With the rapid development of e-commerce (EC) and shopping online, accurate and efficient forecasting of e-commerce sales (ECS) is very important for making strategies for purchasing and inventory of EC enterprises. Affected by many factors, ECS volume range varies greatly and has both linear and nonlinear characteristics. Three forecast models of ECS, autoregressive integrated moving average (ARIMA), nonlinear autoregressive neural network (NARNN), and ARIMA-NARNN, are used to verify the forecasting efficiency of the methods. Several time series of ECS from China’s Jingdong Corporation are selected as experimental data. The result shows that the ARIMA-NARNN model is more effective than ARIMA and NARNN models in forecasting ECS. The analysis found that the ARIMA-NARNN model combines the linear fitting of ARIMA and the nonlinear mapping of NARNN, so it shows better prediction performance than the ARIMA and NARNN methods.

According to previous research studies, automated quayside cranes (AQCs) and automated guided vehicles (AGVs) in automated container terminals have a high potential synergy. In this paper, a collaborative scheduling model for AQCs and AGVs is established and the capacity limitation of the transfer platform on AQCs is considered in the model. The minimum total energy consumption of automated quayside cranes (AQCs) and Automatic Guided Vehicles (AGVs) is taken as the objective function. A two‐stage taboo search algorithm is adopted to solve the problem of collaborative scheduling optimization. This algorithm integrates AQC scheduling and AGV scheduling. The optimal solution to the model is obtained by feedback from the two‐stage taboo search process. Finally, the Qingdao Port is taken as an example of a data experiment. Ten small size test cases are solved to evaluate the performance of the proposed optimization methods. The results show the applicability of the two‐stage taboo search algorithm since it can find near‐optimal solutions, precisely and accurately.

For the complicated operation process, many risk factors, and long cycle of urban logistics, it is difficult to manage the security of urban logistics and it enhances the risk. Therefore, to study a set of effective management mode for the safe operation of urban logistics and improve the risk prediction mechanism, is the primary research item of urban logistics security management. This paper summarizes the risk factors to public security in the process of urban logistics, including pick up, warehouse storage, transport, and the end distribution. Generalized regression neural network (GRNN) is combined with particle swarm optimization (PSO) to predict accidents, and the Apriori algorithm is used to analyze the combination of high-frequency risk factors. The results show that the method of combining GRNN with PSO is effective in accident prediction and has a powerful generalization ability. It can prevent the occurrence of unnecessary urban logistics public accidents, improve the ability of relevant departments to deal with emergency incidents, and minimize the impact of urban logistics accidents on social and public security.

Short‐term traffic volume forecasting is one of the most essential elements in Intelligent Transportation System (ITS) by providing prediction of traffic condition for traffic management and control applications. Among previous substantial forecasting approaches, K nearest neighbor (KNN) is a nonparametric and data‐driven method popular for conciseness, interpretability, and real‐time performance. However, in previous related researches, the limitations of Euclidean distance and forecasting with asymmetric loss have rarely been focused on. This research aims to fill up these gaps. This paper reconstructs Euclidean distance to overcome its limitation and proposes a KNN forecasting algorithm with asymmetric loss. Correspondingly, an asymmetric loss index, Imbalanced Mean Squared Error (IMSE), has also been proposed to test the effectiveness of newly designed algorithm. Moreover, the effect of Loess technique and suitable parameter value of dynamic KNN method have also been tested. In contrast to the traditional KNN algorithm, the proposed algorithm reduces the IMSE index by more than 10%, which shows its effectiveness when the cost of forecasting residual direction is notably different. This research expands the applicability of KNN method in short‐term traffic volume forecasting and provides an available approach to forecast with asymmetric loss.

Autonomous vehicular clouds, as the combination of cloud computing and conventional vehicular ad hoc networks, will provide abundant resources and services by sharing under-utilized resources of future high-end vehicles such as computing power, storage and internet connectivity. Autonomous vehicular clouds will have significant impact if widely implemented in the intelligent transportation system. However, security and privacy issues are still big challenges in autonomous vehicular clouds. In this paper, after analyzing the particularity of autonomous vehicular clouds, we implement a two-layered architecture, in which vehicles are self-organized without the help of roadside units. Then based on the architecture, we put forward an effective key management protocol to distribute a group key efficiently and also provide the authentication and confidentiality that lots of current secure schemes ignore. In addition, according to the different scenarios and security levels we categorize the way of message transmitting into three kinds. At last, with performance evaluations, the proposed protocol can perform more efficiently than other well-known available schemes.

At present, a lot of studies on automatic terminal scheduling are aimed at the shortest operating time. An effective way to reduce the operating time is to increase the amount of operating equipment. However, people often ignore the additional costs and energy consumption caused by increasing the amount of equipment. This paper comprehensively considers the two aspects of the equipment operation time and equipment quantity matching. With the minimum total energy consumption of the operating equipment as the objective function, a cooperative scheduling model of Automated Guided Vehicles (AGVs) and dual Automated Yard Cranes (AYCs) is established. In the modelling process, we also considered the interference problem between dual Automated Yard Cranes (AYCs). In order to solve this complex model, this paper designs an improved multilayer genetic algorithm. Finally, the calculation results from CPLEX and a multilayer genetic algorithm are compared, and the effectiveness of the model and algorithm is proved by experiments. In addition, at the same time, it is proved that it is necessary to consider the interference problem of dual Automated Yard Cranes (AYCs), and the optimal quantity matching scheme for the equipment and the optimal temporary storage location is given.