A methodological framework to incorporate psychophysiological indicators into transportation modeling
Author
dc.contributor.author
Castro, Marisol
Author
dc.contributor.author
Guevara Cue, Cristián Angelo
Author
dc.contributor.author
Jiménez Molina, Ángel
Admission date
dc.date.accessioned
2020-11-19T18:29:25Z
Available date
dc.date.available
2020-11-19T18:29:25Z
Publication date
dc.date.issued
2020
Cita de ítem
dc.identifier.citation
Transportation Research Part C 118 (2020) 102712
es_ES
Identifier
dc.identifier.other
10.1016/j.trc.2020.102712
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/177807
Abstract
dc.description.abstract
Reporting and hypothetical biases are inherent to canonical methods of transportation data collection and had implied that analysis in this field has often neglected aspects that are strong behavioral drivers, such as uncertainty, physical effort or stress. Granular information on these aspects would allow measuring their valuation and/or addressing a pervasive source of endogeneity. Recent advances in miniaturization and data processing, as well as evidence that indicators from biosensors correlate with psychophysiological states and emotions, suggest that there is an opportunity to close this gap by collecting a new type of data from transportation users. This research works on leveraging this opportunity by putting forward, illustrating and testing a methodological framework to incorporate psychophysiological indicators gathered from biosensors into transportation choice behavioral modeling. The proposed framework adapts the integrated choice and latent variable approach by incorporating the psychophysiological responses as additional indicators of a latent psychophysiological state that covariates with utility. For the practical implementation of the proposed framework we also consider a specific form of aggregation of the indicators across time to avoid the curse of dimensionality arising from the unmanageably large number of folds for integration. The proposed framework is illustrated and validated using Monte Carlo simulations. Besides, a prototype field experiment was designed and performed to confirm the validity of three crucial components of the proposed framework: (i) the relation between transportation markers and emotions; (ii) the possibility of measuring those emotions through biosensors installed on travelers, (iii) and the validity of the proposed aggregation needed for practicality. In the experiment, a public transportation user travelled wearing a Printed Circuit Board that integrated tiny biosensors to capture electrodermal activity, heart rate variation, temperature and acceleration. Results provide positive evidence for the research questions, suggesting the convenience of developing larger data collection efforts in the future to take full advantage of the new framework.