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Sensors 2016, 16(11), 1962; doi:10.3390/s16111962

Automated Urban Travel Interpretation: A Bottom-up Approach for Trajectory Segmentation

Department of Infrastructure Engineering, The University of Melbourne, Parkville 3010, Australia
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Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 1 October 2016 / Revised: 16 November 2016 / Accepted: 17 November 2016 / Published: 23 November 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1455 KB, uploaded 23 November 2016]   |  

Abstract

Understanding travel behavior is critical for an effective urban planning as well as for enabling various context-aware service provisions to support mobility as a service (MaaS). Both applications rely on the sensor traces generated by travellers’ smartphones. These traces can be used to interpret travel modes, both for generating automated travel diaries as well as for real-time travel mode detection. Current approaches segment a trajectory by certain criteria, e.g., drop in speed. However, these criteria are heuristic, and, thus, existing approaches are subjective and involve significant vagueness and uncertainty in activity transitions in space and time. Also, segmentation approaches are not suited for real time interpretation of open-ended segments, and cannot cope with the frequent gaps in the location traces. In order to address all these challenges a novel, state based bottom-up approach is proposed. This approach assumes a fixed atomic segment of a homogeneous state, instead of an event-based segment, and a progressive iteration until a new state is found. The research investigates how an atomic state-based approach can be developed in such a way that can work in real time, near-real time and offline mode and in different environmental conditions with their varying quality of sensor traces. The results show the proposed bottom-up model outperforms the existing event-based segmentation models in terms of adaptivity, flexibility, accuracy and richness in information delivery pertinent to automated travel behavior interpretation. View Full-Text
Keywords: trajectory; trip; activity; GPS; IMU; context; temporal calculi; machine learning; MaaS trajectory; trip; activity; GPS; IMU; context; temporal calculi; machine learning; MaaS
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Das, R.D.; Winter, S. Automated Urban Travel Interpretation: A Bottom-up Approach for Trajectory Segmentation. Sensors 2016, 16, 1962.

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