Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/75031
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Civil and Environmental Engineering | - |
dc.creator | Shi, C | - |
dc.creator | Chen, BY | - |
dc.creator | Lam, WHK | - |
dc.creator | Li, Q | - |
dc.date.accessioned | 2018-03-29T09:34:29Z | - |
dc.date.available | 2018-03-29T09:34:29Z | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | http://hdl.handle.net/10397/75031 | - |
dc.language.iso | en | en_US |
dc.publisher | MDPI AG | en_US |
dc.rights | © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Shi, C., Chen, B. Y., Lam, W. H. K., & Li, Q. (2017). Heterogeneous data fusion method to estimate travel time distributions in congested road networks. Sensors (Switzerland), 17(12), (Suppl. ), 2822, - is available athttps://dx.doi.org/10.3390/s17122822 | en_US |
dc.subject | Data fusion | en_US |
dc.subject | Evidence theory | en_US |
dc.subject | Spatial correlation | en_US |
dc.subject | Travel time distribution | en_US |
dc.subject | Uncertainty | en_US |
dc.title | Heterogeneous data fusion method to estimate travel time distributions in congested road networks | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 17 | - |
dc.identifier.issue | 12 | - |
dc.identifier.doi | 10.3390/s17122822 | - |
dcterms.abstract | Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Sensors (Switzerland), Dec. 2017, v. 17, no. 12, 2822, p. 1-21 | - |
dcterms.isPartOf | Sensors (Switzerland) | - |
dcterms.issued | 2017 | - |
dc.identifier.scopus | 2-s2.0-85037532434 | - |
dc.identifier.artn | 2822 | - |
dc.identifier.rosgroupid | 2017002995 | - |
dc.description.ros | 2017-2018 > Academic research: refereed > Publication in refereed journal | - |
dc.description.validate | 201803 bcma | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Shi_Heterogeneous_Data_Fusion.pdf | 6.67 MB | Adobe PDF | View/Open |
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