Network–wide prediction of public transportation ridership using spatio–temporal link–level information-Reference-Cited by-同舟云学术

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Network–wide prediction of public transportation ridership using spatio–temporal link–level information

Published:2020-01 Issue: Volume:82 Page:102549
ISSN:0966-6923
Container-title:Journal of Transport Geography
language:en
Short-container-title:Journal of Transport Geography

Author:

Karnberger Stephan,Antoniou Constantinos

Publisher

Elsevier BV

Subject

General Environmental Science,Transportation,Geography, Planning and Development

Reference33 articles.

1. Gaussian process-based predictive modeling for bus ridership;Bhattacharya,2013

2. Bagging predictors;Breiman;Mach. Learn.,1996

3. Avoiding the crowds: understanding tube station congestion patterns from trip data;Ceapa,2012

4. Transport infrastructure and global competitiveness: balancing mobility and livability;Cervero,2009

5. Direct ridership model of bus rapid transit in Los Angeles County, California;Cervero,2010

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1. Public Transit for Special Events: Ridership Prediction and Train Scheduling;IEEE Transactions on Intelligent Transportation Systems;2024-08

2. Real-time bus arrival delays analysis using seemingly unrelated regression model;Transportation;2024-06-26

3. Nonlinear effects of built environment features on metro ridership: An integrated exploration with machine learning considering spatial heterogeneity;Sustainable Cities and Society;2023-08

4. Predicting bus ridership based on the weather conditions using deep learning algorithms;Transportation Research Interdisciplinary Perspectives;2023-05

5. Developing a multiview spatiotemporal model based on deep graph neural networks to predict the travel demand by bus;International Journal of Geographical Information Science;2023-04-26

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