Abstract
This paper proposed an algorithm to decrease the discrepancies between imputation and
real GPS data. Based on the activity-travel pattern obtained using a Bayesian Belief
Network model, the algorithm takes into account the consistency of the full activitytravel
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 number of
epochs and imputation probabilities to identify the transportation mode for each trip
period between two consecutive activities. In principle, the mode with the highest number
of epochs for which it has the highest probability is selected. The algorithm was tested
using the GPS data recently collected in the Netherlands. Results show that the new
algorithm significantly improves the imputation accuracy of transportation modes.
Original language | English |
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Title of host publication | Proceedings of the NTTS conference, 5-7 March 2013, Brussels, Belgium |
Place of Publication | Brussel, Belgium |
Publication status | Published - 2013 |