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 methodology for data-driven signal...

Wang, Tseng-Jung.

Linked to FindBook

Google Book

Amazon

博客來

A methodology for data-driven signal timing optimization.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A methodology for data-driven signal timing optimization./
Author:	Wang, Tseng-Jung.
Description:	144 p.
Notes:	Source: Dissertation Abstracts International, Volume: 63-12, Section: B, page: 6077.
Contained By:	Dissertation Abstracts International63-12B.
Subject:	Engineering, System Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3073588
ISBN:	0493935010

A methodology for data-driven signal timing optimization.
Wang, Tseng-Jung.

A methodology for data-driven signal timing optimization. - 144 p.

Source: Dissertation Abstracts International, Volume: 63-12, Section: B, page: 6077.

Thesis (Ph.D.)--University of Virginia, 2003.

The overall goals of this dissertation will contribute to the Intelligent Transportation System (ITS) field by developing a methodology to effectively utilize traffic data and to optimize traffic signal control. The statement of this dissertation is that the methodology, integrating data mining techniques, principal variables extraction methodologies, state-based modeling algorithms, and traffic simulations, can be developed to improve the performances of traffic signal control systems.

ISBN: 0493935010Subjects--Topical Terms:

1018128
Engineering, System Science.

A methodology for data-driven signal timing optimization.
LDR:03353nmm 2200313 4500 001 1856042
005 20040616162817.5
008 130614s2003 eng d
020 $a 0493935010
035 $a (UnM)AAI3073588
035 $a AAI3073588
040 $a UnM $c UnM
100 1 $a Wang, Tseng-Jung. $3 1943836
245 1 0 $a A methodology for data-driven signal timing optimization.
300 $a 144 p.
500 $a Source: Dissertation Abstracts International, Volume: 63-12, Section: B, page: 6077.
500 $a Adviser: William T. Scherer.
502 $a Thesis (Ph.D.)--University of Virginia, 2003.
520 $a The overall goals of this dissertation will contribute to the Intelligent Transportation System (ITS) field by developing a methodology to effectively utilize traffic data and to optimize traffic signal control. The statement of this dissertation is that the methodology, integrating data mining techniques, principal variables extraction methodologies, state-based modeling algorithms, and traffic simulations, can be developed to improve the performances of traffic signal control systems.
520 $a ITS systems include large numbers of traffic sensors that collect enormous quantities of data. The historical traffic data is in fact capable of proving abundant amount of information that can aid in the development of improved current control strategies. One data reorganization procedures were developed to refine the raw traffic data. Data mining tools are the keys to explore the in the traffic data. These algorithms help to find the principal variables and to classify traffic data by scientific techniques under scientific logic, not guesstimates or experiences.
520 $a This dissertation describes the research investigating the application of data mining tools to aid in the optimization of traffic signal timing control. Specifically, the principal variables extraction studies make the analysis of large traffic corridor areas are possible. One six-intersection corridor case study was conducted to illustrate that the use of data mining tools. The results of a six-intersection corridor show the techniques of principal variables extraction can effectively reduce the data space and maintain system performances.
520 $a One adaptive signal control model based on the classification of historical traffic data was also introduced, and specified by the results of six-intersection case. Using CART analysis every new surveillance observation can be assigned to one traffic state and one corresponsive signal timing plan is applied to solve this traffic demand. Two artificial traffic conditions were developed to evaluate the adaptive signal control model.
520 $a The results of this dissertation indicated that advanced data mining techniques hold high potential to assist traffic engineers in signal control system design, development and operations. This methodology has good performances in the adaptive signal control model, and the traffic state transition matrices investigation, for the development of automatically, real-time signal control systems.
590 $a School code: 0246.
650 4 $a Engineering, System Science. $3 1018128
690 $a 0790
710 2 0 $a University of Virginia. $3 645578
773 0 $t Dissertation Abstracts International $g 63-12B.
790 1 0 $a Scherer, William T., $e advisor
790 $a 0246
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3073588