Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wang, J. Y. | en_US |
dc.contributor.author | Wong, K. I. | en_US |
dc.contributor.author | Chen, Y. Y. | en_US |
dc.date.accessioned | 2014-12-08T15:28:41Z | - |
dc.date.available | 2014-12-08T15:28:41Z | - |
dc.date.issued | 2012 | en_US |
dc.identifier.isbn | 978-1-4673-3063-3 | en_US |
dc.identifier.issn | 2153-0009 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/20748 | - |
dc.description.abstract | Travel time information is a fundamental component in Advanced Traveler Information System. In this paper, we propose a short-term travel time estimation and prediction framework for long freeway corridor, considering measurements from vehicle detectors (VD) and floating car data (FCD). The modeling approach is based on a modified Nearest-Neighborhood (NN) model with threshold and a regression model capturing the within day variations. The advantages are that our approach allows for missing data without the need of data imputation in real-time, and is suitable for travel time prediction of long corridors. The validation analysis using an 88 km long section of freeway shows satisfactory results. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | Advanced Traveler Information System | en_US |
dc.subject | travel time | en_US |
dc.subject | Nearest Neighborhood | en_US |
dc.subject | floating car data | en_US |
dc.subject | vehicle detector | en_US |
dc.title | Short-term Travel Time Estimation and Prediction for Long Freeway Corridor using NN and regression | en_US |
dc.type | Proceedings Paper | en_US |
dc.identifier.journal | 2012 15TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) | en_US |
dc.citation.spage | 582 | en_US |
dc.citation.epage | 587 | en_US |
dc.contributor.department | 運輸與物流管理系 註:原交通所+運管所 | zh_TW |
dc.contributor.department | Department of Transportation and Logistics Management | en_US |
dc.identifier.wosnumber | WOS:000312599600097 | - |
Appears in Collections: | Conferences Paper |