Green corridors are emerging as a practical mechanism to promote alternative fuels. This paper develops an evolutionary game model to better understand the interactions between shipping lines and cargo owners within a green corridor. A population of 15,000 TEU container ships traveling between Australia and Singapore is simulated to study the economic feasibility of three alternative fuels: liquefied natural gas (LNG), methanol, and ammonia. The results indicate that LNG and methanol have a competitive advantage, while ammonia appears less economically attractive under the assumptions and parameter settings adopted in this study, primarily because of its higher associated costs. Sensitivity analysis reveals cargo owners’ willingness to pay (WTP) for alternative fuels, port discounts, and carbon tax significantly influence the fuel transition. It confirms the shipping industry is likely to be particularly sensitive to the cargo owners’ preference for alternative fuels. The study also highlights the importance of coordination among key stakeholders.

Real-time spatiotemporal forecasting, particularly in traffic systems, requires balancing computational cost and predictive accuracy-a challenge that conventional methods struggle to address effectively. In this work, we propose a non-trade-off framework called Spatial-Temporal Selective State Space (ST-Mamba), which leverages two key components to achieve both efficiency and accuracy concurrently. The Spatial-Temporal Mixer (ST-Mixer) dynamically fuses spatial and temporal features to capture complex dependencies, and the STF-Mamba layer incorporates Mamba’s selective state-space formulation to capture long-range dynamics efficiently. Beyond empirical improvements, we address a critical gap in the literature by presenting a theoretical analysis of ST-Mamba’s expressive power. Specifically, we establish its ability to approximate a broad class of Transformer and formally demonstrate its equivalence to at least two consecutive attention layers within the same framework. This result highlights ST-Mamba’s capacity to capture long-range dependencies while reducing computational overhead efficiently, reinforcing its theoretical and practical advantages over conventional transformer-based models. Through extensive evaluations of real-world traffic datasets, ST-Mamba demonstrates a 61.11% reduction in runtime alongside a 0.67% improvement in predictive performance compared to leading approaches, underscoring its potential to set a new benchmark for real-time spatiotemporal forecasting.

Because they have become an integral part of our daily lives, large quantities of hazardous materials are produced and transported each year. In most industrial societies, life without hazardous materials has become almost unimaginable. Hazardous materials are defined as substances that, during transportation, have the potential to pose adverse effects or risks to public health, safety, or property due to their quantity or form. In this context, hazardous materials or products include explosives, gases, flammable and oxidizing substances, toxic and infectious materials, as well as radioactive, corrosive substances and their associated hazardous wastes. The safe transportation of hazardous materials is considered a comprehensive and multidimensional issue, influenced by various legal and physical factors, as well as the numerous risks that vehicles may encounter during transit. Increasing environmental awareness of the potential impacts of hazardous material accidents on public health has significantly heightened both academic and institutional interest in this field. This study proposes a risk-averse solution to the hazardous material transportation problem through a model developed by integrating the Tabu Search algorithm with a game theory–based approach. Within the model, the dispatcher aims to minimize the expected loss under the worst possible conditions in the event of a disruption in any link of the distribution network. In this framework, the expected cost determined through Nash equilibrium is evaluated as an effective and practical analytical tool for strategic decision-making in the selection of safe routes for hazardous material transportation.

This study investigates the integration of battery-powered Autonomous Mobile Lockers (AMLs) and couriers within a two-echelon parcel pickup and delivery system to streamline parcel transfer between depots and field-operating couriers. Drawing upon the framework of the two-echelon closed vehicle routing problem (VRP), this study introduces two variants to the VRP model, namely open routes and multiple trips. To compare the effectiveness of these extensions, four novel mixed-integer linear programming models are formulated. The numerical experiments reveal that the open VRP models outperform the closed VRP models, achieving cost savings of 1% to 10%. Moreover, multi-trip VRP models demonstrate even greater savings compared to single-trip VRP models, achieving cost reductions of 30% to 52%. Finally, the multi-trip open VRP model achieves savings of 35% to 52% compared to the single-trip closed VRP model.

Accurate, efficient, and rapid traffic forecasting is essential for intelligent transportation systems and plays a pivotal role in urban traffic planning, management, and control. While existing spatiotemporal transformer models have demonstrated effectiveness in traffic flow prediction, they face notable challenges in achieving a balance between computational efficiency and accuracy. Additionally, they often prioritize global trends over local time series information and treat spatial and temporal data separately, limiting their ability to capture complex spatiotemporal interactions. To overcome these limitations, we propose the criss-crossed dual-stream enhanced rectified transformer (CCDSReFormer). This model introduces a novel rectified linear self-attention (ReLSA) mechanism combined with enhanced convolution (EnCov) to reduce computational overhead and sharpen the local feature focus. Furthermore, our cross-learning strategy seamlessly integrates spatial and temporal data, improving the model's ability to capture intricate traffic dynamics. Extensive experiments on six real-world datasets show that CCDSReFormer outperforms existing models in both accuracy and efficiency. An ablation study further validates the contributions of each component, confirming the model's superior ability to forecast traffic flow accurately and efficiently.

As the world accelerates its renewable energy transition, fossil fuel ports risk becoming stranded assets if diversification strategies are not employed. This paper proposes a system dynamics model to investigate the potential for biofuel production in the Port of Newcastle, Australia, the world's largest coal port. We integrate data on biowaste supply from the port's hinterland and evaluate the concurrent demand for biofuels on local and national levels. Our model provides a dynamic and adaptable framework for assessing the feasibility of diversification within a port system. Our findings show that the Port of Newcastle, with its strategic location, infrastructure, and access to regional biowaste and biomass resources in the hinterland, has the potential to become a circular economy hub. A set of policy recommendations to guide regional stakeholders and policymakers towards a more sustainable port ecosystem is proposed.

Concrete demolition waste represents a critical bottleneck in achieving a circular economy for the construction sector. This study develops a system-dynamics model that couples material flows with economic and logistical feedback to quantify how cost structures affect concrete recycling in the Sydney (Australia) metropolitan area. The model is calibrated with (i) official New South Wales 2020–2021 construction-and-demolition waste statistics, (ii) concrete consumption data scaled from state infrastructure reports, and (iii) parameters elicited from structured interviews with recycling contractors and plant operators. Scenario analysis systematically varies recycling-plant fees, landfill levies, and transport costs to trace their nonlinear impacts on three core performance metrics: recycling rate, cumulative landfill mass, and virgin gravel extraction. Results reveal distinct cost tipping points: a 10% rise in landfill-logistics costs or a 25% drop in recycling logistics costs shifts more than 95% of concrete waste into the recycling stream, cutting landfill volumes by up to 47% and reducing virgin aggregate demand by 5%. Conversely, easing landfill costs by 25% reverses these gains, driving landfill dependency above 99% and increasing gravel extraction by 39%. These findings demonstrate that carefully calibrated economic levers can override logistical inefficiencies and accelerate circular construction outcomes. The system-dynamics framework offers policymakers and industry stakeholders a decision-support tool for setting landfill levies, recycling subsidies, and infrastructure investments that jointly minimize waste and conserve natural resources.

Accurate traffic flow prediction is essential for optimizing traffic management, enhancing road safety, and reducing environmental impacts. However, traditional models face challenges with long sequence data with multi-sensors, demanding substantial memory and computational resources, resulting in slow inference times due to the lack of a unified summary state. This paper introduces ST-MambaSync, a novel unified traffic prediction model that uniquely integrates Mamba and Transformer technologies with balanced computational cost and higher accuracy. Our contributions include: (1) We present the first theoretical investigation demonstrating that Mamba functions as a specialized form of attention mechanism with ResNet structure, enhancing its ability to capture local features critical for traffic pattern recognition; (2) We provide the first theoretical proof of the complementary nature between Mamba and Transformer technologies, showing how their respective strengths in local and global feature extraction work synergistically to improve overall performance; and (3) we validate these benefits through extensive experiments with reducing MAE by 0.70%, RMSE by 0.62%, and MAPE by 0.31%. Also, a 64.86% reduction in inference time and a 19.44% reduction in training time compared to the formal state-of-the-art (SOTA) model, establishing new benchmarks for accuracy and processing speed. This breakthrough sets new standards in traffic flow prediction and has significant implications for urban planning and real-time traffic management.

Centralized multimodal transport systems face significant challenges due to data isolation, missing values, and heterogeneous spatial-temporal features, which hinder accurate prediction in traffic flow and travel demand. To address these challenges, we propose Spatial-Temporal Large Language Model with Denoising Diffusion Implicit (STLLM-DF), an innovative which integrates a Spatial-Temporal Denoising Diffusion Implicit Model (ST-DDIM) with a Spatial-Temporal Large Language Model (ST-LLM) to improve the predictions in traffic flow and travel demand in multimodal transport systems. The ST-DDIM effectively learns data distributions to recover noisy and incomplete data, while the ST-LLM captures complex spatial-temporal dependencies across multimodal networks, eliminating manual feature engineering. Extensive experiments conducted on ten real-world datasets from Sydney demonstrate that STLLM-DF consistently outperforms baseline models in both single-task and multi-task predictions (e.g., ), while consistently excelling in short-term and long-term predictions. On average, STLLM-DF achieves improvements in Mean Absolute Error (MAE) by 2.40%, Root Mean Square Error (RMSE) by 4.50%, and Mean Absolute Percentage Error (MAPE) by 1.51%. Furthermore, we evaluate the noise tolerance of STLLM-DF, demonstrating its robust performance under data imperfections. This paper presents a scalable, data-driven solution for managing multimodal transport systems, offering actionable insights for transport regulators.

The global ship recycling industry, dominated by South Asia, faces increasing scrutiny over environmental and safety practices. Simultaneously, a growing move away from coal in the run-up to 2050 will threaten coal ports, challenging them to diversify. This study explores the potential of embedding ship recycling into the transport geography of coal ports that are diversifying to circular economy hubs. We examine a case study of the Port of Newcastle in Australia, currently the ‘world’s largest coal port’. Using a System Dynamics (SD) model, the study evaluates key factors including global demand, operational costs, carbon credits, and recycled material revenues across four scenarios. The base scenario shows the PON facility is unviable at a break-even price of $540 per LDT compared to South Asia’s $780 per LDT. However, profitability improves when key factors such as carbon credits and recycled steel prices are optimised, highlighting the potential for economic viability with targeted policy and market reforms. Further Monte Carlo analysis reveals a wide range of potential profit outcomes, from $-1,284,205 to $1,077,655, reflecting the financial uncertainties and risks associated with the facility. Overall, the study recommends reforms to stabilise the Australian Carbon Credit Unit (ACCU) market, develop a green steel industry, and promote sustainable ship design. With these measures, the proposed ship recycling facility at the PON would be profitable, economically sustainable, and well-positioned to compete globally, paving the way for a shift in the geography of ship recycling from unsustainable facilities in South Asia to developed countries with higher environmental and safety standards. This transition would support Australia’s circular economy aspirations, foster global best practices in sustainable ship recycling, and contribute to achieving its environmental goals.

The electric vehicle, as a green and sustainable technology, has gained tremendous development and application recently in the logistics distribution system. However, the increasing workload and limited infrastructure capacity pose challenges for electric vehicles in the pickup and delivery operating system, including task allocation, electric vehicle routing, and queue scheduling. To address these issues, this paper introduces a pickup and delivery problem with electric vehicles and time windows considering queues, which considers queue scheduling for multiple electric vehicles when operating at the same site. A novel mixed integer linear programming model is proposed to minimize the cost of travel distance and queue time. An adaptive hybrid neighborhood search algorithm is developed to solve the moderately large-scale problem. Experimental results demonstrate the effectiveness of the model and adaptive hybrid neighborhood search algorithm. The competitive performance of the developed algorithm further confirmed by finding 9 new best solutions for the pickup and delivery problem with electric vehicles and time windows benchmark instances. Moreover, the results and sensitivity analysis of objective weight costs highlight the impact and importance of considering queues in the studied problem and obtain some management insights.

Autonomous vehicle navigation in structured environments requires planners capable of generating time-optimal, collision-free trajectories that satisfy dynamic and kinematic constraints. We introduce V*, a graph-based motion planner that represents speed and direction as explicit state variables within a discretised space-time-velocity lattice. Unlike traditional methods that decouple spatial search from dynamic feasibility or rely on post-hoc smoothing, V* integrates both motion dimensions directly into graph construction through dynamic graph generation during search expansion. To manage the complexity of high-dimensional search, we employ a hexagonal discretisation strategy and provide formal mathematical proofs establishing optimal waypoint spacing and minimal node redundancy under constrained heading transitions for velocity-aware motion planning. We develop a mathematical formulation for transient steering dynamics in the kinematic bicycle model, modelling steering angle convergence with exponential behaviour, and deriving the relationship for convergence rate parameters. This theoretical foundation, combined with geometric pruning strategies that eliminate expansions leading to infeasible steering configurations, enables V* to evaluate dynamically admissible manoeuvres, ensuring each trajectory is physically realisable without further refinement. We further demonstrate V*'s performance in simulation studies with cluttered and dynamic environments involving moving obstacles, showing its ability to avoid conflicts, yield proactively, and generate safe, efficient trajectories with temporal reasoning capabilities for waiting behaviours and dynamic coordination.

Amidst growing climate change concerns and decarbonisation imperatives, coal ports worldwide face the challenge of diversifying their operations in response to shifting energy demands. This paper investigates the historical transition of coal ports and their diversification strategies within the context of the circular economy. Employing a systematic review of academic literature and trade publications, we analyse the issues that pose long-term threats to coal ports. We focus on nine coal ports and terminals across the UK, US, China, and Russia, evaluating the diversification strategies employed at each port, and assessing their relevance in today's climate landscape. Notably, the Port of Newcastle, Australia, the world's largest coal port, serves as a key case study, revealing the potential for future-proofing operations through a circular economy framework. We discuss the driving factors and opportunities for this transition. Additionally, we address barriers to adoption, such as leadership challenges, financial viability, and infrastructure suitability, shedding light on the transformative journey coal ports must undertake in response to evolving energy paradigms.

Accurate and efficient traffic prediction is crucial for planning, management, and control of intelligent transportation systems. Most state-of-the-art methods for traffic prediction effectively predict both long-term and short-term by employing spatio-temporal neural networks as prediction models, together with transformers to learn global information on prediction objects (e.g., traffic states of road segments). However, these methods often have a high computational cost to obtain good performance. This paper introduces an innovative approach to traffic flow prediction, the Spatial-Temporal Selective State Space Model (ST-SSMs), featuring the novel ST-Mamba block, which can achieve good prediction accuracy with less computational cost. A comparative analysis highlights the ST-Mamba layer's efficiency, revealing its equivalence to three attention layers, yet with markedly reduced processing time. Through rigorous testing on diverse real-world datasets, the ST-SSMs model demonstrates exceptional improvements in prediction accuracy and computational simplicity, setting new benchmarks in the domain of traffic flow forecasting

The urban transportation network is crucial for societal development, but it is prone to failures like congestion caused by accidents or disasters. In particular, often network-wide failure is the result of a series of cascading failures originating from a small set of individual links. To prevent such failures, it is essential to identify these critical links and take early action. However, most existing approaches in the literature for evaluating the importance of each link rely on manually designed metrics (e.g., the Network Robustness Index). These methods are time-consuming and not suitable for large-scale urban networks. Additionally, these metrics fail to accurately capture the dynamic traffic interactions influenced by vehicle movement. In this paper, we present a novel method for identifying critical links by learning effective traffic interaction representation (the spatio-temporal dependencies) among roads. By representing the network as an un-directed graph and abstracting the road links as the nodes, we introduce a temporal graph attention model to capture spatial and temporal dependence between nodes. This model combines a graph attention network and a long short-term memory neural network and produces an attention matrix, which represents traffic interactions among links. Furthermore, we propose a traffic influence propagation model to evaluate the influence of each link for the entire road network based on the traffic interaction representation. We rank the importance of links based on their influence and then identify the critical links. A real-world case study in the city of Hangzhou, China is conducted to test our method and we use the network efficiency ratio to quantify its performance. The results suggest that our method can effectively identify the critical links at different periods.

The rapid advancement of Intelligent Transportation Systems (ITS) presents challenges, particularly with missing data in multi-modal transportation and the complexity of handling diverse sequential tasks within a centralized framework. To address these issues, we propose the Spatial-Temporal Large Language Model Diffusion (STLLM-DF), an innovative model that leverages Denoising Diffusion Probabilistic Models (DDPMs) and Large Language Models (LLMs) to improve multi-task transportation prediction. The DDPM's robust denoising capabilities enable it to recover underlying data patterns from noisy inputs, making it particularly effective in complex transportation systems. Meanwhile, the non-pretrained LLM dynamically adapts to spatial-temporal relationships within multi-modal networks, allowing the system to efficiently manage diverse transportation tasks in both long-term and short-term predictions. Extensive experiments demonstrate that STLLM-DF consistently outperforms existing models, achieving an average reduction of 2.40% in MAE, 4.50% in RMSE, and 1.51% in MAPE. This model significantly advances centralized ITS by enhancing predictive accuracy, robustness, and overall system performance across multiple tasks, thus paving the way for more effective spatio-temporal traffic forecasting through the integration of frozen transformer language models and diffusion techniques.

This paper proposes an analytical framework for an on-demand meal delivery market that features order bundling and courier sharing among restaurants. The proposed model consists of (i) a physical model describing the delivery process with order bundling, and (ii) an aggregated market equilibrium model characterizing the demand and supply interactions, which enables us to generate managerial insights without knowing the operational details. We analyze the impacts of demand and supply levels as well as the platform’s pricing and wage strategies on the system equilibrium. We outline a regime, which effectively defines a “healthy” state, optimizing the system’s service capacity to its fullest extent. The results demonstrate that a higher maximum bundle size enables a higher throughput capacity, resulting in improved energy efficiency for the system, lower service prices for customers, increased service opportunities for couriers, and higher turnover for restaurants. In comparison to the single-order delivery mode, the bundling delivery mode mitigates a substantial surge in prices during peak service hours. However, an excessive maximum bundle size is not advised for the platform oriented either toward service quality or profit due to the increasing Click-to-Door time and reduced marginal profit.