Context-aware stated preferences with smartphone-based travel surveys

Journal Article (2019)
Author(s)

Mazen Danaf (Massachusetts Institute of Technology)

B. Atasoy (TU Delft - Transport Engineering and Logistics)

Carlos Lima Azevedo (Technical University of Denmark (DTU))

Jing Ding-Mastera (Massachusetts Institute of Technology)

Maya Abou-Zeid (American University of Beirut)

Nathaniel Cox (Massachusetts Institute of Technology)

Fang Zhao (Singapore-MIT Alliance)

Moshe E. Ben-Akiva (Massachusetts Institute of Technology)

Research Group
Transport Engineering and Logistics
Copyright
© 2019 Mazen Danaf, B. Atasoy, Carlos Lima de Azevedo, Jing Ding-Mastera, Maya Abou-Zeid, Nathaniel Cox, Fang Zhao, Moshe Ben-Akiva
DOI related publication
https://doi.org/10.1016/j.jocm.2019.03.001
More Info
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Publication Year
2019
Language
English
Copyright
© 2019 Mazen Danaf, B. Atasoy, Carlos Lima de Azevedo, Jing Ding-Mastera, Maya Abou-Zeid, Nathaniel Cox, Fang Zhao, Moshe Ben-Akiva
Research Group
Transport Engineering and Logistics
Volume number
31
Pages (from-to)
35-50
Reuse Rights

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Abstract

Stated preferences surveys are most commonly used to provide behavioral insights on hypothetical travel scenarios such as new transportation services or attribute ranges beyond those observed in existing conditions. When designing SP surveys, considerable care is needed to balance the statistical objectives with the realism of the experiment. This paper presents an innovative method for smartphone-based stated preferences (SP) surveys leveraging state-of-the-art smartphone-based survey platforms and their revealed preferences sensing capabilities. A random experimental design generates context-aware SP profiles using user specific socioeconomic characteristics and past travel data along with relevant web data for scenario generation. The generated choice tasks are automatically validated to reduce the number of dominant or inferior alternatives in real-time, then validated using Monte-Carlo simulations offline. In this paper we focus our attention on mode choice and design an experiment that considers a wide range of possible existing mode alternatives along with a new alternative on-demand mobility service that does not exist in real life. This experiment is then used to collect SP data or a sample of 224 respondents in the Greater Boston Area. A discrete mode choice model is estimated to illustrate the benefit of the proposed method in capturing current context-specific preferences in response to the new scenario.

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