References

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• 2014Short-term traffic forecasting: Where we are and where we’re going1,248 cited
• 2015A gradient boosting method to improve travel time prediction874 cited
• 2010Statistical methods versus neural networks in transportation research: Differences, similarities and some insights845 cited
• 2013Mining smart card data for transit riders’ travel patterns627 cited
• 2011Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks475 cited
• 2008Nature and/or nurture? Analyzing the determinants of transit ridership across US urbanized areas417 cited
• 2015A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system384 cited
• 2012Half-Mile Circle357 cited
• 2007Transit-Oriented Development's Ridership Bonus: A Product of Self-Selection and Public Policies326 cited
• 2013What influences Metro station ridership in China? Insights from Nanjing264 cited
• 2011Gradient boosting trees for auto insurance loss cost modeling and prediction247 cited
• 2008Neural network based temporal feature models for short-term railway passenger demand forecasting211 cited
• 2013Analysis of Metro ridership at station level and station-to-station level in Nanjing: an approach based on direct demand models