Superimposing activity-travel sequence conditions on GPS data imputation

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.