東華大學圖書館 |

Traffic Metering in Urban Street Networks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Traffic Metering in Urban Street Networks./
作者:	Mohebifard, Rasool.
面頁冊數:	1 online resource (160 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-06, Section: A.
Contained By:	Dissertations Abstracts International84-06A.
標題:	Decomposition. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30167954click for full text (PQDT)
ISBN:	9798358406056

Traffic Metering in Urban Street Networks.
Mohebifard, Rasool.

Traffic Metering in Urban Street Networks. - 1 online resource (160 pages)

Source: Dissertations Abstracts International, Volume: 84-06, Section: A.

Thesis (Ph.D.)--North Carolina State University, 2022.

Includes bibliographical references

Traffic metering or perimeter control is an effective approach to mitigate congestion in urbanstreet networks. Bimodal traffic signals (i.e., green and red indications only) can be placed at the borders of congested areas similar to on-ramps on freeway facilities to regulate the flow of vehicles. The objective of this study is to develop a methodology for traffic metering optimization and study its effects on traffic operations in urban street networks.This dissertation developed an optimization program that aimed at increasing the number of completed trips in urban street networks by controlling traffic metering signals. The Benders decomposition technique was used to propose a solution technique that solved the program efficiently. The case study results for an urban street network of 20 intersections with 13 boundary gates showed that traffic metering significantly improved traffic operations by reducing the travel time of vehicles inside the network by 30.8% to 34.2% compared to a no-metering strategy using two demand profiles. Some vehicles were delayed at the metering signals; however, traffic metering reduced the system-level travel times by 2.7% to 5.4% by changing the used demand profiles.While the solution technique was effective in providing metering strategies very close to the optimal solutions, it did not scale well with the size of the problem (network), and its runtime increased with the size of the network. In addition, traffic signal timings of intersections downstream of boundary gates were assumed to be predefined. Therefore, a cooperative distributed model predictive control approach was developed to optimize metering and intersection signals simultaneously. The methodology decomposed the network into several sub-networks, allocated computational resources to each sub-network, and reduced the computational complexity of the problem. The results of applying the methodology in the network of 20 intersections showed that the cooperative approach optimized traffic metering and traffic signal timings in real-time. In other words, the time discretization in the analysis was 6 seconds and the runtime of solving each sub-network-level optimization problem was less than 6 seconds. Therefore, the updated solutions were available before getting to the next time step. Moreover, in the evaluated scenarios using two demand profiles, the cooperative approach increased the number of completed trips by 6.0 to 12.8% and 10.9 to 11.0% and reduced the total travel times by 8.1 to 9.0% and 23.6 to 24.2% compared to independent signal control and independent traffic metering, respectively.In the proposed methodologies, the locations of gates were assumed to be predetermined while their locations were also an important factor in traffic metering. On one hand, installation and maintenance of metering signals were costly, and on the other hand, installing a higher number of metering signals provided greater flexibility in regulating the flow of vehicles. To address these concerns, an integrated formulation was developed that optimized static metering locations and dynamic metering levels within a limited budget. A solution technique based on the branch and bound technique was also suggested to iteratively reduce the feasible region of the problem to optimize the decision variables and solve the problem.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798358406056Subjects--Topical Terms:

3561186
Decomposition.
Index Terms--Genre/Form:

542853
Electronic books.

Traffic Metering in Urban Street Networks.
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245 1 0 $a Traffic Metering in Urban Street Networks.
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500 $a Source: Dissertations Abstracts International, Volume: 84-06, Section: A.
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502 $a Thesis (Ph.D.)--North Carolina State University, 2022.
504 $a Includes bibliographical references
520 $a Traffic metering or perimeter control is an effective approach to mitigate congestion in urbanstreet networks. Bimodal traffic signals (i.e., green and red indications only) can be placed at the borders of congested areas similar to on-ramps on freeway facilities to regulate the flow of vehicles. The objective of this study is to develop a methodology for traffic metering optimization and study its effects on traffic operations in urban street networks.This dissertation developed an optimization program that aimed at increasing the number of completed trips in urban street networks by controlling traffic metering signals. The Benders decomposition technique was used to propose a solution technique that solved the program efficiently. The case study results for an urban street network of 20 intersections with 13 boundary gates showed that traffic metering significantly improved traffic operations by reducing the travel time of vehicles inside the network by 30.8% to 34.2% compared to a no-metering strategy using two demand profiles. Some vehicles were delayed at the metering signals; however, traffic metering reduced the system-level travel times by 2.7% to 5.4% by changing the used demand profiles.While the solution technique was effective in providing metering strategies very close to the optimal solutions, it did not scale well with the size of the problem (network), and its runtime increased with the size of the network. In addition, traffic signal timings of intersections downstream of boundary gates were assumed to be predefined. Therefore, a cooperative distributed model predictive control approach was developed to optimize metering and intersection signals simultaneously. The methodology decomposed the network into several sub-networks, allocated computational resources to each sub-network, and reduced the computational complexity of the problem. The results of applying the methodology in the network of 20 intersections showed that the cooperative approach optimized traffic metering and traffic signal timings in real-time. In other words, the time discretization in the analysis was 6 seconds and the runtime of solving each sub-network-level optimization problem was less than 6 seconds. Therefore, the updated solutions were available before getting to the next time step. Moreover, in the evaluated scenarios using two demand profiles, the cooperative approach increased the number of completed trips by 6.0 to 12.8% and 10.9 to 11.0% and reduced the total travel times by 8.1 to 9.0% and 23.6 to 24.2% compared to independent signal control and independent traffic metering, respectively.In the proposed methodologies, the locations of gates were assumed to be predetermined while their locations were also an important factor in traffic metering. On one hand, installation and maintenance of metering signals were costly, and on the other hand, installing a higher number of metering signals provided greater flexibility in regulating the flow of vehicles. To address these concerns, an integrated formulation was developed that optimized static metering locations and dynamic metering levels within a limited budget. A solution technique based on the branch and bound technique was also suggested to iteratively reduce the feasible region of the problem to optimize the decision variables and solve the problem.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Decomposition. $3 3561186
650 4 $a Traffic flow. $3 756182
650 4 $a Neural networks. $3 677449
650 4 $a Traffic control. $3 3686354
650 4 $a Civil engineering. $3 860360
650 4 $a Engineering. $3 586835
650 4 $a Transportation. $3 555912
655 7 $a Electronic books. $2 lcsh $3 542853
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710 2 $a North Carolina State University. $3 1018772
773 0 $t Dissertations Abstracts International $g 84-06A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30167954 $z click for full text (PQDT)