Integrated imputation of activity-travel diaries incorporating the measurement of uncertainty

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Procedures to transform GPS tracks into activity-travel diaries have been increasingly addressed due to their potential benefit to replace traditional methods used in travel surveys. Existing approaches for data annotation however are not sufficiently accurate, which normally involves a prompted recall survey for data validation. Imputation algorithms for transportation mode detection seem to be largely dependent on speed-related features, which may blur the quality of classification results, especially with transportation modes having similar speeds. Therefore, in this paper we propose an enhanced integrated imputation approach by incorporating the critical indicators related to trip patterns, reflecting the effects of uncertain travel environments, including bus stops and speed percentiles. A two-step procedure which embeds a segmentation model and a transportation mode inference model is designed and examined based on purified prompted recall data collected in a large-scale travel survey. Results show the superior performance of the proposed approach, where the overall accuracy at trip level reaches 93.2% and 88.1% for training and surveyed data, respectively.
LanguageEnglish
Pages274-292
JournalTransportation Planning and Technology
Volume42
Issue number3
DOIs
StatePublished - 2019

Fingerprint

transportation mode
travel
uncertainty
Global positioning system
integrated approach
segmentation
transform
GPS
Uncertainty
performance
speed

Cite this

@article{acde370f5f0f49fa93645b2f47d1a268,
title = "Integrated imputation of activity-travel diaries incorporating the measurement of uncertainty",
abstract = "Procedures to transform GPS tracks into activity-travel diaries have been increasingly addressed due to their potential benefit to replace traditional methods used in travel surveys. Existing approaches for data annotation however are not sufficiently accurate, which normally involves a prompted recall survey for data validation. Imputation algorithms for transportation mode detection seem to be largely dependent on speed-related features, which may blur the quality of classification results, especially with transportation modes having similar speeds. Therefore, in this paper we propose an enhanced integrated imputation approach by incorporating the critical indicators related to trip patterns, reflecting the effects of uncertain travel environments, including bus stops and speed percentiles. A two-step procedure which embeds a segmentation model and a transportation mode inference model is designed and examined based on purified prompted recall data collected in a large-scale travel survey. Results show the superior performance of the proposed approach, where the overall accuracy at trip level reaches 93.2{\%} and 88.1{\%} for training and surveyed data, respectively.",
author = "T. Feng and H.J.P. Timmermans",
year = "2019",
doi = "10.1080/03081060.2019.1576384",
language = "English",
volume = "42",
pages = "274--292",
journal = "Transportation Planning and Technology",
issn = "0308-1060",
publisher = "Taylor and Francis Ltd.",
number = "3",

}

Integrated imputation of activity-travel diaries incorporating the measurement of uncertainty. / Feng, T.; Timmermans, H.J.P.

In: Transportation Planning and Technology, Vol. 42, No. 3, 2019, p. 274-292.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Integrated imputation of activity-travel diaries incorporating the measurement of uncertainty

AU - Feng,T.

AU - Timmermans,H.J.P.

PY - 2019

Y1 - 2019

N2 - Procedures to transform GPS tracks into activity-travel diaries have been increasingly addressed due to their potential benefit to replace traditional methods used in travel surveys. Existing approaches for data annotation however are not sufficiently accurate, which normally involves a prompted recall survey for data validation. Imputation algorithms for transportation mode detection seem to be largely dependent on speed-related features, which may blur the quality of classification results, especially with transportation modes having similar speeds. Therefore, in this paper we propose an enhanced integrated imputation approach by incorporating the critical indicators related to trip patterns, reflecting the effects of uncertain travel environments, including bus stops and speed percentiles. A two-step procedure which embeds a segmentation model and a transportation mode inference model is designed and examined based on purified prompted recall data collected in a large-scale travel survey. Results show the superior performance of the proposed approach, where the overall accuracy at trip level reaches 93.2% and 88.1% for training and surveyed data, respectively.

AB - Procedures to transform GPS tracks into activity-travel diaries have been increasingly addressed due to their potential benefit to replace traditional methods used in travel surveys. Existing approaches for data annotation however are not sufficiently accurate, which normally involves a prompted recall survey for data validation. Imputation algorithms for transportation mode detection seem to be largely dependent on speed-related features, which may blur the quality of classification results, especially with transportation modes having similar speeds. Therefore, in this paper we propose an enhanced integrated imputation approach by incorporating the critical indicators related to trip patterns, reflecting the effects of uncertain travel environments, including bus stops and speed percentiles. A two-step procedure which embeds a segmentation model and a transportation mode inference model is designed and examined based on purified prompted recall data collected in a large-scale travel survey. Results show the superior performance of the proposed approach, where the overall accuracy at trip level reaches 93.2% and 88.1% for training and surveyed data, respectively.

U2 - 10.1080/03081060.2019.1576384

DO - 10.1080/03081060.2019.1576384

M3 - Article

VL - 42

SP - 274

EP - 292

JO - Transportation Planning and Technology

T2 - Transportation Planning and Technology

JF - Transportation Planning and Technology

SN - 0308-1060

IS - 3

ER -