With the increasing trend of large-scale vessels, the shortage of deep-water berth resources in ports has become increasingly prominent, severely restricting vessel turnover rates and cargo unloading efficiency. To address this bottleneck, this study proposes an optimization approach for load reduction and berth shifting of large dry bulk carriers, coordinating the allocation of core resources such as berths, unloaders, and tugboats to enhance the overall operational efficiency of ports. A multi-objective, two-stage joint scheduling optimization model for berth, unloader, and tugboat operations is developed. The model decouples the problem into two stages: berth-unloader joint allocation and tugboat scheduling. It comprehensively considers operation time and costs to achieve full-process optimization of load reduction and berth shifting. Furthermore, a two-layer solution framework integrating the Starfish Optimization Algorithm (SFOA) with the Non-dominated Sorting Genetic Algorithm (NSGA-II) is proposed. The framework employs tabu search to enhance Pareto front exploration in berth-unloader allocation and utilizes an elite-based chromosome generation mechanism in tugboat scheduling to improve solution quality. Additionally, a multi-energy hybrid tugboat fleet comprising diesel and clean energy-powered vessels is designed, along with a vessel-tugboat matching strategy that factors in emission reduction requirements based on varying tugboat demands of different bulk carrier sizes. Finally, a case study based on a real port in northern China demonstrates the effectiveness of the proposed optimization scheme in alleviating deep-water berth shortages, reducing tugboat emissions, and promoting the intelligent and green transformation of port operations.

Motivated by the emerging dual-sourcing strategy with a blended workforce in on-demand ride service markets, this paper investigates the impact of this strategy on ride services and sizes a heterogeneous fleet operated by both contractual and freelance drivers. We present an integrated heterogeneous fleet management model that incorporates strategic fleet sizing and tactical platform operations, addressing demand uncertainty through an adaptive passenger-vehicle matching rule that outperforms conventional static methods. Employing a two-stage robust optimization framework, the second stage is reformulated into equivalent mathematical programming formulations, solvable using state-of-the-art commercial solvers. A column-and-constraint generation based exact algorithm is developed to solve the two-stage model by leveraging its structural properties. Extensive experiments based on a real-world ride-hailing service demonstrate significant benefits of the dual-sourcing strategy in enhancing service performance.

Maritime crude oil transportation system is highly susceptible to tense geopolitics, extreme weather and sharp market volatility, producing short-term shifts in resilience. Existing research pays limited attention to precise network modeling and to accurate measurement of its resilience. We model a directed global crude oil maritime transportation network (GCOMTN) from tanker AIS data and develop a framework that quantifies resilience with structural and functional metrics. We evaluate four disruption scenarios (minor, moderate, severe, complete) and assess quarterly dynamics. Partial disruptions are designed by empirically derived probabilities. Results show that inter-quarter differences in demand and routing materially shape resilience. Metrics drop sharply when the top 5% of ports are disrupted and then moderate, with clear quarterly contrasts driven largely by mid-ranked ports from 10% to 40%. Structural resilience shows greater fluctuation under partial disruptions (from 0.6 to 0.8 under minor disruptions). For chokepoint disruptions, high-demand quarters exhibit larger structural shifts, whereas low-demand quarters show greater variability in functional resilience. In strait-dependent subnetworks, even moderate disruptions can paralyze affected ports, with resilience falling to 0.03. The Straits of Hormuz and Malacca produce the largest resilience fluctuations. This study offers implications for optimizing the strategic decision-making safely in maritime crude oil transportation.

The rapid adoption of e-bicycles challenges traditional route choice insights, which typically assume that cyclists minimize physical effort and adhere to habitual paths. By partially decoupling travel distance from physical exertion, e-bicycles may relax efficiency-oriented constraints and enable greater flexibility in route choice. This study introduces the concept of Variety Seeking Route Choice Behavior (VSRCB) to quantify the extent to which cyclists deviate from habitual routes, and examines whether electric assistance enhances this flexibility. Using high-resolution GPS trajectory data, we develop a multi-dimensional framework to quantify route variety among Dutch cyclists. The results confirm that VSRCB is an observable phenomenon across both modes, though e-cyclists exhibit higher levels than traditional cyclists. A decomposition analysis indicates that this variety primarily arises from more frequent switching among available route alternatives (Quantity and Balance), rather than from substantial geometric deviation (Distinction). Moreover, VSRCB is most pronounced in highly urbanized areas, where dense road networks support flexible routing, while demographic characteristics show no significant association. These findings suggest that cycling infrastructure planning should move beyond single high-capacity corridors and instead emphasize redundant, mesh-like networks that better accommodate the flexible routing behavior of e-cyclists.

While advanced discrete choice models such as the mixed logit (MXL) and neural-embedded logit models capture taste heterogeneity, important limitations remain. MXL models rely on pre-specified parameter distributions, whereas neural-embedded models typically produce point estimates of taste parameters conditioned on demographics, which fail to flexibly represent random heterogeneity within demographic groups. To bridge this research gap, this study develops a robust Mixture Density Network-embedded Logit (MDN-Logit) model that extends the point estimate of taste parameters to full distributional representations. The model uses a mixture density network to learn flexible, data-driven mixture distributions of taste parameters conditioned on individual socio-demographic characteristics, thereby jointly capturing systematic and random heterogeneity within a unified framework. The MDN-Logit model is validated using synthetic data, the Swissmetro stated preference dataset and a revealed preference freight dataset. Results demonstrate that it successfully recovers complex parameter distributions along with accurate value of time and distance sensitivity estimates, while achieving superior goodness-of-fit and predictive performance over benchmark models such as MXL and TasteNet. Ultimately, its ability to compute policy-relevant indicators, including the value of time and elasticities, makes it a powerful tool for granular passenger and freight transportation planning.

Accurate demand forecasting is critical for strategic capacity planning and policy formulation in the rapidly evolving New Energy Vehicle (NEV) market. However, standard time-series models often struggle to capture the complex nonlinearities and high-frequency structural breaks resulting from China's unique regulatory schedules and cultural environment. To address this, this study proposes Prophet-SRC, a hybrid framework that integrates a decomposable trend model with a Multi-Head Attention-based seasonal residual correction mechanism. Empirical validation against eight state-of-the-art baselines demonstrates the decisive superiority of the proposed framework, which achieves a Mean Absolute Percentage Error (MAPE) of 4.25% and reduces the Mean Squared Error (MSE) by approximately 79.3% compared to top-ranked deep learning variants. Multi-horizon forecasts (2026-2030) reveal a structural dichotomy: while the passenger vehicle sector exhibits robust endogenous growth driven by private consumption, the commercial vehicle sector displays acute volatility linked to fiscal budgeting cycles, with a technological pathway overwhelmingly locked into Battery Electric Vehicles (BEVs). Based on these findings, we recommend manufacturers adopt dynamic capacity allocation strategies via counter-cyclical inventory buffering to mitigate year-end supply shortages. Furthermore, policymakers are advised to implement counter-cyclical procurement mechanisms to smooth the fiscal demand pulse and stabilize upstream supply chains.

Facing profound globalization, supply chain restructuring and green growth, port cities tend to shift from logistics hubs to coastal metropolises. During port-industry-city (PIC) integration, challenges such as spatial expansion conflicts, insufficient industrial upgrades, and pressure on ecologies exist. Exploring effective PIC integration pathway is essential for addressing these challenges and enhancing port city development. This study builds a multi-objective land use optimization model for port cities, aiming to maximize the output of port and shipping service industry (PSSI), enhance urban industrial agglomeration and improve residential quality of life (QOL). The model identifies the foundational urban conditions required for PSSI, quantifies their weights, and constructs production functions. It then optimizes the industrial configuration based on specified industrial policy and uses the Lowry model to determine housing locations. A case study of Ningbo (China), revealed that with rail-transit construction, PSSI output increases by 8.92%, industrial agglomeration by 3.03% and QOL by 8.73%. This study provides a rigorous quantitative framework to guide PSSI and urban land use as port cities transition toward coastal metropolises.

Restricting heavy commercial vehicles (HCV) stopping behavior is essential for traffic management and efficient logistics organization. However, insights into HCV stopping behavior conditional on infrastructure remain limited. We utilize large GPS HCV trajectory data to investigate the spatial variability of stopping duration and its associated influential factors. Results show HCV stopping duration and pre-stop trip distance exhibit marked weekday-weekend heterogeneity, with longer stops and longer preceding trips being more likely to occur on weekdays. We further propose a spatial random forest model to predict HCV stopping duration. In addition to the superiority of the proposed model, we found parking distribution and land use collectively contribute to 76.76% of the predictive power, with trip distance identified as the most influential factor, followed by distance to the nearest freight rail station and to the nearest port, respectively. Furthermore, the analyses also exhibit complex non-linear associations between influential factors and stopping duration, and significant threshold effects. These findings offer valuable insights that can serve as a nuanced guide for planning HCV parking management.

Accelerating maritime decarbonization requires Market-Based Measures (MBMs) to bridge the fossil-green cost gap. Prevailing uniform carbon pricing, however, creates an efficiency-equity dilemma: it offers aggregate cost-effectiveness yet disproportionately burdens developing economies. To enable differentiated treatment within a globally uniform framework, we propose the Port Carbon Intensity Index (PCII) mechanism. Utilizing ports as the nexus of global trade, the PCII links route-specific emissions to economic value-added. Using an integrated optimization framework parameterized by global Automatic Identification System (AIS) data from 2020 for crude oil tankers, we quantify trade-offs between uniform pricing and the PCII. Results show that the PCII mechanism robustly promotes regional equity by shifting compliance costs toward developed regions. Furthermore, the comparative cost-effectiveness of the two mechanisms is highly sensitive to ballast voyage allocation methodologies, and technological cost reductions alone cannot resolve long-term distributional imbalances. These findings provide important insights for designing equitable and effective maritime decarbonization policies.

Crude oil shipping is constrained by the significant geographical separation of production from consumption, necessitating extensive non-revenue-generating ballast voyages that contribute substantially to maritime emissions. However, existing decarbonization regulations treat all transport activities uniformly, overlooking the misalignment between carbon liabilities and economic value generation. To bridge this gap, this study identifies ports as the critical nexus and constructs an integrated framework by coupling Automatic Identification System (AIS) data with an Inter-Country Input-Output (ICIO) model to evaluate the carbon-economic intensity of 405 ports. Ballast voyage emissions are back-allocated to subsequent laden voyages, explicitly aligning carbon accounting with standard charter-party arrangements. Based on this approach, three indicators are proposed: the Port Carbon Intensity Index (PCII), Port Emission-Output Tradeoff (PEOT), and Port Demand Emission Multiplier (PDEM). Results indicate that: (i) highincome economies exhibit the highest median PCII, indicating a misalignment between economic scale and logistical carbon-economic performance; (ii) ballast voyages are a critical factor, accounting for 28-40% of port-associated emissions; and (iii) Middle Eastern hubs function as high-cost bottlenecks driven by East Asian demand, whereas high-leverage nodes like Singapore demonstrate higher mitigation efficiency. The framework facilitates the internalization of carbon costs into commercial contracting, providing actionable benchmarks for decision-making under common sales contracts and chartering agreements.

Understanding the relationship between the built environment and metro travel among older adults is essential for developing age-friendly urban transportation systems. While previous studies primarily focus on ridership, the effects of the built environment on activity duration remain largely unexplored. This study represents the first attempt to explore the effects of the built environment on activity duration among older adults across different metro travel patterns. Using metro smart card data and built environment data from Dalian, China, the study applies latent class analysis (LCA), gradient boosting decision trees (GBDT), and SHapley Additive ExPlanations (SHAP) to identify different metro travel patterns and uncover the underlying environmental mechanisms, including nonlinear and interactive effects. The results show pronounced heterogeneity in metro travel patterns among older adults, as reflected in their daily cycles, time of day, metro line, travel time, and travel cost. Further analysis refines eight metro travel patterns based on short- and long-duration activities, revealing that built environment variables contribute differently to activity duration across these patterns. Interestingly, variables like population density, the number of retail and supply facilities, and the number of medical and health facilities exhibit inverted U-shaped or threshold-type nonlinear effects. Furthermore, interactive effects among built environment variables are predominantly negative, reflecting spatial redundancy and marginal diminishing in high-density metro catchment areas. These findings prompt a reconsideration of the current urban planning paradigm of “multi-functional intensive development”, highlighting the need to balance “abundance” with “appropriateness” and offering theoretical support for more targeted age-friendly transport interventions.

Demand uncertainty in market environments presents substantial challenges in managing port logistics service supply chains. This paper investigates the impact of demand uncertainty on the interactions between entities within a port logistics supply chain, addressing three main issues: customer decision-making uncertainty, time constraints, and demand variability. To minimize the total cost of providing logistics solutions to customers, we propose a two-stage robust optimization model centered around a logistics integrator. In the first stage, the model selects appropriate logistics providers and shipping schedules based on fixed time constraints. The second stage adjusts logistics service arrangements according to fluctuating customer demand, utilizing a column-andconstraint generation algorithm to solve the model. The objective is to deliver optimal logistics solutions that minimize both time and cost, while meeting customer requirements. In the numerical experiments, customer decision-making uncertainty is represented by uncertain budget levels, while demand fluctuations are modeled through changes in coefficients that reflect the degree of variability in demand. The results indicate that by using uncertain budget levels to capture the impact of uncertainty on decision-making and employing polyhedral sets to represent demand uncertainty, the two-stage robust optimization method effectively addresses demand uncertainty in port logistics service supply chains. This study offers valuable insights for optimizing port logistics service supply chains.

This research addresses a river-land multi-modal bulk cargo transportation problem with containerization. It involves three transportation modes: inland waterway, railway, and road transportation. While heterogeneous vessels are commonly employed in inland waterway transportation, few studies have focused on the allocation of these vessels within the context of river-based multimodal transportation. Consequently, introducing decisions on container usage for bulk shipments, identifying containerization locations, and assigning heterogeneous ships to riverine channel in multimodal transportation presents significant challenges. An integer nonlinear programming model based on a directed graph, which incorporates constraints such as water depth, the availability of road and railway vehicles, and the capacity of containerization equipment throughout the planning horizon, is formulated and subsequently linearized. The objective is to minimize the total cost, including transportation, containerization, and cargo damage costs. A multiple ant colony algorithm embedded by a mathematical model is developed to solve the problem. Experiments conducted on numerous near-practical instances demonstrate the effectiveness of the solution methods. The results indicate that for medium- and large-scale instances, the methodology can achieve optimal or high-quality feasible solutions within a reasonable computation time.

The transportation of perishable cargo through existing intermodal freight networks has significantly increased. The focus on efficient transhipment of refrigerated containers has been driven by the strict quality requirements of perishable goods. An intermodal transportation path optimisation model is proposed, the objective is to minimise average cost and quality degradation. Considering the uncertain railway loading demand, the impact of refrigeration supply and failure on quality degradation is explored. The non-dominated sorting genetic algorithm II (NSGA-II) is adopted. A numerical experiment is conducted for the import of apples from the Port of Antwerp to Lanzhou. Results indicate that, although refrigeration failure time is brief, it can lead to up to 40% quality degradation compared to the supply state. The research provides robust transportation solutions for perishable products, recommending that the duration of single stops at nodes be limited to less than 11% of the total time to preserve freshness. For transfer station operators, shortening the duration of refrigeration failure and enhancing service levels within stations emerge as effective methods to attract shippers.

China currently operates over ten cross-border railways, with container transportation serving as a critical component of transnational rail freight. Following the operational commencement of the China-Laos Railway, challenges have emerged regarding mismatched container specifications at border crossings and the economically efficient repatriation of empty containers stranded in Laos. This study develops an optimization model to determine empty container repositioning routes for transnational railway operations while satisfying container demand at various stations over a specified period. By constructing a multi-layered intermodal network, we formulate a linear programming model with the optimization objective of minimizing total transportation costs for cross-border railway empty container repositioning operations. The study proposes a transportation problem-solving methodology incorporating nodal flow requirements. A case study of the China-Laos Railway demonstrates that direct rail transportation outperforms the highway (Vientiane-Haiphong) - maritime (Haiphong-Qinzhou) - railway (Qinzhou-Kunming) intermodal alternative in both cost efficiency and transit time. The proposed model effectively addresses low container turnover rates in China-Laos Railway operations while providing decision-makers with macro-level insights into empty container flow patterns across temporal phases. This analytical framework supports strategic freight organization and container management.

This study examines common-cause failure (CCF) in complex multi-state systems (MSSs), arising from factors such as external environmental influences and the aging of internal units. The random variables associated with system unit parameters are not restricted to exponential distributions and may instead follow various distribution types. The working time of each system unit is modeled using PH distributions, and a matrix-based analytical approach is applied. An improved universal generating function (UGF) is developed on the basis of the PH distribution, forming a reliability analysis method that integrates the enhanced UGF with PH modeling. The reliability of individual units under CCF is evaluated using the weight influence vector method and the factor model, and compared with those obtained under independent unit failures. Conducting CCF analysis is necessary, as it substantially reduces errors in practical applications. The variable speed hydraulic system of a pipe-lifting machine is used to demonstrate the accuracy and applicability of the method, and maintenance strategies are formulated to enhance system reliability. Protective measures are developed based on actual operating conditions to reduce CCF and improve system reliability. The study addresses the research gap in constructing a reliability model and performing corresponding computational analysis for cases in which unit parameter random variables in a multi-state system (MSS) may follow different distribution types under common-cause aging in engineering practice. Accounting for distributional differences in unit parameters provides new approaches for reliability analysis, strengthens the theoretical framework of MSSs, and offers practical guidance for engineering applications.

Maritime crude oil transportation network (COMTN) underpins energy security for importing countries, yet the post-disruption recovery phase of the network remains underexplored. The COMTN exhibits pronounced asymmetric flow dynamics and depends on coordinated actions by multiple stakeholders. To address this gap, we propose a novel resilience optimization model that integrates four stakeholder-oriented response strategies led by nation's decision. We construct a high-fidelity real-world COMTN model from crude oil tanker AIS data, explicitly representing both laden and ballast voyages, distinguishing direct return voyages (DRVs) from indirect return voyages (IRVs), and estimating the time cost of IRVs using a semi-Markov chain. Using China as a representative importing country and processing tanker trajectories for 2023, we apply the model to evaluate recovery performance under alternative disruption scenarios, uncovering distinct recovery priorities and strategy-dependent resilience characteristics of China's COMTN. The result shows that, for China's COMTN, the generalized cost based (GC-based) response strategy delivers the highest resilience across disruption scenarios. The ports and transport links in Northeast Asia, as well as the routes crossing the Strait of Hormuz, the Strait of Malacca and the Taiwan Strait, are consistently assigned high recovery priority. The proposed framework and findings provide practical guidance for importing countries to design more effective COMTN recovery plans and to enhance network resilience.

In the context of geopolitical conflicts, the varying operational tasks and economic drivers lead to distinct route and port selection strategies for laden and ballast crude oil tankers, reflecting functional and structural heterogeneity within the crude oil maritime transportation network (COMTN). This paper proposes a multiplex network model with laden and ballast layers to characterize the COMTN. Taking the Russia-Ukraine conflict as a case study, we examine changes in the global and four intercontinental COMTNs between 2021 and 2023. Specifically, we characterize the network structure using multiple empirical metrics, and characterize resilience by two dimensions of robustness and adaptability. We construct a redistribution model to characterize the dynamic adjustment of COMTN. Furthermore, we design a deliberate attack scenario based on the duplex collective influence (DCI) score of ports and four historical disruption scenarios to assess network resilience. Results show that the shift in global transport center of transport flows and regional reconfiguration alter the hierarchical importance of ports in the laden/ballast layer and significantly affect the average path cost (APC). After the conflict, resilience increases in the Middle East-East Asia (ME-EA) and Middle East-Europe (ME-EU) corridors but decreases in the North America-Europe (NA-EU) and North America-East Asia (NA-EA) corridors. These findings offer valuable insights for stakeholders and policymakers seeking to better understand the complex transport relationships in COMTN and enhance network resilience.