The transition from diesel-driven urban freight transport towards more electric urban freight transport turns out to be challenging in practice. A major concern for transport operators is how to find a reliable charging strategy for a larger electric vehicle fleet that provides flexibility based on different daily mission profiles within that fleet, while also minimizing costs. This contribution assesses the trade-off between a large battery pack and opportunity charging with regard to costs and operational constraints. Based on a case study with 39 electric freight vehicles that have been used by a parcel delivery company and a courier company in daily operations for over a year, various scenarios have been analyzed by means of a TCO analysis. Although a large battery allows for more flexibility in planning, opportunity charging can provide a feasible alternative, especially in the case of varying mission profiles. Additional personnel costs during opportunity charging can be avoided as much as possible by a well-integrated charging strategy, which can be realized by a reservation system that minimizes the risk of occupied charging stations and a dense network of charging stations.
MULTIFILE
The transition from diesel-driven urban freight transport towards more electric urban freight transport turns out to be challenging in practice. A major concern for transport operators is how to find a reliable charging strategy for a larger electric vehicle fleet that provides flexibility based on different daily mission profiles within that fleet, while also minimizing costs. This contribution assesses the trade-off between a large battery pack and opportunity charging with regard to costs and operational constraints. Based on a case study with 39 electric freight vehicles that have been used by a parcel delivery company and a courier company in daily operations for over a year, various scenarios have been analyzed by means of a TCO analysis. Although a large battery allows for more flexibility in planning, opportunity charging can provide a feasible alternative, especially in the case of varying mission profiles. Additional personnel costs during opportunity charging can be avoided as much as possible by a well-integrated charging strategy, which can be realized by a reservation system that minimizes the risk of occupied charging stations and a dense network of charging stations.
MULTIFILE
The aim of this research/project is to investigate and analyze the opportunities and challenges of implementing AI technologies in general and in the transport and logistics sectors. Also, the potential impacts of AI at sectoral, regional, and societal scales that can be identified and chan- neled, in the field of transport and logistics sectors, are investigated. Special attention will be given to the importance and significance of AI adoption in the development of sustainable transport and logistics activities using intelligent and autonomous transport and cleaner transport modalities. The emphasis here is therefore on the pursuit of ‘zero emissions’ in transport and logistics at the urban/city and regional levels.Another goal of this study is to examine a new path for follow-up research topics related to the economic and societal impacts of AI technology and the adoption of AI systems at organizational and sectoral levels.This report is based on an exploratory/descriptive analysis and focuses mainly on the examination of existing literature and (empirical) scientific research publica- tions, previous and ongoing AI initiatives and projects (use cases), policy documents, etc., especially in the fields of transport and logistics in the Netherlands. It presents and discusses many aspects of existing challenges and opportunities that face organizations, activities, and individuals when adopting AI technology and systems.
Despite increasing efforts regarding knowledge valorisation, a significant gap between knowledge development and policy practice remains. Urban Intelligence bridges this gap by bringing cutting edge knowledge to the table, developing new policy concepts and by promoting smart data use.The professorship of Urban Intelligence takes a multimodal and integrated approach by connecting knowledge of transport engineering, urban planning and urban design. Research output encompasses data-driven projects, such as ‘Multimodal Brabant’ and ‘Measurement Weeks Breda‘, which translate big data into knowledge for policy development.Furthermore, data analysis tool and data dashboards for cycling, such as ‘CyclePRINT’ have been developed. To enhance the integration of built environment and transportation, we developed the Bicycle-Oriented Development (BOD) concept. This is currently being integrated into an overarching development philosophy, ‘Multimodal Urban Development’, which integrates the optimisation of multimodal networks, location choices for new urban developments and the provision of shared mobility via mobility hubs.
