國立虎尾科技大學 |

Travel Time Estimation for Congested Urban Traffic.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Travel Time Estimation for Congested Urban Traffic./
Author:	Xu, Zeng.
Description:	1 online resource (212 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-09(E), Section: A.
Contained By:	Dissertation Abstracts International79-09A(E).
Subject:	Transportation. -
Online resource:	click for full text (PQDT)
ISBN:	9780355992175

Travel Time Estimation for Congested Urban Traffic.
Xu, Zeng.

Travel Time Estimation for Congested Urban Traffic. - 1 online resource (212 pages)

Source: Dissertation Abstracts International, Volume: 79-09(E), Section: A.

Thesis (Ph.D.)--New York University Tandon School of Engineering, 2018.

Includes bibliographical references

Traffic congestion is a prevalent problem in most cities worldwide, adversely affecting the economy and the environment, while diminishing the quality of travel through increased travel times. For efficient traffic management strategies to control and mitigate congestion, it is necessary to know the state of the traffic on road networks at any given point in time. Average travel time, speed, and Level of Service (LOS) have been commonly used by traffic engineers to measure the quality of the operational conditions and the performance of a traffic facility. In urban CBD settings, however, the variability in the travel times due to random demand fluctuations, non-recurrent incidents, and other interruptions caused by traffic control devices makes these standard measures less meaningful.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355992175Subjects--Topical Terms:

558117
Transportation.
Index Terms--Genre/Form:

554714
Electronic books.

Travel Time Estimation for Congested Urban Traffic.
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245 1 0 $a Travel Time Estimation for Congested Urban Traffic.
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300 $a 1 online resource (212 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-09(E), Section: A.
500 $a Advisers: Saif Eddin G. Jabari; John C. Falcocchio.
502 $a Thesis (Ph.D.)--New York University Tandon School of Engineering, 2018.
504 $a Includes bibliographical references
520 $a Traffic congestion is a prevalent problem in most cities worldwide, adversely affecting the economy and the environment, while diminishing the quality of travel through increased travel times. For efficient traffic management strategies to control and mitigate congestion, it is necessary to know the state of the traffic on road networks at any given point in time. Average travel time, speed, and Level of Service (LOS) have been commonly used by traffic engineers to measure the quality of the operational conditions and the performance of a traffic facility. In urban CBD settings, however, the variability in the travel times due to random demand fluctuations, non-recurrent incidents, and other interruptions caused by traffic control devices makes these standard measures less meaningful.
520 $a During this research, analysis of the empirical travel time data led to the identification of many peaks in the travel time distribution of congested traffic flow in a densely developed CBD. That is, the distribution of travel times in this type of environment is multi-modal, and not unimodal as is usually assumed. Thus conventional travel time estimations that use indexes from unimodal distributions, such as average speed, as estimators of the traffic condition cannot accurately reflect the genuine situation on the road, particularly as perceived by vehicular travelers. The objective of this research was to develop an analytical model that can accurately reflect the multi-modal aspect of the travel time distribution.
520 $a Time-space diagram analysis and statistical estimation were applied separately to estimate the travel time distribution in the proposed multi-modal framework. Arterial travel time data collected from RFID (radio frequency identification devices) transponders within a typical urban CBD traffic network were used to tune and validate the empirical and mathematical models that estimate the percentage of vehicles making varying numbers of stops along the arterial segment.
520 $a The empirical approach was able to identify clear travel time clusters in the data. Time-space diagrams were used to simulate vehicle trajectories and calculate the travel time thresholds that bound each of the clusters, using historical samples. The empirical experiment result revealed a strong connection between dense traffic signals and multi-modality in the travel time distribution. For the analytical approach, finite mixture models (FMM) were assumed in mathematically approximating the multi-modal distribution of travel time, which were then solved through both Expectation-Maximization and the sparse method. The resulting statistical model expresses quality of service by the proportion of vehicular traffic that experiences varying numbers of stops. The model well represents the clusters of data found in the travel time distribution and thus gives a more realistic picture of the quality of service experienced by users of the facility. Additionally the model is easily used in real-world applications because prior knowledge of the 'ground truth' data, such as detailed signal timing is not needed. Thus the significance of this work is that a model was developed that gives a measure of effectiveness that is sensitive to the roadway users' perspective, is easy to use, and provides a more realistic picture to the traffic engineer of the facility performance.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Transportation. $3 558117
650 4 $a Engineering. $3 561152
650 4 $a Civil engineering. $3 561339
655 7 $a Electronic books. $2 local $3 554714
690 $a 0709
690 $a 0537
690 $a 0543
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a New York University Tandon School of Engineering. $b Civil Engineering. $3 1190516
773 0 $t Dissertation Abstracts International $g 79-09A(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10816030 $z click for full text (PQDT)