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Combining multiple transportation modes, intermodal transportation has been widely used in shipping hazardous materials (hazmat). But the relevant research on intermodal transportation for hazmat is still limited, especially when the planning environment contains possible system disruptions. This study develops a scenario-based robust optimization model for a rail-truck intermodal transportation network that ships regular and multiple hazmat freights with random disruptions at intermodal yards. To be specific, three operational level and one strategic level recovery mechanisms are proposed to maintain network connectivity during disruptions. Then, embedding various yard disruption scenarios with recovery plans, the expected risk and corresponding variability are minimized simultaneously, considering an additional augmented constraint to ensure the reliability in cost. Numerical experiments based on a real-world intermodal network of CSX, a leading rail-based freight transporter in North America, are conducted to find the optimal robust network structure and routing plan. A series of sensitivity analyses, in terms of recovery mechanisms and key parameter values, reveal relationships among the robustness and reliability of the intermodal transportation system. Further managerial insights can be used to assist intermodal carrier in seeking contingency plans for disruptions.

The optimization of the train frequency of the Airport express line (AEL) is crucial for improving the efficiency of air-rail intermodal transport. It directly influences passenger transfer convenience and overall service quality, thereby bolstering the competitiveness of the transport system This study focuses on the optimization of “AEL and Flight Succession” in the context of air-rail intermodal transport. By analyzing the departure and landing time of airport flights, we assess the demand from various passenger flows and identify key factors that impact the connection between the AEL and flights. Based on these factors, we develop a demand-driven optimization model for AEL frequency, aimed at minimizing total travel time and the number of unserved passengers. A simulated annealing algorithm is employed to solve this model. The Lanzhou-Zhongchuan AEL serves as a case study for validation. The results demonstrate that the optimized schedule reduces total passenger travel time costs by 0.93% and 3.82%, respectively, while accounting for passenger time sensitivity and fairness principles, with a difference of 2.89% between these scenarios. In addition, the optimization scheme decreases the number of unserved passengers by 14.7% and reduces the percentage of flights and trains failing to meet occupancy constraints by 17%. This study illustrates that the schedule optimization strategy not only effectively increases the number of served passengers but also significantly reduces total intermodal and commuter travel time. Such findings provide a solid scientific foundation for AEL operations and management to develop a more efficient and rational train schedule in the context of air-rail intermodal transport.

The problems of developing intelligent service provision systems face difficulties in the representation of dynamic aspects of cargo transportation processes and integration of different and heterogeneous ICT components to support the systems’ necessary functionality. This research aims to develop the architecture of the e-service provision system that can help in traffic management, coordination of works at trans-shipment terminals, and provide intellectual service support during intermodal transportation cycles. The objectives concern the secure application of the Internet of Things (IoT) technology and wireless sensor networks (WSNs) to monitor transport objects and context data recognition. The means for safety recognition of moving objects by integrating them with the infrastructure of IoT and WSNs are proposed. The architecture of the construction of the e-service provision system is proposed. The algorithms of identification, authentication, and safety connection of moving objects into an IoT platform are developed. The solution of application of blockchain mechanisms for the identification of stages of identification of moving objects is described by analysing ground transport. The methodology combines a multi-layered analysis of intermodal transportation with extensional mechanisms of identification of objects and methods of synchronization of interactions between various components. Adaptable e-service provision system architecture properties are validated during the experiments with NetSIM network modelling laboratory equipment and show their usability.

In order to integrate the use of transportation resources, develop a reasonable sea–rail intermodal container transportation plan, and achieve cost reduction and efficiency improvement of the multimodal transportation system, a method for predicting the daily freight volume of sea–rail intermodal transportation based on a convolutional neural network (CNN) algorithm is proposed and a new feature processing method is used: weight assignment (WA). Firstly, we use qualitative methods to preliminarily select the indicators, and then use multiple interpolation to fill in the missing raw data. Next, Pearson and Spearman quantitative analysis methods are used, and the analysis results are grouped using the k-means, with the high correlation groups assigned high weights. Next, we use quadratic interpolation to obtain the daily data. Finally, a weight assignment-enhanced convolutional neural network (WACNN) model and seven other mainstream models are constructed, using the Yingkou port container throughput prediction as a case study. The research results indicate that the WACNN prediction model has the best performance and strong robustness. The research results can provide a reference basis for the planning of sea–rail intermodal container transportation and the allocation of transportation resources, and achieve the overall efficiency improvement of logistics systems.

The incorporation of Artificial Intelligence (AI) models into various optimization systems is on the rise. However, addressing complex urban and environmental management challenges often demands deep expertise in domain science and informatics. This expertise is essential for deriving data and simulation-driven insights that support informed decision-making. In this context, we investigate the potential of leveraging the pre-trained Large Language Models (LLMs) to create knowledge representations for supporting operations research. By adopting ChatGPT-4 API as the reasoning core, we outline an applied workflow that encompasses natural language processing, Methontology-based prompt tuning, and Generative Pre-trained Transformer (GPT), to automate the construction of scenario-based ontologies using existing research articles and technical manuals of urban datasets and simulations. From these ontologies, knowledge graphs can be derived using widely adopted formats and protocols, guiding various tasks towards data-informed decision support. The performance of our methodology is evaluated through a comparative analysis that contrasts our AI-generated ontology with the widely recognized pizza ontology, commonly used in tutorials for popular ontology software. We conclude with a real-world case study on optimizing the complex system of multi-modal freight transportation. Our approach advances urban decision support systems by enhancing data and metadata modeling, improving data integration and simulation coupling, and guiding the development of decision support strategies and essential software components.

This paper addresses the multi-objective optimization for the road–rail intermodal routing problem that aims to minimize the total costs and carbon dioxide emissions of the routes. To achieve high timeliness of the entire transportation process, pickup and delivery services are simultaneously improved based on the employment of fuzzy soft time windows to measure their service levels. The modeling of road–rail intermodal routing considers fixed schedules of rail and time flexibility of road to match the real-world transportation scenario, in which travel times and carbon dioxide emission factors of road services are considered to be time-varying. To improve the feasibility of the routing, uncertainty of travel times and carbon dioxide emission factors of road services and capacities of rail services are incorporated into the problem. By applying trapezoidal fuzzy numbers to formulate the uncertainty, we propose a fuzzy multi-objective nonlinear optimization model for the routing problem that integrates the truck departure time planning for road services. After processing the model with fuzzy chance-constrained programming and linearization, we obtain an auxiliary equivalent crisp linear model and solve it by designing an interactive fuzzy programming approach with the Bounded Objective Function method. Based on an empirical case study, we demonstrate the validity of the proposed approach and discuss the effects of improving the confidence levels and service levels on the optimization results. The case analysis reveals several managerial insights that help to realize an efficient transportation organization by making effective trade-offs among lowering costs, reducing emissions, improving service levels, and enhancing feasibility.

It has been proven that exploring how to achieve an efficient transportation system is a crucial component of every sustainable transportation study. Rail-water intermodal transportation is recognized as one of the future transportation methods for being efficient, economical and environmentally friendly. To improve the efficiency, reduce transportation costs and maximize the resource utilization of outbound intermodal container transportation, based on the relationship between the container central station and the port station in the actual problems, the organization of railway container transportation was studied. A multi-objective optimization model was established in order to minimize the total cost in the process of transportation, which means maximizing the resource utilization and ensuring it is environmentally friendly. Additionally, an improved genetic algorithm (GA) was developed to solve the model. The calculation results of the model are obtained by the simulation calculation. The comparison with the conventional fixed axis transportation organization method proves that the model and algorithm can reduce costs by up to 24.57%. The result also shows that the container transport organization should be tried to satisfy the direct loading and discharging condition of “train-ship,” meanwhile reducing the storage time at the high toll central station. In conclusion, the model and algorithm are feasible and effective. Due to the universality of the model, it can be easily used and generalized in or out of China.

Existing research concentrates on analyzing the propagation and recovery of complex network risk or failure under a single model, which makes it difficult to effectively deal with the chain reaction. Concerning the recovery delay caused by the risk–failure interactions, this paper proposes a model for the propagation and recovery of risk–failure interactions. This model not only considers the network risk–failure interactive propagation mechanism but also introduces the load-balancing strategy and repair mechanism. The study quantifies the impact of the station on network resilience after different attack modes. In addition, the resilience metrics based on the station failure are established to accurately represent the resilience evolution of the network during propagation and recovery. Finally, focusing on the Belt and Road transportation network, we explore the evolution of network resilience under the variation of failure station repair time, station risk state recovery rate, and hub station allocation parameters. The simulation results showed that the model reduced the resilience loss through resilience recovery and accelerated the network back to normal in the face of attacks, shortening the station repair time and increasing the station risk recovery rate significantly improved the overall resilience level of the network, and increasing the proportion of hub station balancing based on the residual capacity effectively improved the minimum resilience of the sea–rail intermodal transportation network.

Nowadays, sustainable development is becoming a model for significant improvements in the modern world. Taking into consideration possible future challenges, transport must have sustainable features. Today, such a type of transportation as intermodal transport (transport that uses more than one mode of transportation) is gaining relevance. It is one of the most promising types of transport from an economic and environmental point of view. In this paper, the importance of sustainable development of intermodal freight transport is analyzed; the state of foreign trade and the trade balance in Ukraine and Poland are examined; and the sustainable development trends and prospects of trade relations between two countries are determined, taking into account the existing product structure. It is noted that the post-war recovery of Ukraine will require the activation of logistical flows with EU countries. The analysis of the state of freight transport by various modes of transport allowed for theoretically justifying the insufficient attention given to the development of intermodal freight transport between Ukraine and Poland for logistics optimization in international trade. From a methodological perspective, it is proposed to describe the subject area in terms of graph theory. The practical contribution involved the testing of the proposed model of intermodal freight transport to optimize logistics flows in international trade between Ukraine and Poland, considering the minimization of transport service costs and time. This required refining the mathematical apparatus based on the method of successive moves and linking their development to specific supply chains.

The construction of new international land and sea trade corridors (ILSTC) has improved transportation infrastructure along the routes and facilitated improved trade mechanisms. Based on data from 13 Chinese provinces and ASEAN from 2012-2021, this article applies an extended gravity model to explore the following questions: (1) Does the ASEAN countries' participation in the construction of the ILSTC promote their trade growth with China; (2) Whether the construction of the ILSTC improves the railroads, highways, and waterway transportation infrastructure of the 13 Chinese provinces facilitated trade growth with ASEAN countries? The study found that: first, ASEAN countries' participation in ILSTC construction significantly impacted their trade growth with China. Second, the ILSTC improves railroad and highway transportation facilities in 13 Chinese provinces and significantly contributes to their trade growth with ASEAN countries. However, its trade effect on waterway transport is not significant, primarily due to insufficient investment in the development of inland waterway infrastructure in 13 provinces of China and the limitations posed by natural conditions. Based on the findings, this study recommends strengthening policy coordination, advancing infrastructure and digital-intelligent development, promoting trade facilitation and liberalization, and fostering industrial integration. This provides a reference value for further participation in the ILSTC.