IDENTIFICATION OF TRANSPORT MODES OF COMMUTERS VIA UNSUPERVISED LEARNING IMPLEMENTED USING MULTISTAGE LEARNER WITH DOMAIN GENERALIZATION

IDENTIFICATION OF TRANSPORT MODES OF COMMUTERS VIA UNSUPERVISED LEARNING IMPLEMENTED USING MULTISTAGE LEARNER WITH DOMAIN GENERALIZATION

Conventional transport mode detection relies either on GPS data or uses supervised learning for transport mode detection, requiring labelled data with hand crafted features. Embodiments of the present disclosure provide a method and system for identification of transport modes of commuters via unsup...

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Bibliographische Detailangaben
Hauptverfasser: RAMAKRISHNAN, RAMESH KUMAR, BANDYOPADHYAY, SOMA, PAL, ARPAN, DATTA, ANISH
Format: Patent
Sprache:eng ; fre ; ger
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
PHYSICS
SIGNALLING
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
TRAFFIC CONTROL SYSTEMS
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Zusammenfassung:Conventional transport mode detection relies either on GPS data or uses supervised learning for transport mode detection, requiring labelled data with hand crafted features. Embodiments of the present disclosure provide a method and system for identification of transport modes of commuters via unsupervised learning implemented using a multistage learner. Unlabeled time series data received from accelerometer of commuters mobiles from a diversified population is processed using a unique journey segment detection technique to eliminate redundant data corresponding to stationary segments of commuter or user. The non-stationary journey segments are represented using domain generalizable Invariant Auto-Encoded Compact Sequence (I-AECS), which is a learned compact representation encompassing the encoded best diversity and commonality of latent feature representation across diverse users and cities. The multistage unsupervised learning model utilizes hierarchical clustering to generate clusters at different levels to infer modes of transport.