Locating charging infrastructure for freight transport using multiday travel data

• Published in: Transport policy Vol. 152; pp. 21 - 28
• Main Authors: Fu, Jiali, Nåbo, Arne, Bhatti, Harrison John
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2024
• Subjects: Battery electric vehicle; Charging infrastructure; Freight transport; Freight transport; Battery electric vehicle; Charging infrastructure
• ISSN: 0967-070X; 1879-310X; 1879-310X
• DOI: 10.1016/j.tranpol.2024.04.007

Vehicle electrification has shown the potential to reduce environmental impacts and greenhouse gas emissions from the transport sector. As electric vehicles (EVs) become increasingly prominent, the efficient placement of charging infrastructure poses a complex challenge that demands careful consideration. This paper delves into the investigation of how travel and parking patterns, derived from empirical data on freight vehicles, influence the optimal distribution of charging infrastructure across the freight network. This paper presents a node-based approach to optimize the allocation of charging infrastructure tailored explicitly for freight transport. The study identifies optimal locations for operator-owned charging infrastructure by leveraging GPS-based data collected from a fleet of freight vehicles operating in the greater Gothenburg metropolitan area. This research aims to enhance our understanding of the charging infrastructure requirements inherent in the freight transport system and provide decision support to logistics companies contemplating the shift from conventional fossil fuel vehicles to electric freight vehicles. The proposed model holds the potential for seamless adaptation to diverse freight transport systems, offering valuable insights to expedite the transition toward fossil-free freight transport on a broader scale. •Complexity in charging infrastructure placement for freight transport.•Empirical data from a fleet of freight vehicles in Gothenburg metropolitan area.•Investigation of travel and parking patterns of freight vehicles.•Node-based optimization approach to allocate charging infrastructure.