Citation map · Study

SZodixStudyAcademic Library

Shelves Open access

Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated

Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China

12 references·5 citing·45 second hop

FromToDepth

this work references cited by second hop

References

1998Forecasting with artificial neural networks:4,282 cited
1998Time Series Forecasting with Neural Networks: A Comparative Study Using the Air Line Data419 cited
2009Some stylized facts about high-speed rail: A review of HSR experiences around the world407 cited
2008Neural network based temporal feature models for short-term railway passenger demand forecasting211 cited
2006Detecting long-range correlations of traffic time series with multifractal detrended fluctuation analysis186 cited
2012The retrieval of intra-day trend and its influence on traffic prediction184 cited
1998Improved grey prediction models for the trans‐pacific air passenger market120 cited
1995FORECASTING INTERNATIONAL AIRLINE PASSENGER TRAFFIC USING NEURAL NETWORKS80 cited
2014Forecasting demand for high speed rail66 cited
1997Inter-urban rail demand, elasticities and competition in Great Britain: Evidence from direct demand models49 cited
2012Methodological Options and Data Sources for the Development of Long-Distance Passenger Travel Demand Models: A Comprehensive Review40 cited
1985Understanding trends in inter‐city rail traffic in Great Britain32 cited

Cited by

2017Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach748 cited
2018

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets

299 cited

2017Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach259 cited

2017A novel passenger flow prediction model using deep learning methods255 cited

2017Using an ARIMA-GARCH Modeling Approach to Improve Subway Short-Term Ridership Forecasting Accounting for Dynamic Volatility155 cited