Citation map · Study

SZodixStudyAcademic Library

Shelves Open access

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

A gradient boosting method to improve travel time prediction

7 references·11 citing·46 second hop

FromToDepth

this work references cited by second hop

References

2008Automatic Time Series Forecasting: The forecast Package for R3,665 cited
2006Analysis of Financial Time Series2,378 cited
2014Short-term traffic forecasting: Where we are and where we’re going1,248 cited
2010Analysis of Financial Time Series1,054 cited
2013Dynamic data-driven local traffic state estimation and prediction152 cited
2009Bayesian committee of neural networks to predict travel times with confidence intervals150 cited
2009Memory properties and fractional integration in transportation time-series112 cited

Cited by

2017LSTM network: a deep learning approach for short‐term traffic forecast1,899 cited
2018A hybrid deep learning based traffic flow prediction method and its understanding806 cited
2018Applying a random forest method approach to model travel mode choice behavior519 cited
2018Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo446 cited
2018Road Traffic Forecasting: Recent Advances and New Challenges387 cited
2016Travel time prediction with LSTM neural network370 cited
2019How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds

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

313 cited

2020Examining the relationship between built environment and metro ridership at station-to-station level302 cited

2021Learning Dynamic and Hierarchical Traffic Spatiotemporal Features With Transformer302 cited

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

2021Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach257 cited