Since ordinary city road map has not covered the road restrictions information for the lorry, and lacks of hot spots labeling, they cannot satisfy massive batches and long-distance road transportation requirements of bulk commodity transporting. In order to address the issues of frequent transportation accidents and low logistics efficiency, and further improve the truck drivers’ travel experience, it is urgent to combine the type of goods transported with the type of truck as well as the driver’s route selection preference to study the building method of customized logistics map for bulk commodity transporting. With the widespread applications of mobile Internet and Internet of vehicles, spatio-temporal data generated by bulk commodity transporting is growing rapidly. It constitutes logistics big data with other logistics operational data, which provides a solid data foundation for logistics map building. This study first comprehensively reviews the state-of-the-art work about the issue of map building using trajectory data. Then, to tackle the limitations of existing digital map building methods in the field of bulk commodity transporting, a data-driven logistics map building framework is put forward using multi-source logistics data. The following researches are focused on: (1) multi-constraint logistics map construction based on users' prior knowledge; (2) dynamic spatio-temporal data driven logistics map incremental updating. Logistics map will become AI infrastructure for new generation of logistics technology fit for bulk commodity transportation. The research results of this study provide rich practical contents for the technical innovation of logistics map building, and offer new solutions to promote the cost reduction and efficiency improvement of logistics, which have important theoretical significance and application values.