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ArticleA gradient boosting method to improve travel time prediction

Yanru Zhang2015

Article2015

A gradient boosting method to improve travel time prediction

Yanru Zhang · Ali E. Haghani

874 citations·0/0 files stored·Transportation Research Part C Emerging Technologies

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Traffic Prediction and Management Techniques Transportation Planning and Optimization Traffic control and management

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References

2008Automatic Time Series Forecasting: The forecast Package for R 2006Analysis of Financial Time Series 2014Short-term traffic forecasting: Where we are and where we’re going 2010Analysis of Financial Time Series 2013Dynamic data-driven local traffic state estimation and prediction 2009Bayesian committee of neural networks to predict travel times with confidence intervals 2009Memory properties and fractional integration in transportation time-series

Cited by

2017LSTM network: a deep learning approach for short‐term traffic forecast 2018A hybrid deep learning based traffic flow prediction method and its understanding 2018Applying a random forest method approach to model travel mode choice behavior 2018Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo 2018Road Traffic Forecasting: Recent Advances and New Challenges 2016Travel time prediction with LSTM neural network

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Details

Year2015

Typearticle

Published2015-04-05

VenueTransportation Research Part C Emerging Technologies

ISSN-L0968-090X

Languageen

Citations874

Files0 public / 0

ShelfTraffic Prediction and Management Techniques

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doi10.1016/j.trc.2015.02.019

openalex_workW2106100548

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2019

How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds

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