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ReduxGO: Context-Aware Mobile Recommender Application for Reducing Stop-and-Go Scenario

There are many technologies and applications available that can be used to assist driver to avoid traffic congestion. However, most of the current applications are emphasizing on giving current updates to drivers to avoid the congested area. Based on prior research, the stop-and-go traffic scenario...

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Main Author: Haron, N.
Format: Article
Published: IEEE Computer Society 2017
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017245020&doi=10.1109%2fISMS.2016.83&partnerID=40&md5=f7b1c96be60cf5de5eda38e80836a978
http://eprints.utp.edu.my/20117/
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spelling my.utp.eprints.201172018-04-22T14:41:31Z ReduxGO: Context-Aware Mobile Recommender Application for Reducing Stop-and-Go Scenario Haron, N. There are many technologies and applications available that can be used to assist driver to avoid traffic congestion. However, most of the current applications are emphasizing on giving current updates to drivers to avoid the congested area. Based on prior research, the stop-and-go traffic scenario is one of the main causes of heavy traffic jams. However, there is no specific application that can assist the driver to reduce the stop-and-go traffic condition. Therefore, we propose a mobile application based on context-aware recommender system to aid driver in deciding the right speed to alleviate the stop-and-go scenario. This paper describes the design and implementation of the mobile application called ReduxGO. We have evaluated the application using Technology Acceptance Model (TAM) and the results yield the users' perceptions towards using the application. © 2016 IEEE. IEEE Computer Society 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017245020&doi=10.1109%2fISMS.2016.83&partnerID=40&md5=f7b1c96be60cf5de5eda38e80836a978 Haron, N. (2017) ReduxGO: Context-Aware Mobile Recommender Application for Reducing Stop-and-Go Scenario. Proceedings - International Conference on Intelligent Systems, Modelling and Simulation, ISMS . pp. 349-355. http://eprints.utp.edu.my/20117/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description There are many technologies and applications available that can be used to assist driver to avoid traffic congestion. However, most of the current applications are emphasizing on giving current updates to drivers to avoid the congested area. Based on prior research, the stop-and-go traffic scenario is one of the main causes of heavy traffic jams. However, there is no specific application that can assist the driver to reduce the stop-and-go traffic condition. Therefore, we propose a mobile application based on context-aware recommender system to aid driver in deciding the right speed to alleviate the stop-and-go scenario. This paper describes the design and implementation of the mobile application called ReduxGO. We have evaluated the application using Technology Acceptance Model (TAM) and the results yield the users' perceptions towards using the application. © 2016 IEEE.
format Article
author Haron, N.
spellingShingle Haron, N.
ReduxGO: Context-Aware Mobile Recommender Application for Reducing Stop-and-Go Scenario
author_facet Haron, N.
author_sort Haron, N.
title ReduxGO: Context-Aware Mobile Recommender Application for Reducing Stop-and-Go Scenario
title_short ReduxGO: Context-Aware Mobile Recommender Application for Reducing Stop-and-Go Scenario
title_full ReduxGO: Context-Aware Mobile Recommender Application for Reducing Stop-and-Go Scenario
title_fullStr ReduxGO: Context-Aware Mobile Recommender Application for Reducing Stop-and-Go Scenario
title_full_unstemmed ReduxGO: Context-Aware Mobile Recommender Application for Reducing Stop-and-Go Scenario
title_sort reduxgo: context-aware mobile recommender application for reducing stop-and-go scenario
publisher IEEE Computer Society
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017245020&doi=10.1109%2fISMS.2016.83&partnerID=40&md5=f7b1c96be60cf5de5eda38e80836a978
http://eprints.utp.edu.my/20117/
_version_ 1738656166079102976
score 13.211869

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