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Deep survival modelling for shared mobility

  • Published:2021-07 Issue: Volume:128 Page:103213
  • ISSN:0968-090X
  • Container-title:Transportation Research Part C: Emerging Technologies
  • language:en
  • Short-container-title:Transportation Research Part C: Emerging Technologies

Author:

Kostic BojanORCID,Loft Mathilde Pryds,Rodrigues FilipeORCID,Borysov Stanislav S.ORCID

Funder

European Commission

Horizon 2020

Horizon 2020 Framework Programme

Publisher

Elsevier BV

Subject

Computer Science Applications,Transportation,Automotive Engineering,Civil and Structural Engineering

Reference51 articles.

1. Tensorflow: Large-scale machine learning on heterogeneous systems;Abadi,2015

2. Dynamic linear models to predict bike availability in a bike sharing system;Almannaa;Int. J. Sustain. Transp.,2020

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

4. Modeling competing free-floating carsharing operators – a case study for zurich, switzerland;Balac;Transp. Res. C,2019

5. Modeling the impact of parking price policy on free-floating carsharing: Case study for zurich, switzerland;Balac;Transp. Res. C,2017

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1. Creating most needed customized bus services: A collaborative analysis of user-route dynamics;Transportation Research Part D: Transport and Environment;2024-08

2. Fake it till you make it: Synthetic data for emerging carsharing programs;Transportation Research Part D: Transport and Environment;2024-02

3. Digitale Verantwortung;Verbraucherinformatik;2024

4. The analysis of the shared bike usage pattern: Application of survival model to Taiwan YouBike;Asian Transport Studies;2024

5. Deep Bayesian survival analysis of rail useful lifetime;Engineering Structures;2023-11
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