Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/323717 
Year of Publication: 
2024
Citation: 
[Journal:] Decision Sciences [ISSN:] 1540-5915 [Volume:] 56 [Issue:] 2 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2024 [Pages:] 193-212
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
Electrifying transit bus networks (TBNs) has recently become a challenging problem that many public transport operators around the world are facing. Due to the limited driving range of electric buses, electric TBNs are more sensitive to operational delays and uncertainties. Moreover, the impact on sustainability is most profound when the buses are powered by renewable energy resources, which are often subject to intermittency and uncertainty. In this work, we tackle the complicated problem of planning charging schedules amid these various sources of uncertainty. We develop a real‐time decision support system that uses real‐time data, predictions, and mathematical optimization to update the charging schedules and mitigate the impact of operational uncertainties. Our results show that the online strategy can maintain higher reliability and renewable energy utilization levels compared to other charging strategies. The study has been carried out in cooperation with the public transport operator in Rotterdam in the Netherlands to assist them in their TBN transition process.
Subjects: 
electric transit bus networks
real‐time decision support system
renewable energy
smart charging
sustainable public transport
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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