On data processing required to derive mobility patterns from passively-generated mobile phone data

Feilong Wang

doi:10.1016/j.trc.2017.12.003

Return

On data processing required to derive mobility patterns from passively-generated mobile phone data

2018-02-01

104

王

王飞龙 (Feilong Wang)

Cynthia Chen *

DOI：10.1016/j.trc.2017.12.003

Original

Save

View PDF

Abstract

En 中文

Passively-generated mobile phone data is emerging as a potential data source for transportation research and applications. Despite the large amount of studies based on the mobile phone data, only a few have reported the properties of such data, and documented how they have processed the data. In this paper, we describe two types of common mobile phone data: Call Details Record (CDR) data and sightings data, and propose a data processing framework and the associated algorithms to address two key issues associated with the sightings data: locational uncertainty and oscillation. We show the effectiveness of our proposed methods in addressing these two issues compared to the state of art algorithms in the field. We also demonstrate that without proper processing applied to the data, the statistical regularity of human mobility patterns-a key, significant trait identified for human mobility-is over-estimated. We hope this study will stimulate more studies in examining the properties of such data and developing methods to address them. Though not as glamorous as those directly deriving insights on mobility patterns (such as statistical regularity), understanding properties of such data and developing methods to address them is a fundamental research topic on which important insights are derived on mobility patterns.

Keywords:

Human mobility trajectory

Locational uncertainty

Oscillation problem

Representativeness issue

Incremental clustering method

Time-window-based method

Statistical regularity

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.