Expanding a(n) (electric) bicycle-sharing system to a new city: Prediction of demand with spatial regression and random forests-Reference-Cited by-同舟云学术

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Expanding a(n) (electric) bicycle-sharing system to a new city: Prediction of demand with spatial regression and random forests

Published:2020-04 Issue: Volume:84 Page:102692
ISSN:0966-6923
Container-title:Journal of Transport Geography
language:en
Short-container-title:Journal of Transport Geography

Author:

Guidon Sergio,Reck Daniel J.,Axhausen Kay

Publisher

Elsevier BV

Subject

General Environmental Science,Transportation,Geography, Planning and Development

Reference22 articles.

1. Statistik der Bevölkerung und der Haushalte (STATPOP);Swiss Federal Statistical Office (BFS),2016

2. Statistik der Unternehmensstruktur (STATENT);Swiss Federal Statistical Office (BFS),2017

3. Statistisches Jahrbuch des Schweizerischen Städteverbandes;Schweizerische Städteverband (SSV),2019

4. Modeling bike availability in a bike-sharing system using machine learning;Ashqar,2017

5. Spatial modelling with euclidean distance fields and machine learning;Behrens;Eur. J. Soil Sci.,2018

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1. Understanding human mobility and trip demand through sparse trajectories of private e-bikes;Journal of Cleaner Production;2024-09

2. Enhancing Bicycle Trajectory Planning in Urban Environments through Complex Network Optimization;Journal of Urban Planning and Development;2024-09

3. Understanding the Competition and Cooperation between Dockless Bike-Sharing and Metro Systems in View of Mobility;Sustainability;2024-07-07

4. Predicting cycling flows in cities without cycling data;Revista Eletrônica de Iniciação Científica em Computação;2024-06-28

5. Time-dependent trip generation for bike sharing planning: A multi-task memory-augmented graph neural network;Information Fusion;2024-06

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