Yong Zhang

River–sea direct transport systems emerging along the Pinglu Canal connect coastal and inland port clusters through shared lock facilities. When berth and lock resources are planned independently, uneven port workloads, mismatched vessel flows, and avoidable waiting may arise. This study formulates a coordinated scheduling problem for a multi-port–lock network operating under direct transshipment. The mixed-integer programming model jointly determines port assignment, berth service sequences, lockage plans, and vessel placement within lock chambers, while balancing lock-operation expenditure against the port dwell time of seagoing and river vessels. To address large instances, a hierarchical iterative collaborative optimization algorithm (HICOA) is developed. Its iterative mechanism exchanges information between berth and lock decisions, and a packing heuristic and adaptive large neighborhood search are applied to the associated subproblems. Computational results show that HICOA recovers the optimum for small instances and produces feasible solutions efficiently when CPLEX is constrained by a 5-h time limit. Compared with independent scheduling, coordinated optimization reduces total port time by up to 55.69%. The sensitivity analysis further indicates that river-vessel arrival dispersion and berth distribution across ports materially influence system performance.

This study addresses the problems of vessel congestion and inefficient resource utilization caused by the independent scheduling of port berths and locks in river–sea intermodal transport. Moreover, this study explores the joint scheduling optimization problem for a single port and a single lock under the direct transshipment mode of seagoing and river vessels. First, considering the spatiotemporal coupling characteristics of the port–lock system, a mixed-integer linear programming model integrating seaside and riverside berth allocation, lock batch scheduling, and 2D layout constraints of the lock chambers is constructed. The objective is to minimize the time costs of seagoing vessels in a port, river vessels in a port, and river vessels passing through the locks. Second, a hierarchical iterative collaborative optimization algorithm is designed. The outer layer uses an iterative algorithm framework, while the inner layer utilizes a heuristic algorithm to address the lock scheduling problem. An adaptive large neighborhood search algorithm is used to address the berth scheduling problem. Finally, the model and algorithm are validated through numerical instances. Compared with the commercial solver CPLEX, the proposed algorithm HICOA achieves up to 81.45% lower costs in medium-to-large instances. Compared with independent scheduling, joint scheduling reduces the total vessel dwell time cost by up to 66.22%, demonstrating the significant value of port–lock collaboration. Furthermore, the arrival time window of river vessels and the ratio of the number of vessels to the number of berths have a significant influence on system performance.

With the increasing security of food supply chains, ensuring the optimal transportation of perishable goods has become crucial. Various options and modes of transportation offer significant advantages in terms of efficiency, cost-effectiveness and environmental sustainability. However, the uncertainty associated with network structure and unforeseen events would pose unique challenges for optimising the food cold chain container routing. This research aims to develop a multi-objective optimisation model for the container-based food cold chain transportation system. Considering the random failure of the network nodes scenario, a container with a cold chain multimodal routing optimisation model was constructed. The optimisation goal is to minimise transportation costs, carbon emission costs and total transportation time. The research adopted a mixed-integer optimisation model by linearisation technology. Gurobi was used to solve the problem. The model and solution were then verified by the empirical data. The findings uncover the new influence of the node failure on the multimodal route design, as well as different parameters on the cost of multimodal routes for better decision-making. The research contributes to the development of more resilient and efficient food cold chain transportation systems through the new conditions of five constraints in the proposed model, which is capable of adapting to uncertainties in network structure and ad-hoc situations.

Metro operation management relies on accurate predictions of passenger flow in the future. This study begins by integrating cross-city (including source and target city) knowledge and developing a short-term passenger flow prediction framework (METcross) for the metro. Firstly, we propose a basic framework for modeling cross-city metro passenger flow prediction from the perspectives of data fusion and transfer learning. Secondly, METcross framework is designed to use both static and dynamic covariates as inputs, including economy and weather, that help characterize station passenger flow features. This framework consists of two steps: pre-training on the source city and fine-tuning on the target city. During pre-training, data from the source city trains the feature extraction and passenger flow prediction models. Fine-tuning on the target city involves using the source city's trained model as the initial parameter and fusing the feature embeddings of both cities to obtain the passenger flow prediction results. Finally, we tested the basic prediction framework and METcross framework on the metro networks of Wuxi and Chongqing to experimentally analyze their efficacy. Results indicate that the METcross framework performs better than the basic framework and can reduce the Mean Absolute Error and Root Mean Squared Error by 22.35% and 26.18%, respectively, compared to single-city prediction models.

Preservice mathematics teachers’ accurate understanding of mathematical problem solving and its teaching is key to the performance of their professional quality. This study aims to investigate preservice mathematics teachers’ understanding of problem solving and its teaching and compares it with the understanding of in-service mathematics teachers. After surveying 326 in-service mathematics teachers, this study constructs a reliable and valid tool for the cognition of mathematical problem solving and its teaching and conducts a questionnaire survey on 26 preservice mathematics teachers. Survey results reveal that preservice mathematics teachers have a good understanding of mathematical problem solving and its teaching and are more confident in the transfer value of problem solving ability. By contrast, in-service teachers are more optimistic that problem solving requires exploration, continuous thinking, and the participation of metacognition. This article concludes that preservice mathematics teachers should focus more on the initiative and creativity of students and put students at the center of education. In addition, teacher educators should provide more teaching practice opportunities for preservice teachers. The findings also show that in-service teachers’ understanding of problem solving and its teaching is inferior to that of preservice teachers on some indicators, implying the importance of post-service training for in-service teachers.

A hybrid-clustering model is presented for the probabilistic characterization of ship traffic and anomaly detection. A hybrid clustering model was proposed to increase the efficiency of trajectory clustering in the port area and analyze the maritime traffic patterns in port. The model identified dissimilarities between trajectories based on characteristics, using K-Means and the density-based spatial clustering of applications with noise algorithm (DBSCAN). Firstly, the ship’s trajectory characteristics are constructed based on real ship trajectories considering static characteristics and dynamic characteristics of ship trajectories to calculate the characteristic dissimilarity between trajectories. Simultaneously, the spatial dissimilarity could be quantified using the Hausdorff algorithm. Then, the ship trajectory is clustered initially based on the departure and destination characteristics using K-Means algorithms to obtain various sub-trajectories. However, there are still different types of trajectories in each sub-trajectory. Thus, the DBSCAN algorithm is adopted to cluster the sub-trajectory based on the analysis of the different trajectory characteristics. Finally, the proposed model is applied to the characterization of the Zhanjiang Port, and the results show that the hybrid-clustering method can effectively cluster ship trajectory and present probabilistic characterization of ship traffic and anomaly detection. This lays a solid theoretical foundation for the supervision and risk control of intelligent ships.

The purpose of this study is to investigate whether spatial-temporal dependence models can improve the prediction performance of short-term freight volume forecasts in inland ports. To evaluate the effectiveness of spatial-temporal dependence forecasting, the basic time series forecasting models for use in our comparison were first built based on an autoregression integrated moving average model (ARIMA), a back-propagation neural network (BPNN), and support vector regression (SVR). Subsequently, combining a gradient boosting decision tree (GBDT) with SVR, an SVR-GBDT model for spatial-temporal dependence forecast was constructed. The SVR model was only used to build a spatial-temporal dependence forecasting model, which does not distinguish spatial and temporal information but instead takes them as data features. Taking inland ports in the Yangtze River as an example, the results indicated that the ports’ weekly freight volumes had a higher autocorrelation with the previous 1–3 weeks, and the Pearson correlation values of the ports’ weekly cargo volume were mainly located in the interval (0.2–0.5). In addition, the weekly freight volumes of the inland ports were higher depending on their past data, and the spatial-temporal dependence model improved the performance of the weekly freight volume forecasts for the inland river. This study may help to (1) reveal the significance of spatial correlation factors in ports’ short-term freight volume predictions, (2) develop prediction models for inland ports, and (3) improve the planning and operation of port entities.

COVID-19 first appeared in Wuhan city of Hubei Province in China in December 2019. It has a substantial impact on human life all around the world, especially for citizens. The threat of COVID-19 has resulted in people shopping online to get fresh food and reduce outdoor trips. Collecting data from adult internet users in Wuhan, China in 2020, this study aims to explore the influence of COVID-19 on fresh food shopping behavior. In addition, a comparison and ordered logit model are constructed to demonstrate the changes and effects of COVID-19. The results suggest that more citizens in Wuhan city will buy fresh food online and the cost and frequency are also increased. The experience of online shopping for fresh food during the lock-down days has promoted more online shopping. The factors, such as frequency of online shopping before the COVID-19 outbreak, frequency of online shopping during the COVID-19 pandemic, and age, have a negative effect on the proportion of online shopping after the lock-down days, while the proportion of online shopping before the COVID-19 outbreak, the proportion of online shopping during the COVID-19 pandemic, and travel time of in-store shopping before the COVID-19 outbreak have a positive effect. The results provide insights for managers, city planners, and policymakers.