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Open AccessArticle

Detecting Urban Transport Modes Using a Hybrid Knowledge Driven Framework from GPS Trajectory

Department of Infrastructure Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
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Author to whom correspondence should be addressed.
Academic Editors: Bin Jiang, Constantinos Antoniou and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2016, 5(11), 207; https://doi.org/10.3390/ijgi5110207
Received: 16 September 2016 / Revised: 18 October 2016 / Accepted: 28 October 2016 / Published: 9 November 2016
(This article belongs to the Special Issue Geospatial Big Data and Transport)
Transport mode information is essential for understanding people’s movement behavior and travel demand estimation. Current approaches extract travel information once the travel is complete. Such approaches are limited in terms of generating just-in-time information for a number of mobility based applications, e.g., real time mode specific patronage estimation. In order to detect the transport modalities from GPS trajectories, various machine learning approaches have already been explored. However, the majority of them produce only a single conclusion from a given set of evidences, ignoring the uncertainty of any mode classification. Also, the existing machine learning approaches fall short in explaining their reasoning scheme. In contrast, a fuzzy expert system can explain its reasoning scheme in a human readable format along with a provision of inferring different outcome possibilities, but lacks the adaptivity and learning ability of machine learning. In this paper, a novel hybrid knowledge driven framework is developed by integrating a fuzzy logic and a neural network to complement each other’s limitations. Thus the aim of this paper is to automate the tuning process in order to generate an intelligent hybrid model that can perform effectively in near-real time mode detection using GPS trajectory. Tests demonstrate that a hybrid knowledge driven model works better than a purely knowledge driven model and at per the machine learning models in the context of transport mode detection. View Full-Text
Keywords: GPS; trajectory; fuzzy logic; neuro-fuzzy; transport mode; context; travel behaviour GPS; trajectory; fuzzy logic; neuro-fuzzy; transport mode; context; travel behaviour
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Das, R.D.; Winter, S. Detecting Urban Transport Modes Using a Hybrid Knowledge Driven Framework from GPS Trajectory. ISPRS Int. J. Geo-Inf. 2016, 5, 207.

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