Real-time urban traffic state estimation and prediction using a data-fusion framework based on link neighbors
| Main Authors: | Luuk O de Vries, Luc J J Wismans, Eric van Berkum |
|---|---|
| Format: | Proceeding eJournal |
| Terbitan: |
, 2018
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/1456427 |
| ctrlnum |
1456427 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Luuk O de Vries</creator><creator>Luc J J Wismans</creator><creator>Eric van Berkum</creator><date>2018-04-16</date><description>Effective ITS and traffic management purposes requires a complete and accurate information about current and predicted traffic states in the transport network. The current state-of-the-art in literature regarding traffic state estimation and prediction yields efforts which mostly focus on highways, which are not bluntly transferrable to an urban environment and do not maximize the utilization of all available traffic data.
This paper describes the development and assessment of a data-driven traffic state estimation and prediction framework for application in an urban environment. It uses the intuitive relationship between past, current and future traffic states on neighboring links to train and improve estimation/prediction accuracy and fill the gaps on those links where no floating car data are available. Additionally, this framework is tested on the well-known Sioux Falls Scenario. When penetration rate of floating cars is 5%, on average 50% of the urban links are estimated within 5 km/h accuracy. For a prediction horizon of 5 minutes, it performs almost equal with a percentage of 49%.</description><identifier>https://zenodo.org/record/1456427</identifier><identifier>10.5281/zenodo.1456427</identifier><identifier>oai:zenodo.org:1456427</identifier><relation>doi:10.5281/zenodo.1456426</relation><relation>url:https://zenodo.org/communities/tra2018</relation><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode</rights><subject>Traffic Data, Data Fusion Framework, Traffic State Estimation, Traffic State Prediction, Urban Traffic Network, Real-Time, Data-Driven Approach, Link-Neighborhoods.</subject><title>Real-time urban traffic state estimation and prediction using a data-fusion framework based on link neighbors</title><type>Journal:Proceeding</type><type>Journal:Proceeding</type><recordID>1456427</recordID></dc>
|
| format |
Journal:Proceeding Journal Journal:eJournal |
| author |
Luuk O de Vries Luc J J Wismans Eric van Berkum |
| title |
Real-time urban traffic state estimation and prediction using a data-fusion framework based on link neighbors |
| publishDate |
2018 |
| topic |
Traffic Data Data Fusion Framework Traffic State Estimation Traffic State Prediction Urban Traffic Network Real-Time Data-Driven Approach Link-Neighborhoods |
| url |
https://zenodo.org/record/1456427 |
| contents |
Effective ITS and traffic management purposes requires a complete and accurate information about current and predicted traffic states in the transport network. The current state-of-the-art in literature regarding traffic state estimation and prediction yields efforts which mostly focus on highways, which are not bluntly transferrable to an urban environment and do not maximize the utilization of all available traffic data.
This paper describes the development and assessment of a data-driven traffic state estimation and prediction framework for application in an urban environment. It uses the intuitive relationship between past, current and future traffic states on neighboring links to train and improve estimation/prediction accuracy and fill the gaps on those links where no floating car data are available. Additionally, this framework is tested on the well-known Sioux Falls Scenario. When penetration rate of floating cars is 5%, on average 50% of the urban links are estimated within 5 km/h accuracy. For a prediction horizon of 5 minutes, it performs almost equal with a percentage of 49%. |
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IOS17403.1456427 |
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Universitas PGRI Palembang |
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189 |
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library:university library |
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Perpustakaan Universitas PGRI Palembang |
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587 |
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Marga Life in South Sumatra in the Past: Puyang Concept Sacrificed and Demythosized |
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17403 |
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KOTA PALEMBANG |
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SUMATERA SELATAN |
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IOS17403 |
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2022-07-26T05:32:40Z |
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2022-07-26T05:32:40Z |
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dc |
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17.538404 |