phone trace data represent a reasonable proxy for individual mobility and show enormous potential as an alternative and more frequently updatable data source and a compliment to the conventional travel surveys in mobility study. © 2012 Elsevier Ltd.|
|dc.subject||Mobile phone traces|
|dc.subject||Vehicle Kilometers Traveled (VKT)|
|dc.description.sourcetitle||Transportation Research Part C: Emerging Technologies|
|Appears in Collections:||Staff Publications|
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