Medium-term public transit route ridership forecasting: What, how and why? A case study in Lyon-Reference-Cited by-同舟云学术

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Medium-term public transit route ridership forecasting: What, how and why? A case study in Lyon

Published:2021-05 Issue: Volume:105 Page:124-133
ISSN:0967-070X
Container-title:Transport Policy
language:en
Short-container-title:Transport Policy

Author:

Egu Oscar,Bonnel Patrick

Publisher

Elsevier BV

Subject

Transportation,Geography, Planning and Development

Reference27 articles.

1. Prévision de la demande de transport;Bonnel,2002

2. Random forests;Breiman;Mach. Learn.,2001

3. Spherical k-means clustering;Buchta;J. Stat. Software,2012

4. A data-driven approach to prioritizing bus schedule revisions at New York city transit;Coleman;Transport. Res. Rec.,2018

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1. A Low-Rank Bayesian Temporal Matrix Factorization for the Transfer Time Prediction Between Metro and Bus Systems;IEEE Transactions on Intelligent Transportation Systems;2024-07

2. A comparison of time series methods for post-COVID transit ridership forecasting;Journal of Public Transportation;2024

3. Forecasting passenger flows and headway at train level for a public transport line: Focus on atypical situations;Transportation Research Part C: Emerging Technologies;2023-08

4. The potential of Wi-Fi data to estimate bus passenger mobility;Technological Forecasting and Social Change;2023-07

5. What’s eating public transit in the United States? Reasons for declining transit ridership in the 2010s;Transportation Research Part A: Policy and Practice;2022-03

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