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Move: Mobile Observers Variants and Extensions.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Move: Mobile Observers Variants and Extensions./
作者:	Florin, Ryan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	195 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
Contained By:	Dissertations Abstracts International83-04B.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28649881
ISBN:	9798460432868

Move: Mobile Observers Variants and Extensions.
Florin, Ryan.

Move: Mobile Observers Variants and Extensions. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 195 p.

Source: Dissertations Abstracts International, Volume: 83-04, Section: B.

Thesis (Ph.D.)--Old Dominion University, 2021.

This item must not be sold to any third party vendors.

Traffic state estimation is a fundamental task of Intelligent Transportation Systems. Recent advances in sensor technology and emerging computer and vehicular communications paradigms have brought the task of estimating traffic state parameters in real-time within reach.This has led to the main research question of this thesis: Can a vehicle accurately estimate traffic parameters using onboard resources shared through CV technology in a lightweight manner without utilizing centralized or roadside infrastructure?In 1954 Wardrop and Charlesworth proposed the Moving Observer method to measure traffic parameters based on an observed number of vehicle passes. We start by proposing methods for detecting vehicle passes using both radar and V2X as a well as with V2X only.Next, a modernization of the Moving Observer method, called the MO1 method, using the capabilities of modern vehicles is proposed which mitigates some of the limitations of the original method. The results show our method is able to provide estimates comparable to stationary observer methods, even in low flow scenarios.The MO2 method also utilizes two vehicles traveling in the same direction to determine a density between the two vehicles. Again, the results show this method provides estimates comparable to stationary observer methods, even in low flow scenarios.The MO3 method is similar to the MO2 method; however, here the two vehicles travel in oncoming traffic. In doing so, the vehicles' relative velocity is large, leading us to hypothesize that the method will work well in urban traffic. The results for the MO3 method in urban traffic did not meet our expectations, which inspired us to develop the MO3-Flow method. The MO3-Flow method aggregates the counts of multiple vehicles to determine flow.The MO3-Flow method requires additional roadside infrastructure. To remove this need, a Virtual Road Side Unit architecture is proposed. This architecture uses vehicles on the roadway to act in place of roadside infrastructure. We show this architecture provides ample service coverage if the data image is sufficiently small.

ISBN: 9798460432868Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Traffic state estimation

Move: Mobile Observers Variants and Extensions.
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245 1 0 $a Move: Mobile Observers Variants and Extensions.
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300 $a 195 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
500 $a Advisor: Olariu, Stephan.
502 $a Thesis (Ph.D.)--Old Dominion University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Traffic state estimation is a fundamental task of Intelligent Transportation Systems. Recent advances in sensor technology and emerging computer and vehicular communications paradigms have brought the task of estimating traffic state parameters in real-time within reach.This has led to the main research question of this thesis: Can a vehicle accurately estimate traffic parameters using onboard resources shared through CV technology in a lightweight manner without utilizing centralized or roadside infrastructure?In 1954 Wardrop and Charlesworth proposed the Moving Observer method to measure traffic parameters based on an observed number of vehicle passes. We start by proposing methods for detecting vehicle passes using both radar and V2X as a well as with V2X only.Next, a modernization of the Moving Observer method, called the MO1 method, using the capabilities of modern vehicles is proposed which mitigates some of the limitations of the original method. The results show our method is able to provide estimates comparable to stationary observer methods, even in low flow scenarios.The MO2 method also utilizes two vehicles traveling in the same direction to determine a density between the two vehicles. Again, the results show this method provides estimates comparable to stationary observer methods, even in low flow scenarios.The MO3 method is similar to the MO2 method; however, here the two vehicles travel in oncoming traffic. In doing so, the vehicles' relative velocity is large, leading us to hypothesize that the method will work well in urban traffic. The results for the MO3 method in urban traffic did not meet our expectations, which inspired us to develop the MO3-Flow method. The MO3-Flow method aggregates the counts of multiple vehicles to determine flow.The MO3-Flow method requires additional roadside infrastructure. To remove this need, a Virtual Road Side Unit architecture is proposed. This architecture uses vehicles on the roadway to act in place of roadside infrastructure. We show this architecture provides ample service coverage if the data image is sufficiently small.
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650 4 $a Automotive engineering. $3 2181195
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653 $a Intelligent transportation systems
653 $a Onboard resource sharing
653 $a Detecting vehicle passes
653 $a Radar
653 $a Vehicle to Everything
653 $a Moving Observer method
653 $a Estimating traffic density
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690 $a 0540
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773 0 $t Dissertations Abstracts International $g 83-04B.
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