2019
Feng, T.; Timmermans, H. J. P.
Integrated imputation of activity-travel diaries incorporating the measurement of uncertainty Journal Article
In: Transportation Planning and Technology, vol. 42, iss. 3, 2019, ISSN: 10290354.
Abstract | Links | BibTeX | Tags: Activity-travel diary, GPS, imputation, travel survey, trip patterns, uncertainty
@article{Feng2019,
title = {Integrated imputation of activity-travel diaries incorporating the measurement of uncertainty},
author = {T. Feng and H. J. P. Timmermans},
doi = {10.1080/03081060.2019.1576384},
issn = {10290354},
year = {2019},
date = {2019-01-01},
journal = {Transportation Planning and Technology},
volume = {42},
issue = {3},
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.},
keywords = {Activity-travel diary, GPS, imputation, travel survey, trip patterns, uncertainty},
pubstate = {published},
tppubtype = {article}
}
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.
2016
Feng, T.; Timmermans, H. J. P.
Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data Journal Article
In: Transportation Planning and Technology, vol. 39, iss. 2, 2016, ISSN: 10290354.
Abstract | Links | BibTeX | Tags: activity-travel data, Bayesian network, classification algorithm, data imputation, decision tree, Global Positioning System (GPS), rules, travel survey
@article{Feng2016,
title = {Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data},
author = {T. Feng and H. J. P. Timmermans},
doi = {10.1080/03081060.2015.1127540},
issn = {10290354},
year = {2016},
date = {2016-01-01},
journal = {Transportation Planning and Technology},
volume = {39},
issue = {2},
abstract = {Global Positioning System (GPS) technologies have been increasingly considered as an alternative to traditional travel survey methods to collect activity-travel data. Algorithms applied to extract activity-travel patterns vary from informal ad-hoc decision rules to advanced machine learning methods and have different accuracy. This paper systematically compares the relative performance of different algorithms for the detection of transportation modes and activity episodes. In particular, naive Bayesian, Bayesian network, logistic regression, multilayer perceptron, support vector machine, decision table, and C4.5 algorithms are selected and compared for the same data according to their overall error rates and hit ratios. Results show that the Bayesian network has a better performance than the other algorithms in terms of the percentage correctly identified instances and Kappa values for both the training data and test data, in the sense that the Bayesian network is relatively efficient and generalizable in the context of GPS data imputation.},
keywords = {activity-travel data, Bayesian network, classification algorithm, data imputation, decision tree, Global Positioning System (GPS), rules, travel survey},
pubstate = {published},
tppubtype = {article}
}
Global Positioning System (GPS) technologies have been increasingly considered as an alternative to traditional travel survey methods to collect activity-travel data. Algorithms applied to extract activity-travel patterns vary from informal ad-hoc decision rules to advanced machine learning methods and have different accuracy. This paper systematically compares the relative performance of different algorithms for the detection of transportation modes and activity episodes. In particular, naive Bayesian, Bayesian network, logistic regression, multilayer perceptron, support vector machine, decision table, and C4.5 algorithms are selected and compared for the same data according to their overall error rates and hit ratios. Results show that the Bayesian network has a better performance than the other algorithms in terms of the percentage correctly identified instances and Kappa values for both the training data and test data, in the sense that the Bayesian network is relatively efficient and generalizable in the context of GPS data imputation.