On the public citation graph

Graph Analysis and Prediction-Based Timetable Rescheduling Framework to Reduce Metro Line Capacity Loss after Partial Disruption

0 signed · 52 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s13369-019-04189-8openalex-extracted
Transfer Coordination-Based Train Organization for Small-Size Metro Networks
inferredcites →https://doi.org/10.1016/j.apm.2015.06.031openalex-extracted
Rescheduling subway trains by a discrete event model considering service balance performance
inferredcites →https://doi.org/10.1016/j.cie.2012.08.004openalex-extracted
Modelling disruptions and resolving conflicts optimally in a railway schedule
inferredcites →https://doi.org/10.1016/j.cie.2019.02.035openalex-extracted
A parallel multi-objective genetic algorithm with learning based mutation for railway scheduling
inferredcites →https://doi.org/10.1016/j.cor.2022.106075openalex-extracted
Accelerating logic-based Benders decomposition for railway rescheduling by exploiting similarities in delays
inferredcites →https://doi.org/10.1016/j.ejor.2013.12.020openalex-extracted
Adjusting a railway timetable in case of partial or complete blockades
inferredcites →https://doi.org/10.1016/j.eswa.2008.07.069openalex-extracted
Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions
inferredcites →https://doi.org/10.1016/j.jrtpm.2017.12.003openalex-extracted
Differences and similarities in European railway disruption management practices
inferredcites →https://doi.org/10.1016/j.ress.2021.108183openalex-extracted
Quantitative analysis for resilience-based urban rail systems: A hybrid knowledge-based and data-driven approach
inferredcites →https://doi.org/10.1016/j.trb.2011.05.001openalex-extracted
Robust single-track train dispatching model under a dynamic and stochastic environment: A scenario-based rolling horizon solution approach
inferredcites →https://doi.org/10.1016/j.trb.2014.01.009openalex-extracted
An overview of recovery models and algorithms for real-time railway rescheduling
inferredcites →https://doi.org/10.1016/j.trb.2015.04.001openalex-extracted
Real-time high-speed train rescheduling in case of a complete blockage
inferredcites →https://doi.org/10.1016/j.trb.2016.01.004openalex-extracted
Reinforcement learning approach for train rescheduling on a single-track railway
inferredcites →https://doi.org/10.1016/j.trb.2016.08.011openalex-extracted
Rescheduling a metro line in an over-crowded situation after disruptions
inferredcites →https://doi.org/10.1016/j.trb.2019.02.015openalex-extracted
Railway timetable rescheduling with flexible stopping and flexible short-turning during disruptions
inferredcites →https://doi.org/10.1016/j.trb.2019.12.005openalex-extracted
Coupling time-indexed and big-M formulations for real-time train scheduling during metro service disruptions
inferredcites →https://doi.org/10.1016/j.trb.2020.08.005openalex-extracted
An actor-critic deep reinforcement learning approach for metro train scheduling with rolling stock circulation under stochastic demand
inferredcites →https://doi.org/10.1016/j.trb.2021.08.003openalex-extracted
Real-time integrated train rescheduling and rolling stock circulation planning for a metro line under disruptions
inferredcites →https://doi.org/10.1016/j.trb.2022.12.008openalex-extracted
Robust collaborative passenger flow control on a congested metro line: A joint optimization with train timetabling
inferredcites →https://doi.org/10.1016/j.trc.2015.04.004openalex-extracted
Analyzing passenger train arrival delays with support vector regression
inferredcites →https://doi.org/10.1016/j.trc.2018.02.006openalex-extracted
Macroscopic multiple-station short-turning model in case of complete railway blockages
inferredcites →https://doi.org/10.1016/j.trc.2018.05.019openalex-extracted
Prediction of arrival times of freight traffic on US railroads using support vector regression
inferredcites →https://doi.org/10.1016/j.trc.2018.11.001openalex-extracted
Metro disruption management: Optimal initiation time of substitute bus services under uncertain system recovery time
inferredcites →https://doi.org/10.1016/j.trc.2022.103679openalex-extracted
A literature review of Artificial Intelligence applications in railway systems
inferredcites →https://doi.org/10.1080/15472450.2013.801714openalex-extracted
Evaluating Disturbance Robustness of Railway Schedules
inferredcites →https://doi.org/10.1080/19439962.2019.1645773openalex-extracted
Train stop deployment planning in the case of complete blockage: An integer linear programing model
inferredcites →https://doi.org/10.1080/21680566.2020.1854896openalex-extracted
Simulation-optimization framework for train rescheduling in rapid rail transit
inferredcites →https://doi.org/10.1080/21680566.2023.2203847openalex-extracted
Collaborative optimization of rescue operation and timetable rescheduling under metro train failure
inferredcites →https://doi.org/10.1080/23249935.2018.1564799openalex-extracted
Maximum likelihood regression tree with two-variable splitting scheme for subway incident delay
inferredcites →https://doi.org/10.1080/23249935.2023.2204965openalex-extracted
A general metro timetable rescheduling approach for the minimisation of the capacity loss after random line disruption
inferredcites →https://doi.org/10.1109/bigdata.2018.8622214openalex-extracted
Deep Reinforcement Learning Approach for Train Rescheduling Utilizing Graph Theory
inferredcites →https://doi.org/10.1109/icirt.2016.7588734openalex-extracted
Railway disruption timetable: Short-turnings in case of complete blockage
inferredcites →https://doi.org/10.1109/itsc.2019.8917180openalex-extracted
A Deep Reinforcement Learning Approach to High-speed Train Timetable Rescheduling under Disturbances
inferredcites →https://doi.org/10.1109/itsc45102.2020.9294188openalex-extracted
Reinforcement Learning in Railway Timetable Rescheduling
inferredcites →https://doi.org/10.1109/tits.2013.2296655openalex-extracted
Energy-Efficient Train Operation in Urban Rail Transit Using Real-Time Traffic Information
inferredcites →https://doi.org/10.1109/tits.2021.3131637openalex-extracted
Artificial Intelligence in Railway Transport: Taxonomy, Regulations, and Applications
inferredcites →https://doi.org/10.1109/tits.2021.3137410openalex-extracted
Improving Synchronization in High-Speed Railway and Air Intermodality: Integrated Train Timetable Rescheduling and Passenger Flow Forecasting
inferredcites →https://doi.org/10.1109/tits.2022.3170950openalex-extracted
Robust Control for Dynamic Train Regulation in Fully Automatic Operation System Under Uncertain Wireless Transmissions
inferredcites →https://doi.org/10.1109/tits.2023.3253928openalex-extracted
A Multistage Decision Optimization Approach for Train Timetable Rescheduling Under Uncertain Disruptions in a High-Speed Railway Network
inferredcites →https://doi.org/10.1126/science.abl6944openalex-extracted
Zhengzhou subway flooding a warning for other major cities
inferredcites →https://doi.org/10.1155/2016/6820394openalex-extracted
The Analysis and Calculation Method of Urban Rail Transit Carrying Capacity Based on Express-Slow Mode
inferredcites →https://doi.org/10.1155/2018/5367295openalex-extracted
A Short Turning Strategy for Train Scheduling Optimization in an Urban Rail Transit Line: The Case of Beijing Subway Line 4
inferredcites →https://doi.org/10.1177/0361198119840339openalex-extracted
Predicting Near-Term Train Schedule Performance and Delay Using Bi-Level Random Forests
inferredcites →https://doi.org/10.1287/trsc.2020.0979openalex-extracted
Interactions and Equilibrium Between Rescheduling Train Traffic and Routing Passengers in Microscopic Delay Management: A Game Theoretical Study
inferredcites →https://doi.org/10.1371/journal.pone.0291497openalex-extracted
Deviation of peak hours for metro stations based on least square support vector machine
inferredcites →https://doi.org/10.17877/de290r-14262openalex-extracted
Making Large-Scale SVM Learning Practical
inferredcites →https://doi.org/10.2219/rtriqr.50.8openalex-extracted
A Train Stop Deployment Planning Algorithm Using a Petri-net-based Modelling Approach
inferredcites →https://doi.org/10.3141/2353-03openalex-extracted
Disruption Recovery in Passenger Railways
inferredcites →https://doi.org/10.3389/fams.2023.1128181openalex-extracted
A machine learning framework for neighbor generation in metaheuristic search
inferredcites →https://doi.org/10.3390/su11082351openalex-extracted
Dispatching High-Speed Rail Trains via Utilizing the Reverse Direction Track: Adaptive Rescheduling Strategies and Application
inferredcites →https://doi.org/10.3390/systems11020059openalex-extracted
A New Decision Method of Flexible Job Shop Rescheduling Based on WOA-SVM
inferredcites →https://doi.org/10.4230/lipics.cp.2026.61openalex-extracted
A GNN-Guided Predict-and-Search Framework for Mixed-Integer Linear Programming
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.