Average-Speed Forecast and Adjustment via VANETs.
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| Title: | Average-Speed Forecast and Adjustment via VANETs. |
|---|---|
| Authors: | Yang, Jyun-Yan1, Chou, Li-Der1, Tung, Chi-Feng1, Huang, Shu-Min2, Wang, Tong-Wen2 |
| Source: | IEEE Transactions on Vehicular Technology. Nov2013, Vol. 62 Issue 9, p4318-4327. 10p. |
| Subjects: | Vehicular ad hoc networks, Traffic flow, Ad hoc computer networks, Intelligent transportation systems, Artificial neural networks |
| Abstract: | A wet road is slippery so vehicles often slow down their speed to increase the safety margin, thus usually reducing the average speed. This reduction in average speed may produce a chain reaction that shifts, extends, or amplifies a slowdown on downstream road segments. Conventional average-speed forecasting approaches are unable to respond to sudden chain reactions because these approaches do not consider the effect of weather factors and upstream road segments. Since accurate forecast of average speed can improve gas consumption, carbon dioxide emissions, and travel time, this paper proposes a short-term average-speed forecast and adjustment (ASFA) approach based on a study of prediction bias correlation among adjacent road segments and on weather factors, such as temperature, humidity, and rainfall. First, this approach applies an artificial neural network to predict the average speed of a road segment. Then, vehicles can monitor current traffic to calculate average speed via vehicular ad hoc networks (VANETs). Finally, the vehicles adjust the predicted average speed according to the observed average speed. Real traffic measurements and weather data are used for the evaluation of the proposed scheme and Civic Boulevard in Taipei City is selected as the prediction target. The results show that the proposed ASFA improves accuracy by 57.4% when compared with a hybrid approach on an urban street during rush hour. This paper estimates and simulates a case study by aggregating traffic data in 186 ms on Shi-Min Boulevard via the VANETs during rush hour. [ABSTRACT FROM PUBLISHER] |
| Copyright of IEEE Transactions on Vehicular Technology is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 91898876 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Average-Speed Forecast and Adjustment via VANETs. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yang%2C+Jyun-Yan%22">Yang, Jyun-Yan</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Chou%2C+Li-Der%22">Chou, Li-Der</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Tung%2C+Chi-Feng%22">Tung, Chi-Feng</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Huang%2C+Shu-Min%22">Huang, Shu-Min</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Wang%2C+Tong-Wen%22">Wang, Tong-Wen</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Vehicular+Technology%22">IEEE Transactions on Vehicular Technology</searchLink>. Nov2013, Vol. 62 Issue 9, p4318-4327. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Vehicular+ad+hoc+networks%22">Vehicular ad hoc networks</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+flow%22">Traffic flow</searchLink><br /><searchLink fieldCode="DE" term="%22Ad+hoc+computer+networks%22">Ad hoc computer networks</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+transportation+systems%22">Intelligent transportation systems</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: A wet road is slippery so vehicles often slow down their speed to increase the safety margin, thus usually reducing the average speed. This reduction in average speed may produce a chain reaction that shifts, extends, or amplifies a slowdown on downstream road segments. Conventional average-speed forecasting approaches are unable to respond to sudden chain reactions because these approaches do not consider the effect of weather factors and upstream road segments. Since accurate forecast of average speed can improve gas consumption, carbon dioxide emissions, and travel time, this paper proposes a short-term average-speed forecast and adjustment (ASFA) approach based on a study of prediction bias correlation among adjacent road segments and on weather factors, such as temperature, humidity, and rainfall. First, this approach applies an artificial neural network to predict the average speed of a road segment. Then, vehicles can monitor current traffic to calculate average speed via vehicular ad hoc networks (VANETs). Finally, the vehicles adjust the predicted average speed according to the observed average speed. Real traffic measurements and weather data are used for the evaluation of the proposed scheme and Civic Boulevard in Taipei City is selected as the prediction target. The results show that the proposed ASFA improves accuracy by 57.4% when compared with a hybrid approach on an urban street during rush hour. This paper estimates and simulates a case study by aggregating traffic data in 186 ms on Shi-Min Boulevard via the VANETs during rush hour. [ABSTRACT FROM PUBLISHER] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Vehicular Technology is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TVT.2013.2267210 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 4318 Subjects: – SubjectFull: Vehicular ad hoc networks Type: general – SubjectFull: Traffic flow Type: general – SubjectFull: Ad hoc computer networks Type: general – SubjectFull: Intelligent transportation systems Type: general – SubjectFull: Artificial neural networks Type: general Titles: – TitleFull: Average-Speed Forecast and Adjustment via VANETs. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yang, Jyun-Yan – PersonEntity: Name: NameFull: Chou, Li-Der – PersonEntity: Name: NameFull: Tung, Chi-Feng – PersonEntity: Name: NameFull: Huang, Shu-Min – PersonEntity: Name: NameFull: Wang, Tong-Wen IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2013 Type: published Y: 2013 Identifiers: – Type: issn-print Value: 00189545 Numbering: – Type: volume Value: 62 – Type: issue Value: 9 Titles: – TitleFull: IEEE Transactions on Vehicular Technology Type: main |
| ResultId | 1 |
