Year: 2015
Type: conference-paper
Abstract: Several techniques aim to classify human activity using data from sensors e.g., GPS, accelerometer, Wi-Fi and GSM.The sensor data allow inferring transportation modes as car, bus, walk, and bike.Despite some techniques show improvements in accuracy, researchers constantly deal with issues such as over-segmentation and low precision in trip reporting.Journeys are over-segmented due to the ambiguous situations, for instance: traffic lights, traffic jam, bus stops and weak signal reception.Thereby, current techniques report high misclassification errors.We present a method for detecting changes of transportation mode on a multimodal journey, where the input data regard to the classification of human activities.We use a space transformation for extracting features that identify a transition between two transportation modes.The data are collected from the Google API for Human Activity Classification through a crowdsourcing-based application for smartphones.Results show improvements on precision and accuracy in comparison to initial classification data outcomes.Therefore, our approach reduces the over-segmentation for multimodal journeys.
Authors: Angel J. Lopez, Daniel Ochoa, Sidharta Gautama
Institutions: Ghent University, Escuela Superior Politecnica del Litoral
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W1567041041