Metro Service Delay Recovery: Comparison of Strategies and Constraints Across Systems

Schmöcker, Jan-Dirk; Cooper, Shoshana; Adeney, William

doi:10.3141/1930-04

Cited by 33 publications

(36 citation statements)

References 3 publications

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“…In addition, network-level rail operations require more robust control because challenges along one small part of the line can have a widespread impact on the whole transit network. In addition, the increased concern among travels means that a small operations error can attract a great deal of public attention, and the negative impacts can be magnified by internet exposure or cell phone networks if issue is not properly addressed in a timely manner [6][7][8].…”

Section: Increasing Public Travel Demand Leads To Great Challenges Tomentioning

confidence: 99%

Challenges and Innovative Solutions in Urban Rail Transit Network Operations and Management: China’s Guangzhou Metro Experience

2016

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Urban rail transit operations have changed from a single line to a multiline network. The network operations have undergone quantitative and qualitative changes, and operations management is facing rapid internal and external changes. Using the Guangzhou Metro network operation practices as a case study, this paper first systematically analyzes the features of the operation scale, the proportion of urban mass transit, the surge in public demand, the security of the operational service capacity, reforms to the operation governance structure, the highspeed expansion of staff, and the development of knowledge and skills in urban mass transit networks. The paper then proposes several responses to the challenges that such networks face; for example, this paper proposes creating an innovative network operations management system, strengthening the management foundation, creating plans to promote operation capacity, enhancing security risk management and equipment quality management, developing a crisis public relations response, and applying information technology. In addition, this paper systematically describes countermeasures for multiline network operations, such as developing a management mechanism for network operations, actively cultivating staff skills, creating innovative transport organization models to enhance operational capacity, establishing a production service assessment system to continuously improve the level of transportation service, establishing a quantifiable safety assessment system and equipment quality index model, strengthening quality controls for security and equipment, extensively using information technology to ensure the health of the urban mass transit network operation, and implementing sustainable development measures.

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Section: Increasing Public Travel Demand Leads To Great Challenges Tomentioning

confidence: 99%

Challenges and Innovative Solutions in Urban Rail Transit Network Operations and Management: China’s Guangzhou Metro Experience

2016

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“…Equation 3 relates the temporal change of m s (k, τ ) to the diffusion, passenger flow, and routing matrix that come along with every passenger transfer t ∈ • k ∪ k • joining or leaving it, in which • k/k • denotes the set of all incoming/outgoing passenger transfers w.r.t. k. In particular, D s (t), D s : T s → {K ∈ R n×n : K[i, j] = 0 for any i = j} specifies the diffusion, φ s (t, τ ), φ s : T s × R ≥0 → R ≥0 n , the passenger flow, and R s (t), R s : T s → {K ∈ [0, 1] n×n : K[·, i] ∈ {0, 1} for any i ∈ {1, 2, .…”

Section: B Balance Equationsmentioning

confidence: 99%

“…On the one hand, there are highly unpredictable asynchronous events for which statistical data is hard to obtain. It is thus difficult to include them in the daily network operation [3], e.g. by means of minute-by-minute or hourly forecasts.…”

Section: Introductionmentioning

confidence: 99%

Predicting traffic load in public transportation networks

Haar

Theissing

2016

2016 American Control Conference (ACC)

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This work is part of an ongoing effort to understand the dynamics of passenger loads in modern, multimodal transportation networks (TNs) and to mitigate the impact of perturbations, under the restrictions that the precise number of passengers in some point of the TN that intend to reach a certain destination (i.e. their distribution over different trip profiles) is unknown. We introduce an approach based on a stochastic hybrid automaton model for a TN that allows to compute how such probabilistic load vectors are propagated through the TN, and develop a computation strategy for forecasting the network's load a certain time in the future.

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“…For example, Evans and Morrison (1997) incorporated disruption effects in economic models for public transport and Silkunas (2006) examined reimbursements by transit authorities to passengers for such cases. Some research investigated rescheduling of metro systems following a disruption: for example, a comparison of strategies and the various constraints on managing delays due to incidents in metro systems was provided by Schmocker et al (2005), while Valdes et al (2006) simulated metro operations under disruptions to evaluate different approaches for controlling such conditions. Finally, Tsuchiya et al (2006) studied passenger support following an incident and developed a decision support system (DSS) to inform passengers of alternative routes when part of a metro system went out of service and Scott et al (2006) presented a novel method for indentifying critical links of a transportation network.…”

Section: Introductionmentioning

confidence: 99%

The bus bridging problem in metro operations: conceptual framework, models and algorithms

2009

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Metro networks provide efficient transportation services to large numbers of travelers in urban areas around the World; any unexpected operational disruption can lead to rapid degradation of the provided level of service by a city's public transportation system. In such instances, quick and efficient substitution of services is necessary for accommodating metro passengers including the widely used practice of "bridging" metro stations using bus services. Despite its widespread application, bus bridging is largely done ad-hoc and not as part of an integrated optimization procedure. In this paper we propose a methodological framework for planning and designing an efficient bus bridging network. Furthermore, we offer a set of structured steps and optimization models and algorithms for handling bus bridging problems.

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Metro Service Delay Recovery: Comparison of Strategies and Constraints Across Systems

Cited by 33 publications

References 3 publications

Challenges and Innovative Solutions in Urban Rail Transit Network Operations and Management: China’s Guangzhou Metro Experience

Challenges and Innovative Solutions in Urban Rail Transit Network Operations and Management: China’s Guangzhou Metro Experience

Predicting traffic load in public transportation networks

The bus bridging problem in metro operations: conceptual framework, models and algorithms

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