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Article: Real-time estimation of multi-class path travel times using multi-source traffic data
Title | Real-time estimation of multi-class path travel times using multi-source traffic data |
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Authors | |
Issue Date | 1-Mar-2024 |
Publisher | Elsevier |
Citation | Expert Systems with Applications, 2024, v. 237 How to Cite? |
Abstract | In practice, most of the intelligent transportation systems provide average travel times of all vehicles on selected paths in real time on a regular basis. However, path travel times of different vehicles could vary widely under different traffic conditions. There is a need to consider the differences in vehicle classes for path travel time estimation. This paper proposes a novel modeling framework that considers variance–covariance relationships between vehicle classes for real-time estimation of multi-class path travel times with use of multi-source traffic data collected from various types of sensors. The proposed methodology is examined with a case study of a selected urban expressway in Hong Kong with data obtained from multiple sources. The path travel time estimates by vehicle class are validated and the results demonstrate the merits and performance of the proposed framework. |
Persistent Identifier | http://hdl.handle.net/10722/332035 |
ISSN | 2023 Impact Factor: 7.5 2023 SCImago Journal Rankings: 1.875 |
DC Field | Value | Language |
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dc.contributor.author | Li, Ang | - |
dc.contributor.author | Lam, William HK | - |
dc.contributor.author | Ma, Wei | - |
dc.contributor.author | Wong, SC | - |
dc.contributor.author | Chow, Andy HF | - |
dc.contributor.author | Lam, Tam Mei | - |
dc.date.accessioned | 2023-09-28T05:00:25Z | - |
dc.date.available | 2023-09-28T05:00:25Z | - |
dc.date.issued | 2024-03-01 | - |
dc.identifier.citation | Expert Systems with Applications, 2024, v. 237 | - |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.uri | http://hdl.handle.net/10722/332035 | - |
dc.description.abstract | <p>In practice, most of the <a href="https://www.sciencedirect.com/topics/engineering/intelligent-transportation-system" title="Learn more about intelligent transportation systems from ScienceDirect's AI-generated Topic Pages">intelligent transportation systems</a> provide average travel times of all vehicles on selected paths in real time on a regular basis. However, path travel times of different vehicles could vary widely under different traffic conditions. There is a need to consider the differences in vehicle classes for path travel time estimation. This paper proposes a novel modeling framework that considers variance–covariance relationships between vehicle classes for real-time estimation of multi-class path travel times with use of multi-source traffic data collected from various types of sensors. The proposed methodology is examined with a case study of a selected urban expressway in Hong Kong with data obtained from multiple sources. The path travel time estimates by vehicle class are validated and the results demonstrate the merits and performance of the proposed framework.<br></p> | - |
dc.language | eng | - |
dc.publisher | Elsevier | - |
dc.relation.ispartof | Expert Systems with Applications | - |
dc.title | Real-time estimation of multi-class path travel times using multi-source traffic data | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.eswa.2023.121613 | - |
dc.identifier.volume | 237 | - |
dc.identifier.issnl | 0957-4174 | - |