Estimating Link Travel Time Distribution Using Network Tomography Technique

Duan, P; Mao, G; Huang, B; Kang, J

Estimating Link Travel Time Distribution Using Network Tomography Technique

Duan, P Mao, G

Huang, B Kang, J

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, 2019, pp. 2598-2603
Issue Date:: 2019-10-01

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Field	Value	Language
dc.contributor.author	Duan, P
dc.contributor.author	Mao, G https://orcid.org/0000-0002-3598-4949
dc.contributor.author	Huang, B
dc.contributor.author	Kang, J
dc.date	2019-10-27
dc.date.accessioned	2020-06-20T01:38:22Z
dc.date.available	2020-06-20T01:38:22Z
dc.date.issued	2019-10-01
dc.identifier.citation	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, 2019, pp. 2598-2603
dc.identifier.isbn	9781538670248
dc.identifier.uri	http://hdl.handle.net/10453/141576
dc.description.abstract	© 2019 IEEE. Recently, link travel time distribution (LTTD) estimation has gained a lot of interest since the probabilistic model not only captures the dynamic features of link travel time but also provides abundant knowledge like the mean and variance which can be used as indicators to analyze link travel time reliability. However, existing methods still suffer from a number of problems: 1) most studies employ parametric models, e.g., Gaussian, which is only suitable in the limited traffic conditions like free flow or congestion. 2) many techniques heavily rely on the measurements detected on the roads. They cannot be applied to the whole road network since there is absence of observations in some roads due to the limited number of traffic detectors installed in the road network. In lieu of the aforementioned challenges, in the paper, we employ kernel density estimator (KDE) to model LTTD which is validated to be effective in any state of traffic condition. Further, motivated by the network tomography techniques, we propose an expectation maximization (EM) based algorithm to estimate model parameters only with end-to-end (E2E) measurements detected by traffic detectors at or near some road intersections. With 3.0e+07 GPS trajectories collected by the taxicabs in Xi'an, China, the experimental results show that the LTTD estimated by our proposed method are in excellent agreement with the empirical distributions, and better than its counterparts adopting Gaussian and log-normal models.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
dc.relation.ispartof	IEEE Intelligent Transportation Systems Conference
dc.relation.isbasedon	10.1109/ITSC.2019.8917279
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Estimating Link Travel Time Distribution Using Network Tomography Technique
dc.type	Conference Proceeding
utslib.location.activity	Auckland, New Zealand
utslib.for	0805 Distributed Computing
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-20T01:38:19Z
pubs.finish-date	2019-10-30
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-10-27
dc.location	Piscataway, USA

Abstract:

© 2019 IEEE. Recently, link travel time distribution (LTTD) estimation has gained a lot of interest since the probabilistic model not only captures the dynamic features of link travel time but also provides abundant knowledge like the mean and variance which can be used as indicators to analyze link travel time reliability. However, existing methods still suffer from a number of problems: 1) most studies employ parametric models, e.g., Gaussian, which is only suitable in the limited traffic conditions like free flow or congestion. 2) many techniques heavily rely on the measurements detected on the roads. They cannot be applied to the whole road network since there is absence of observations in some roads due to the limited number of traffic detectors installed in the road network. In lieu of the aforementioned challenges, in the paper, we employ kernel density estimator (KDE) to model LTTD which is validated to be effective in any state of traffic condition. Further, motivated by the network tomography techniques, we propose an expectation maximization (EM) based algorithm to estimate model parameters only with end-to-end (E2E) measurements detected by traffic detectors at or near some road intersections. With 3.0e+07 GPS trajectories collected by the taxicabs in Xi'an, China, the experimental results show that the LTTD estimated by our proposed method are in excellent agreement with the empirical distributions, and better than its counterparts adopting Gaussian and log-normal models.

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http://hdl.handle.net/10453/141576