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Data Use and Sharing in Public Transit Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data Use and Sharing in Public Transit Systems./
作者:	Liu, Qi.
面頁冊數:	1 online resource (183 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-03, Section: A.
Contained By:	Dissertations Abstracts International84-03A.
標題:	Transportation. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29396554click for full text (PQDT)
ISBN:	9798351473925

Data Use and Sharing in Public Transit Systems.
Liu, Qi.

Data Use and Sharing in Public Transit Systems. - 1 online resource (183 pages)

Source: Dissertations Abstracts International, Volume: 84-03, Section: A.

Thesis (Ph.D.)--New York University Tandon School of Engineering, 2023.

Includes bibliographical references

This study is concerned with the use of transit data in planning and operations, and the modeling of data-sharing. It is divided into three parts: transit state estimation, data-sharing coopetition, and disruption mitigation. The first part is about using stop count data to infer transit system state for planning or operational purposes. A network-level dynamic transit passenger flow estimation model is proposed, based on congested schedule-based transit equilibrium assignment. A solution algorithm is proposed for the mathematical program with schedule-based transit equilibrium constraints with proven quadratic space- and time-complexities. The error bound is proven to be linearly proportional to the number of measurements under suitable assumptions. Computational experiments are conducted first to verify the methodology with two synthetic data sets (one of which is Sioux Falls compared to a benchmark model), followed by a validation of the method using bus data from Qingpu District in Shanghai, China from July 1, 2016, with 4 bus lines, 120 segments, and 55 bus stops. The estimation results farely well compared with the benchmark method. The core of this study is the second part, in which the data-sharing decision making mechanism is investigated under the context of oligopolistic transit market. Various forms of data sharing are axiomatized. A new way of studying coopetition, especially data-sharing coopetition, is proposed. The problem of the Bayesian game with signal dependence on actions is observed; and a method to handle such dependence is proposed. This part of the study focuses on fixed-route transit service markets. A discrete model is first presented to analyze the data-sharing coopetition of an oligopolistic transit market when externality effect exists. Given a fixed data sharing structure, a Bayesian game is used to capture the competition under uncertainty while a coalition formation model is used to determine the stable data-sharing decisions. A new method of composite coalition is proposed to study efficient markets. An alternative continuous model is proposed to handle large networks using simulation. These models are applied to various types of networks. Test results show that perfect information may lead to perfect selfishness. Sharing more data does not necessarily improve transit service for all groups, at least if transit operators remain noncooperative. Service complementarity does not necessarily guarantee a grand data-sharing coalition. These results can provide insights on policy-making, like whether city authorities should enforce compulsory data-sharing along with cooperation between operators or setup a voluntary data-sharing platform. Many ideas and methods presented in this part are generally applicable, like the data-sharing axiomatization scheme, coopetition modeling, the method to handle Bayesian games with signal dependence on actions, and the idea of coalition composition.The third part of the study is about transit disruption mitigation using historical data (in the form of demand and disruption pattern) and real-time system information. The ability of fast recovery from disruptions is of vital importance for the reliability of transit systems. A hierarchical framework for disruption mitigation is adopted. The mitigation process is decomposed into three phases: (i) network level resource relocation, (ii) execution of relocation on the network, and (iii) line local adjustment. Two phase (i) models (BM, ITM) are presented. Based on the observation that users in urban transit are paying for recurrent network services instead of individual runs, the basic task units to be adjusted in both models are line service level instead of run level. The two models differ in the way they handle random disruption duration. BM uses an expected value and treats duration as deterministic. ITM includes a delay decision to learn from the system before taking substantial actions. The underlying model belongs to the class of nonconvex joint routing and resource allocation (nJRRA) problems; corresponding solution methods are presented for the ϵ-constrained nJRRA as a quadratically constrained quadratic program (QCQP). Five different demand patterns and four different disruption patterns (20 combinations) are tested on a toy transit network. The proposed BB and ITM strategies are compared with line local adjustment (LLA) and bus-bridging (BB) as benchmark strategies. Test results show that ITM outperforms the other strategies under a limited running time of 5 minutes. The optimal strategies for typical combinations of demand and disruption patterns are obtained.

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

Mode of access: World Wide Web

ISBN: 9798351473925Subjects--Topical Terms:

555912
Transportation.
Subjects--Index Terms:

CoopetitionIndex Terms--Genre/Form:

542853
Electronic books.

Data Use and Sharing in Public Transit Systems.
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500 $a Advisor: Chow, Joseph Y.J.
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504 $a Includes bibliographical references
520 $a This study is concerned with the use of transit data in planning and operations, and the modeling of data-sharing. It is divided into three parts: transit state estimation, data-sharing coopetition, and disruption mitigation. The first part is about using stop count data to infer transit system state for planning or operational purposes. A network-level dynamic transit passenger flow estimation model is proposed, based on congested schedule-based transit equilibrium assignment. A solution algorithm is proposed for the mathematical program with schedule-based transit equilibrium constraints with proven quadratic space- and time-complexities. The error bound is proven to be linearly proportional to the number of measurements under suitable assumptions. Computational experiments are conducted first to verify the methodology with two synthetic data sets (one of which is Sioux Falls compared to a benchmark model), followed by a validation of the method using bus data from Qingpu District in Shanghai, China from July 1, 2016, with 4 bus lines, 120 segments, and 55 bus stops. The estimation results farely well compared with the benchmark method. The core of this study is the second part, in which the data-sharing decision making mechanism is investigated under the context of oligopolistic transit market. Various forms of data sharing are axiomatized. A new way of studying coopetition, especially data-sharing coopetition, is proposed. The problem of the Bayesian game with signal dependence on actions is observed; and a method to handle such dependence is proposed. This part of the study focuses on fixed-route transit service markets. A discrete model is first presented to analyze the data-sharing coopetition of an oligopolistic transit market when externality effect exists. Given a fixed data sharing structure, a Bayesian game is used to capture the competition under uncertainty while a coalition formation model is used to determine the stable data-sharing decisions. A new method of composite coalition is proposed to study efficient markets. An alternative continuous model is proposed to handle large networks using simulation. These models are applied to various types of networks. Test results show that perfect information may lead to perfect selfishness. Sharing more data does not necessarily improve transit service for all groups, at least if transit operators remain noncooperative. Service complementarity does not necessarily guarantee a grand data-sharing coalition. These results can provide insights on policy-making, like whether city authorities should enforce compulsory data-sharing along with cooperation between operators or setup a voluntary data-sharing platform. Many ideas and methods presented in this part are generally applicable, like the data-sharing axiomatization scheme, coopetition modeling, the method to handle Bayesian games with signal dependence on actions, and the idea of coalition composition.The third part of the study is about transit disruption mitigation using historical data (in the form of demand and disruption pattern) and real-time system information. The ability of fast recovery from disruptions is of vital importance for the reliability of transit systems. A hierarchical framework for disruption mitigation is adopted. The mitigation process is decomposed into three phases: (i) network level resource relocation, (ii) execution of relocation on the network, and (iii) line local adjustment. Two phase (i) models (BM, ITM) are presented. Based on the observation that users in urban transit are paying for recurrent network services instead of individual runs, the basic task units to be adjusted in both models are line service level instead of run level. The two models differ in the way they handle random disruption duration. BM uses an expected value and treats duration as deterministic. ITM includes a delay decision to learn from the system before taking substantial actions. The underlying model belongs to the class of nonconvex joint routing and resource allocation (nJRRA) problems; corresponding solution methods are presented for the ϵ-constrained nJRRA as a quadratically constrained quadratic program (QCQP). Five different demand patterns and four different disruption patterns (20 combinations) are tested on a toy transit network. The proposed BB and ITM strategies are compared with line local adjustment (LLA) and bus-bridging (BB) as benchmark strategies. Test results show that ITM outperforms the other strategies under a limited running time of 5 minutes. The optimal strategies for typical combinations of demand and disruption patterns are obtained.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
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653 $a Coopetition
653 $a Data sharing
653 $a Disruption mitigation
653 $a Incident management
653 $a Public transit systems
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690 $a 0709
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a New York University Tandon School of Engineering. $b Civil & Urban Engineering. $3 3694665
773 0 $t Dissertations Abstracts International $g 84-03A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29396554 $z click for full text (PQDT)