Modeling the effect of real-time crowding information (RTCI) on passenger distribution in trains-Reference-Cited by-同舟云学术

Modeling the effect of real-time crowding information (RTCI) on passenger distribution in trains

Published:2022-12 Issue: Volume:166 Page:354-368
ISSN:0965-8564
Container-title:Transportation Research Part A: Policy and Practice
language:en
Short-container-title:Transportation Research Part A: Policy and Practice

Author:

Peftitsi Soumela,Jenelius Erik^ORCID,Cats Oded

Publisher

Elsevier BV

Subject

Management Science and Operations Research,Transportation,Civil and Structural Engineering,Business, Management and Accounting (miscellaneous),Aerospace Engineering

Reference50 articles.

1. Valuing crowding in public transport: Implications for cost-benefit analysis;Batarce;Transp. Res. A,2016

2. Examining the factors that impact public transport commuting satisfaction;Cantwell;J. Public Transp.,2009

3. A unified framework for vehicle rerouting and traffic light control to reduce traffic congestion;Cao;IEEE Trans. Intell. Transp. Syst.,2017

4. Multi-agent transit operations and assignment model;Cats;Procedia Comput. Sci.,2013

5. Modeling the impacts of public transport reliability and travel information on passengers’ waiting-time uncertainty;Cats;EURO J. Transp. Logist.,2017

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