Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/83519
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dc.contributor.authorLu, Yuen
dc.contributor.authorZeng, Zengen
dc.contributor.authorWu, Huayuen
dc.contributor.authorChua, Gim Guanen
dc.contributor.authorZhang, Jingjingen
dc.contributor.editorLinares López, Carlosen
dc.date.accessioned2019-07-04T04:00:30Zen
dc.date.accessioned2019-12-06T15:24:43Z-
dc.date.available2019-07-04T04:00:30Zen
dc.date.available2019-12-06T15:24:43Z-
dc.date.issued2018en
dc.identifier.citationLu, Y., Zeng, Z., Wu, H., Chua, G. G., & Zhang, J. (2018). An intelligent system for taxi service : analysis, prediction and visualization. AI Communications, 31(1), 33-46. doi:10.3233/AIC-170747en
dc.identifier.issn0921-7126en
dc.identifier.urihttps://hdl.handle.net/10356/83519-
dc.identifier.urihttp://hdl.handle.net/10220/49125en
dc.description.abstractThe fast advancements in sensor data acquisition and vehicle telematics facilitate data collection from taxis and thus, enable building a system to monitor and analyze the citywide taxi service. In this paper, we present a novel and practical system for taxi service analytics and visualization. By utilizing both real time and historical taxi data, the system conducts the estimation on region based passenger wait time for taxi, where recurrent neural network (RNN) and deep learning algorithms are used to build a predictive model. The built RNN-based predictive model achieves 73.3% overall accuracy, which is significantly higher than other classic models. Meanwhile, the system conducts the analytics on the taxi pickup hotspots and trip distributions. The experimental results show that around 97% trips are accurately identified and more than 200 hotspots in the city are successfully detected. Moreover, a novel three dimensional (3D) visualization together with the informative user interface is designed and implemented to ease the information access, and to help system users to understand the characteristics and gain insights of the taxi service.en
dc.language.isoenen
dc.relation.ispartofseriesAI Communicationsen
dc.rights© 2018 IOS Press and the Authors. All rights reserved.en
dc.subjectEngineering::Electrical and electronic engineeringen
dc.subjectRecurrent Neural Networken
dc.subjectDeep Learningen
dc.titleAn intelligent system for taxi service : analysis, prediction and visualizationen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen
dc.identifier.doi10.3233/AIC-170747en
item.fulltextNo Fulltext-
item.grantfulltextnone-
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