Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
Data-Enabled Stochastic Modeling for Evaluating Schedule Robustness of Railway Networks
by
Khadilkar, Harshad
in
Analysis
/ Case studies
/ Data
/ Delay
/ Distribution (Probability theory)
/ Evaluation
/ Infrastructure
/ Learning models (Stochastic processes)
/ Measures
/ Networks
/ Operations management
/ Punctuality
/ Rail transportation
/ rail transportation network
/ Railroads
/ Railway networks
/ Railways
/ Robustness
/ schedule robustness
/ Schedules
/ Scheduling algorithms
/ Side effects
/ stochastic delay propagation
/ Stochastic models
/ Timetables
/ Trains
/ Transportation networks
/ Transportation problem (Operations research)
2017
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Data-Enabled Stochastic Modeling for Evaluating Schedule Robustness of Railway Networks
by
Khadilkar, Harshad
in
Analysis
/ Case studies
/ Data
/ Delay
/ Distribution (Probability theory)
/ Evaluation
/ Infrastructure
/ Learning models (Stochastic processes)
/ Measures
/ Networks
/ Operations management
/ Punctuality
/ Rail transportation
/ rail transportation network
/ Railroads
/ Railway networks
/ Railways
/ Robustness
/ schedule robustness
/ Schedules
/ Scheduling algorithms
/ Side effects
/ stochastic delay propagation
/ Stochastic models
/ Timetables
/ Trains
/ Transportation networks
/ Transportation problem (Operations research)
2017
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Data-Enabled Stochastic Modeling for Evaluating Schedule Robustness of Railway Networks
by
Khadilkar, Harshad
in
Analysis
/ Case studies
/ Data
/ Delay
/ Distribution (Probability theory)
/ Evaluation
/ Infrastructure
/ Learning models (Stochastic processes)
/ Measures
/ Networks
/ Operations management
/ Punctuality
/ Rail transportation
/ rail transportation network
/ Railroads
/ Railway networks
/ Railways
/ Robustness
/ schedule robustness
/ Schedules
/ Scheduling algorithms
/ Side effects
/ stochastic delay propagation
/ Stochastic models
/ Timetables
/ Trains
/ Transportation networks
/ Transportation problem (Operations research)
2017
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Data-Enabled Stochastic Modeling for Evaluating Schedule Robustness of Railway Networks
Journal Article
Data-Enabled Stochastic Modeling for Evaluating Schedule Robustness of Railway Networks
2017
Request Book From Autostore
and Choose the Collection Method
Overview
This paper evaluates the robustness of a railway network with respect to operational delays. It assumes that trains in the network operate on fixed routes and with reference to a timetable. A stochastic delay propagation model is proposed for identifying primary (externally imposed) delays and for computing the resultant secondary (knock-on) delays. Delay probability distributions are computed for each train at each station on its journey, using timetable and infrastructure data for identifying potential station resource conflicts with other trains. The delay predictions are used to evaluate schedule robustness using two newly proposed metrics.
Individual robustness
measures the ability of trains to limit the adverse effects of their own primary delays. On the other hand,
collective robustness
measures the ability of the network as a whole, to limit the knock-on effects of primary delays imposed on a small fraction of trains. The two metrics provide stochastic guarantees on the punctuality of trains when the published schedule is put in operation. The applicability of the proposed methodology is validated using empirical data from a portion of the Indian Railways network, containing more than 38,000 train arrival/departure records. While a railway network is used as a case study, the same ideas can be applied to any scheduled transportation network.
Publisher
INFORMS,Transportation Science & Logistics Society of the Institute for Operations Research and Management Sciences,Institute for Operations Research and the Management Sciences
This website uses cookies to ensure you get the best experience on our website.