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Title:

Educated Rules for the Prediction of Human Mobility Patterns Based on Sparse Social Media and Mobile Phone Data

Accession Number:

01514313

Record Type:

Component

Availability:

Transportation Research Board Business Office

500 Fifth Street, NW
Washington, DC 20001 United States

Abstract:

Traditionally, mobility prediction at any level -as for example city, district regional or national level- relies on household or individual level surveys. Nevertheless, the static information provision from household/individual travel surveys for mobility prediction fails to capture the effects of the fast-evolving mobility trends, particularly today when individuals tend to relocate and change their mobility behavior more frequently than before. This paper presents techniques that handle automatically real-time data from social sensing mechanisms (i.e., social media and mobile phones) to take advantage of the wide deployment of pervasive computing devices and the information exchange through them. The techniques are used to circumvent the shortcomings of traditional data sources and derive insights about the activity patterns of individuals for estimating their mobility behavior. In more detail, the automatic techniques comprise different rule-sets that process information about the type of interactions (i.e., chatting via social media), the timing of interactions, the emotional state of individuals while interacting and the duration and re-currency of interactions to develop the mobility profiles of individuals and forecast their mobility patterns during the week or the weekend. The techniques are validated against a four-month sample using information published on social networks by a set of users from the same city. The output of the techniques can be used for updating regularly the data provided by travel surveys or for developing tailored mobility forecasting models.

Supplemental Notes:

This paper was sponsored by TRB committee ADB40(1) Emerging Methods. Alternate title: Educated Rules for Prediction of Human Mobility Patterns Based on Sparse Social Media and Mobile Phone Data.

Monograph Accession #:

01503729

Report/Paper Numbers:

14-0745

Language:

English

Corporate Authors:

Transportation Research Board

500 Fifth Street, NW
Washington, DC 20001 United States

Authors:

Gkiotsalitis, Konstantinos
Alesiani, Francesco
Baldessari, Roberto

Pagination:

20p

Publication Date:

2014

Conference:

Transportation Research Board 93rd Annual Meeting

Location: Washington DC
Date: 2014-1-12 to 2014-1-16
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; References; Tables

Subject Areas:

Highways; Planning and Forecasting; I72: Traffic and Transport Planning

Source Data:

Transportation Research Board Annual Meeting 2014 Paper #14-0745

Files:

TRIS, TRB, ATRI

Created Date:

Jan 27 2014 2:19PM