运力预测在大宗物流中发挥着关键作用, 对提高运力调度与车货匹配的精准性具有重要意义. 网约车运力预测目标为预测未来时段内可用车辆的数量; 而大宗物流的运力预测任务旨在预估未来时段内不同货运流向的空闲车辆信息 (如车辆ID(Identity Document)), 这与货车是否能在预计时间内返回钢厂 (称为运力可达性) 紧密相关. 以钢铁物流为例, 需要考虑由钢厂运输货物至客户企业以及从客户企业返回钢厂这两段行程耗时的影响. 由于长途运输过程中货车需要多次停留但停留时长不等, 停留时间的不确定使准确预测运输送达时间面临挑战; 此外, 网络货运平台仅对钢厂的货运任务进行运力指派, 货车返程货源则由司机自行联系确定, 导致返程轨迹缺失, 为预测货车返回钢厂的时间带来挑战. 为解决上述挑战, 基于物流企业的运单、车辆、轨迹以及运输终点等数据集, 提取货车的停留行为特征、运输终点特征、环境特征等, 并引入自注意力机制分别获取不同特征对两段行程耗时影响的权重, 进一步提升运力可达性预测的精度. 在此基础上, 提出了基于自注意力机制的运力预测方法, 包括基于历史流向相似性的运力候选集生成、基于自注意力机制的运力可达性预测、基于长短期记忆网络 (Long Short-Term Memory, LSTM) 模型的运力承运流向预测等3个部分. 最后, 在真实数据集上进行了大量对比实验. 实验结果表明, 所提方法具有更高的预测精度, 能为大宗物流的运力调度优化等任务提供强有力的决策支持.

Capacity prediction plays an important role in smart logistics, and its results are important for improving the accuracy of capacity scheduling and truck-cargo matching. Existing researches on capacity prediction in urban road networks aim to determine the number of available vehicles in future periods, while the problem of capacity prediction in bulk logistics aims at predicting the information on the trucks (e.g. the truck’s identity document (ID)) to carry certain types of goods for different flows, which is closely related to whether the trucks can return to the steel plant within the expected time (called capacity accessibility). In the case of bulk logistics, it is necessary to take into account the impact of the time spent on the two trips from the steel plant to the customer’s business and back to the steel plant. Since trucks need to stop several times in the long-distance transportation process but the length of stopping time varies, the uncertainty of stopping time makes the accurate prediction of transportation delivery time difficult. In addition, the freight platform only assigns capacity to one-way transport tasks (i.e. from the steel plant to the customer’s business), and the return trip (i.e. back to the steel plant) is determined by the truck drivers, which leads to the lack of return trajectory and poses a challenge to predict the return time of trucks to the steel plant. In order to solve the above challenges, based on the data sets of waybills, trucks, trajectories, and transport endpoints of logistics enterprises, we extract the stay behavior features, transport endpoint features, and environmental features. Then, the self-attention mechanism is introduced to obtain the weights of different features on the time consumption of two trips respectively to further improve the accuracy of capacity accessibility prediction. On this basis, a truck capacity prediction method based on self-attention mechanism is proposed, including capacity candidate set generation based on historical flow similarity, capacity accessibility prediction based on self-attention mechanism, and capacity carrier flow prediction based on long short-term memory (LSTM). Finally, the experimental results of comparison experiments on real logistics datasets show that the proposed method has higher prediction accuracy and can provide powerful decision support for the optimization of capacity scheduling in bulk logistics.