東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

A Network Tomography Approach for Tr...

Zhang, Ruoxi.

Linked to FindBook

Google Book

Amazon

博客來

A Network Tomography Approach for Traffic Monitoring in Smart Cities.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Network Tomography Approach for Traffic Monitoring in Smart Cities./
Author:	Zhang, Ruoxi.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	47 p.
Notes:	Source: Masters Abstracts International, Volume: 57-06.
Contained By:	Masters Abstracts International57-06(E).
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10792195
ISBN:	9780438114760

A Network Tomography Approach for Traffic Monitoring in Smart Cities.
Zhang, Ruoxi.

A Network Tomography Approach for Traffic Monitoring in Smart Cities. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 47 p.

Source: Masters Abstracts International, Volume: 57-06.

Thesis (M.S.)--Missouri University of Science and Technology, 2018.

Various urban planning and managing activities required by a Smart City are feasible because of traffic monitoring. As such, the thesis proposes a network tomography-based approach that can be applied to road networks to achieve a cost-efficient, flexible, and scalable monitor deployment. Due to the algebraic approach of network tomography, the selection of monitoring intersections can be solved through the use of matrices, with its rows representing paths between two intersections, and its columns representing links in the road network. Because the goal of the algorithm is to provide a cost-efficient, minimum error, and high coverage monitor set, this problem can be translated into an optimization problem over a matroid, which can be solved efficiently by a greedy algorithm. Also as supplementary, the approach is capable of handling noisy measurements and a measurement-to-path matching. The approach proves a low error and a 90% coverage with only 20% nodes selected as monitors in a downtown San Francisco, CA topology.

ISBN: 9780438114760Subjects--Topical Terms:

523869
Computer science.

A Network Tomography Approach for Traffic Monitoring in Smart Cities.
LDR:01978nmm a2200301 4500 001 2200538
005 20190315110957.5
008 201008s2018 ||||||||||||||||| ||eng d
020 $a 9780438114760
035 $a (MiAaPQ)AAI10792195
035 $a (MiAaPQ)missouriscitech:11198
035 $a AAI10792195
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zhang, Ruoxi. $3 3183249
245 1 2 $a A Network Tomography Approach for Traffic Monitoring in Smart Cities.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 47 p.
500 $a Source: Masters Abstracts International, Volume: 57-06.
500 $a Adviser: Simone Silvestri.
502 $a Thesis (M.S.)--Missouri University of Science and Technology, 2018.
520 $a Various urban planning and managing activities required by a Smart City are feasible because of traffic monitoring. As such, the thesis proposes a network tomography-based approach that can be applied to road networks to achieve a cost-efficient, flexible, and scalable monitor deployment. Due to the algebraic approach of network tomography, the selection of monitoring intersections can be solved through the use of matrices, with its rows representing paths between two intersections, and its columns representing links in the road network. Because the goal of the algorithm is to provide a cost-efficient, minimum error, and high coverage monitor set, this problem can be translated into an optimization problem over a matroid, which can be solved efficiently by a greedy algorithm. Also as supplementary, the approach is capable of handling noisy measurements and a measurement-to-path matching. The approach proves a low error and a 90% coverage with only 20% nodes selected as monitors in a downtown San Francisco, CA topology.
590 $a School code: 0587.
650 4 $a Computer science. $3 523869
650 4 $a Transportation. $3 555912
690 $a 0984
690 $a 0709
710 2 $a Missouri University of Science and Technology. $b Computer Science. $3 3169043
773 0 $t Masters Abstracts International $g 57-06(E).
790 $a 0587
791 $a M.S.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10792195