End-to-end trajectory transportation mode classification using Bi-LSTM recurrent neural network

Hongbin Liu, Ickjai Lee

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

40 Citations (Scopus)

Abstract

Transportation mode classification is a key task in trajectory data mining. It adds human behaviour semantics to raw trajectories for trip recommendation, traffic management and transport planning. Previous approaches require heavy pre-processing and feature extraction processes in order to build a classifier, which is complicated and time-consuming. Recurrent neural network has demonstrated its capacity in sequence modelling tasks ranging from machine translation, speech recognition to image captioning. In this paper, we propose a trajectory transportation mode classification framework that is based on an end-to-end bidirectional LSTM classifier. The proposed classification process does not require any feature extraction process, but automatically learns features from trajectories, and use them for classification. We further improve this framework by feeding the time interval as an external feature by embedding. Our experiments on real GPS datasets demonstrate that our approach outperforms existing methods with regard to AUC.

Original languageEnglish
Title of host publicationProceedings of the 2017 12th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2017
EditorsTianrui Li, Luis Martinez Lopez, Yun Li
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538618295
DOIs
Publication statusPublished - 1 Jul 2017
Externally publishedYes
Event12th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2017 - NanJing, JiangSu, China
Duration: 24 Nov 201726 Nov 2017

Publication series

NameProceedings of the 2017 12th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2017
Volume2018-January

Conference

Conference12th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2017
Country/TerritoryChina
CityNanJing, JiangSu
Period24/11/1726/11/17

Keywords

  • LSTM
  • Recurrent Neural Network
  • Trajectory
  • Transportation mode

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