2015
Feng, T.; Timmermans, H. J. P.
Detecting spatial and temporal route information of GPS traces Proceedings Article
In: 2015, ISSN: 18632351.
Abstract | Links | BibTeX | Tags: Bayesian belief network, GPS, Map matching, Road network
@inproceedings{Feng2015b,
title = {Detecting spatial and temporal route information of GPS traces},
author = {T. Feng and H. J. P. Timmermans},
doi = {10.1007/978-3-319-11463-7_5},
issn = {18632351},
year = {2015},
date = {2015-01-01},
journal = {Lecture Notes in Geoinformation and Cartography},
volume = {214},
abstract = {This paper aims at detecting route information of GPS traces to represent spatial and temporal information of trips. A Bayesian belief network model is used to calculate the probability of a road matching a GPS log point. The algorithm incorporates road network topology, distance from trace nodes to road segments, the angle between two lines, direction difference, accuracy of measured GPS log point, and position of roads. GPS data collected in the Eindhoven region, The Netherlands, is used to examine the performance of this algorithm. Results based on a small sample show that the algorithm has a good performance in both processing efficiency and prediction accuracy of correctly identified instances. Prediction accuracy using a small sample is 87.02 %.},
keywords = {Bayesian belief network, GPS, Map matching, Road network},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper aims at detecting route information of GPS traces to represent spatial and temporal information of trips. A Bayesian belief network model is used to calculate the probability of a road matching a GPS log point. The algorithm incorporates road network topology, distance from trace nodes to road segments, the angle between two lines, direction difference, accuracy of measured GPS log point, and position of roads. GPS data collected in the Eindhoven region, The Netherlands, is used to examine the performance of this algorithm. Results based on a small sample show that the algorithm has a good performance in both processing efficiency and prediction accuracy of correctly identified instances. Prediction accuracy using a small sample is 87.02 %.
Feng, Tao; Timmermans, Harry J P
Detecting spatial and temporal route information of GPS traces Proceedings Article
In: Ivan, Igor; Benenson, Itzhak; Jiang, Bin; Horák, Jiri; Haworth, James; Inspektor, Tomás (Ed.): pp. 61-75, Kluwer Academic Publishers, 2015, ISBN: 9783319114620, (11th Symposium on Geoinformatics for Intelligent Transportation (GIS Ostrava 2014), GIS Ostrava 2014 ; Conference date: 27-01-2014 Through 29-01-2014).
Abstract | Links | BibTeX | Tags: Bayesian belief network, GPS, Map matching, Road network
@inproceedings{Feng2015d,
title = {Detecting spatial and temporal route information of GPS traces},
author = {Tao Feng and Harry J P Timmermans},
editor = {Igor Ivan and Itzhak Benenson and Bin Jiang and Jiri Horák and James Haworth and Tomás Inspektor},
doi = {10.1007/978-3-319-11463-7_5},
isbn = {9783319114620},
year = {2015},
date = {2015-01-01},
journal = {Geoinformatics for Intelligent Transportation},
pages = {61-75},
publisher = {Kluwer Academic Publishers},
abstract = {This paper aims at detecting route information of GPS traces to represent spatial and temporal information of trips. A Bayesian belief network model is used to calculate the probability of a road matching a GPS log point. The algorithm incorporates road network topology, distance from trace nodes to road segments, the angle between two lines, direction difference, accuracy of measured GPS log point, and position of roads. GPS data collected in the Eindhoven region, The Netherlands, is used to examine the performance of this algorithm. Results based on a small sample show that the algorithm has a good performance in both processing efficiency and prediction accuracy of correctly identified instances. Prediction accuracy using a small sample is 87.02 %.},
note = {11th Symposium on Geoinformatics for Intelligent Transportation (GIS Ostrava 2014), GIS Ostrava 2014 ; Conference date: 27-01-2014 Through 29-01-2014},
keywords = {Bayesian belief network, GPS, Map matching, Road network},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper aims at detecting route information of GPS traces to represent spatial and temporal information of trips. A Bayesian belief network model is used to calculate the probability of a road matching a GPS log point. The algorithm incorporates road network topology, distance from trace nodes to road segments, the angle between two lines, direction difference, accuracy of measured GPS log point, and position of roads. GPS data collected in the Eindhoven region, The Netherlands, is used to examine the performance of this algorithm. Results based on a small sample show that the algorithm has a good performance in both processing efficiency and prediction accuracy of correctly identified instances. Prediction accuracy using a small sample is 87.02 %.
2014
Feng, T.; Timmermans, H. J. P.
Superimposing activity–travel sequence conditions on GPS data imputation Journal Article
In: Journal of Location Based Services, vol. 8, iss. 4, 2014, ISSN: 17489733.
Abstract | Links | BibTeX | Tags: Bayesian belief network, imputation algorithm, prompted recall
@article{Feng2014,
title = {Superimposing activity–travel sequence conditions on GPS data imputation},
author = {T. Feng and H. J. P. Timmermans},
doi = {10.1080/17489725.2014.977361},
issn = {17489733},
year = {2014},
date = {2014-01-01},
journal = {Journal of Location Based Services},
volume = {8},
issue = {4},
abstract = {Applications of new technology in travel surveys have demonstrated the possibility to obtain good quality activity data than traditional survey methods. However, the quality of the imputation diary data highly depends on the predictability of data processing algorithms, which are not fully ready yet. Narrowing the gap between imputation results and true activity–travel patterns is necessary to improve the ease of data confirmation in prompted recall surveys and develop fully automatic data imputation systems. This paper proposes an algorithm to decrease the discrepancies between imputed activity–travel diary and the so-called ground truth. Based on the activity–travel pattern obtained using a Bayesian belief network model, the algorithm takes into account the consistency of the full activity–travel pattern within a day in the sense that the activity–travel sequence is represented in terms of a hierarchical set of tours, and the transportation modes within a tour are logically consistent. We explore three different approaches based on the frequency at the trip/tour level and imputation probability at the epoch level, for each transportation mode. Results obtained based on the test using GPS data in the Netherlands show that the new algorithm significantly improves the imputation accuracy of transportation modes compared with an algorithm that does superimpose these pattern constraints.},
keywords = {Bayesian belief network, imputation algorithm, prompted recall},
pubstate = {published},
tppubtype = {article}
}
Applications of new technology in travel surveys have demonstrated the possibility to obtain good quality activity data than traditional survey methods. However, the quality of the imputation diary data highly depends on the predictability of data processing algorithms, which are not fully ready yet. Narrowing the gap between imputation results and true activity–travel patterns is necessary to improve the ease of data confirmation in prompted recall surveys and develop fully automatic data imputation systems. This paper proposes an algorithm to decrease the discrepancies between imputed activity–travel diary and the so-called ground truth. Based on the activity–travel pattern obtained using a Bayesian belief network model, the algorithm takes into account the consistency of the full activity–travel pattern within a day in the sense that the activity–travel sequence is represented in terms of a hierarchical set of tours, and the transportation modes within a tour are logically consistent. We explore three different approaches based on the frequency at the trip/tour level and imputation probability at the epoch level, for each transportation mode. Results obtained based on the test using GPS data in the Netherlands show that the new algorithm significantly improves the imputation accuracy of transportation modes compared with an algorithm that does superimpose these pattern constraints.
2013
Feng, T.; Timmermans, H. J. P.
Transportation mode recognition using GPS and accelerometer data Journal Article
In: Transportation Research Part C: Emerging Technologies, vol. 37, 2013, ISSN: 0968090X.
Abstract | Links | BibTeX | Tags: Accelerometer, Activity type, Bayesian belief network, GPS, Transportation mode
@article{Feng2013b,
title = {Transportation mode recognition using GPS and accelerometer data},
author = {T. Feng and H. J. P. Timmermans},
doi = {10.1016/j.trc.2013.09.014},
issn = {0968090X},
year = {2013},
date = {2013-01-01},
journal = {Transportation Research Part C: Emerging Technologies},
volume = {37},
abstract = {Potential advantages of global positioning systems (GPS) in collecting travel behavior data have been discussed in several publications and evidenced in many recent studies. Most applications depend on GPS information only. However, transportation mode detection that relies only on GPS information may be erroneous due to variance in device performance and settings, and the environment in which measurements are made. Accelerometers, being used mainly for identifying peoples' physical activities, may offer new opportunities as these devices record data independent of exterior contexts. The purpose of this paper is therefore to examine the merits of employing accelerometer data in combination with GPS data in transportation mode identification. Three approaches (GPS data only, accelerometer data only and a combination of both accelerometer and GPS data) are examined. A Bayesian Belief Network model is used to infer transportation modes and activity episodes simultaneously. Results show that the use of accelerometer data can make a substantial contribution to successful imputation of transportation mode. The accelerometer only approach outperforms the GPS only approach in terms of the predictive accuracy. The approach which combines GPS and accelerometer data yields the best performance. © 2013 Elsevier Ltd.},
keywords = {Accelerometer, Activity type, Bayesian belief network, GPS, Transportation mode},
pubstate = {published},
tppubtype = {article}
}
Potential advantages of global positioning systems (GPS) in collecting travel behavior data have been discussed in several publications and evidenced in many recent studies. Most applications depend on GPS information only. However, transportation mode detection that relies only on GPS information may be erroneous due to variance in device performance and settings, and the environment in which measurements are made. Accelerometers, being used mainly for identifying peoples’ physical activities, may offer new opportunities as these devices record data independent of exterior contexts. The purpose of this paper is therefore to examine the merits of employing accelerometer data in combination with GPS data in transportation mode identification. Three approaches (GPS data only, accelerometer data only and a combination of both accelerometer and GPS data) are examined. A Bayesian Belief Network model is used to infer transportation modes and activity episodes simultaneously. Results show that the use of accelerometer data can make a substantial contribution to successful imputation of transportation mode. The accelerometer only approach outperforms the GPS only approach in terms of the predictive accuracy. The approach which combines GPS and accelerometer data yields the best performance. © 2013 Elsevier Ltd.