Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79424
Title: Clustering driving trip trajectory data based on pattern discovery techniques
Authors: Wu, GPK 
Chan, KCC 
Keywords: Spatio-temporal data mining
Trajectory clustering
Urban traffic analytics
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: 2018 IEEE 3rd International Conference on Big Data Analysis, ICBDA 2018, 2018, p. 453-457 How to cite?
Abstract: Identifying patterns to characterize driving human driving styles from driving trip data is a promising and interesting area of research and application. To cluster the driving trips in a set of recorded GPS tracks, this paper presents an information theoretic approach to characterize them based on their occurrences of frequently detected patterns. The patterns are discovered through a statistical significance test on a generated set of spatio-temporal data and its associated attributes that represent the characteristics of recorded GPS data. For evaluating the performance of the proposed approach, a real dataset with ground truth information is tested to validate its clustering power and compare with other approaches. The result indicates the approach is effective and efficient to extract interpretable features to summarize the complex driving behaviors to form a good representation of driving styles for machine learning to achieve good performance.
Description: 3rd IEEE International Conference on Big Data Analysis, ICBDA 2018, Shanghai, China, 9-12 March 2018
URI: http://hdl.handle.net/10397/79424
ISBN: 9781538647936
DOI: 10.1109/ICBDA.2018.8367726
Appears in Collections:Conference Paper

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