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Title:

Modelling departure time choices using mobile phone data

Accession Number:

01697533

Record Type:

Component

Abstract:

The rapid growth in passive mobility tracking technologies has led to a departure time choice studies based on global positioning system (GPS) data in recent years. GPS data is however still expensive to collect and affected by technical issues like signal losses and battery depletion which create gaps in the data. On the other hand, the rapid growth in mobile phone penetration rates has led to the emergence of alternative passive mobility datasets such as Global System for Mobile communication (GSM) data, which covers wider population and can be used to derive departure time information. This motivates this research where the authors rigorously compare the strengths and weaknesses of real-world GSM and GPS data to investigate their potential use for modelling departure time choice. The authors describe practical approaches to extract relevant information from the passive datasets and propose a modelling framework that accounts for the fact that the desired departure times are unobserved. The authors assume that the preferred departure times randomly vary across the users and apply the mixed logit framework to jointly estimate the distribution parameters of the preferred departure times and the sensitivities to schedule delay. The authors find that fewer time gaps in the GSM data lead to more reliable model results when compared against those based on GPS data, despite the higher location accuracy of the latter. This is also supported by the comparison of the valuation metrics derived from both models, where those obtained from GSM data are found to be closer to those based on traditional data sources.

Supplemental Notes:

This paper was sponsored by TRB committee ADB40 Standing Committee on Transportation Demand Forecasting.

Report/Paper Numbers:

19-03724

Language:

English

Corporate Authors:

Transportation Research Board

Authors:

Bwambale, Andrew
Choudhury, Charisma F
Hess, Stephane

Pagination:

21p

Publication Date:

2019

Conference:

Transportation Research Board 98th Annual Meeting

Location: Washington DC, United States
Date: 2019-1-13 to 2019-1-17
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; References; Tables

Subject Areas:

Data and Information Technology; Planning and Forecasting; Transportation (General)

Source Data:

Transportation Research Board Annual Meeting 2019 Paper #19-03724

Files:

TRIS, TRB, ATRI

Created Date:

Dec 7 2018 9:30AM