Empirical study on taxi's mobility nature in dense urban area

Qiu, Z; Zhang, S; Zhou, W; Yu, S

Empirical study on taxi's mobility nature in dense urban area

Qiu, Z Zhang, S Zhou, W Yu, S

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Publication Type:: Conference Proceeding
Citation:: INFOCOM 2018 - IEEE Conference on Computer Communications Workshops, 2018, pp. 232 - 237
Issue Date:: 2018-07-06

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Field	Value	Language
dc.contributor.author	Qiu, Z	en_US
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Zhou, W	en_US
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743	en_US
dc.date.issued	2018-07-06	en_US
dc.identifier.citation	INFOCOM 2018 - IEEE Conference on Computer Communications Workshops, 2018, pp. 232 - 237	en_US
dc.identifier.isbn	9781538659793	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133460
dc.description.abstract	© 2018 IEEE. Vehicular mobility statistics including vehicle velocity, relative velocity and link duration between vehicles may impact greatly on V2V communications and networking but existing works based on large scale real traces are rarely reported. In this paper, we firstly present the statistical analysis on this topic using the taxi traces in one metropolis of China, which reveals the practical distribution of the mobility statistics mentioned above. We propose a computation methodology with low computation complexity to approximate vehicular communication pattern by analyzing large scale vehicular trace data. By Maximum Likelihood Estimation (MLE), we conclude that the taxi velocity follows normal distribution and the relative velocity follows Logistic distribution in different disconnected distance. Moreover, the link duration is verified to comply with generalized Pareto distribution in different disconnected distance. Such findings are significant for designing practical transmission technology and protocols.	en_US
dc.relation.ispartof	INFOCOM 2018 - IEEE Conference on Computer Communications Workshops	en_US
dc.relation.isbasedon	10.1109/INFCOMW.2018.8406901	en_US
dc.title	Empirical study on taxi's mobility nature in dense urban area	en_US
dc.type	Conference Proceeding
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2018 IEEE. Vehicular mobility statistics including vehicle velocity, relative velocity and link duration between vehicles may impact greatly on V2V communications and networking but existing works based on large scale real traces are rarely reported. In this paper, we firstly present the statistical analysis on this topic using the taxi traces in one metropolis of China, which reveals the practical distribution of the mobility statistics mentioned above. We propose a computation methodology with low computation complexity to approximate vehicular communication pattern by analyzing large scale vehicular trace data. By Maximum Likelihood Estimation (MLE), we conclude that the taxi velocity follows normal distribution and the relative velocity follows Logistic distribution in different disconnected distance. Moreover, the link duration is verified to comply with generalized Pareto distribution in different disconnected distance. Such findings are significant for designing practical transmission technology and protocols.

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http://hdl.handle.net/10453/133460