普通的城市道路地图未能覆盖(超)重卡货车的道路禁限行信息, 缺少标注适用于大宗货运的热门停驻区域, 无法满足货运司机的大批量长距离公路运输需求. 为解决大宗货运交通事故频发、物流效率低下等问题, 进一步提升货运司机的出行体验感, 亟需结合运输货物类型、货车车型以及司机的线路选择偏好等因素, 研究适用于公路大宗货运的定制化物流地图构建方法. 随着移动互联网、车联网的普及, 大宗货运产生的时空数据迅猛增长, 与物流运营数据等一起构成物流大数据, 为构建物流地图提供了数据基础. 在梳理地图构建技术的基础上, 针对现有电子地图构建方法在大宗货运领域的局限性, 利用多源物流数据提出了一个数据驱动的物流地图构建框架, 主要研究内容包括: (1)基于用户先验知识的多约束物流地图构建; (2)动态时空数据驱动的物流地图增量更新. 物流地图将成为大宗货运发展新一代物流科技的AI基础设施. 研究成果为物流地图构建的技术创新提供了丰富的实践内容, 也为促进大宗物流降本增效提供了新的解决思路, 具有重要的理论意义和应用价值.
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.