Pengjun Zheng

With the rapid expansion of global trade, the environmental impacts of port operations have attracted increasing attention. This study develops a mixed-integer programming (MIP) model to identify low-carbon energy mix replacement pathways for yard trucks (YTs), aiming to minimize total cost under low-carbon constraints while accounting for both economic and environmental performance. In the proposed model, the carbon emission cost is separated into transport and idling components to reflect differences in emissions across vehicle operating states. An electric YT (ET) replacement discount coefficient is introduced to quantify the reduction in the effective service capacity of ETs under port operating conditions. The practical significance of the proposed methodology is demonstrated through a case study. The results show that the optimized scheme reduces carbon intensity per unit throughput by 46% over the 15-year planning period, with an average annual decline of 4%. The results also reveal a staged transition pathway: diesel YTs (DTs) are gradually phased out, liquefied natural gas YTs (LNGTs) serve as an interim option, ETs are rapidly deployed and assume a dominant role, and hydrogen fuel cell YTs (HFCTs) increase steadily in the later stages. This study also includes various sensitivity tests, which illustrate that although the magnitude of total cost may vary across different scenarios, the overarching direction of transition remains robust. These findings can inform the formulation of energy mix optimization strategies for YTs.

Accurate traffic state estimation (TSE) for port motorway networks is critical for mitigating congestion and improving operational efficiency in port cities. Port motorways have their specific traffic patterns, such as surges in container truck demand triggered by vessel arrival events. Considering this characteristic, we propose a novel TSE method named HGCN-VA, enabling a real-time inductive inference based on a graph neural network (GNN). The framework constructs a heterogenous graph comprising both road sensors and ports, while introducing an influence weight learning module based on attention mechanisms to adaptively align vessel events with their traffic consequences. A hierarchical spatial module is further designed, where initial heterogeneous diffuse graph convolution layers explicitly model cross-domain interactions between port and sensor nodes, followed by standard diffusion graph convolution layers to capture traffic propagation patterns. Extensive experiments on a simulation port dataset demonstrate that HGCN-VA outperforms baselines in TSE accuracy. Ablation studies further validate the effectiveness of incorporating vessel information and the proposed heterogeneous spatial modeling strategy.

Maritime administrations frequently implement short-term intensive safety actions to mitigate collision risk in mixed commercial–fishing traffic waters. However, empirical evidence on their effectiveness remains limited because maritime accidents are rare and behavior-level risk indicators are not routinely incorporated into policy evaluation. This study develops an AIS-based evaluation framework that uses monthly near-miss counts as a behavior-level proxy of navigational risk and combines this proxy with a difference-in-differences (DID) design to assess a maritime special safety action in Ningbo–Zhoushan waters. Using large-scale AIS trajectory data, near-miss events are identified based on DCPA and TCPA criteria and then aggregated to the sea area–month level. The analysis covers 9 sea areas from January to December 2023 (108 observations). In the baseline two-way fixed-effects specification, the coefficient on Treat × Post is positive but statistically insignificant (β = 0.4094, SE = 0.2581), indicating that the intervention did not produce robust evidence of a reduction in the AIS-based near-miss indicator. Event-study estimates likewise show no statistically significant persistent dynamic treatment effect within the observation window. These findings suggest that, under the proxy measure and identification strategy used in this study, the special safety action did not generate a clearly identifiable reduction in near-miss counts in treated waters. Methodologically, the study demonstrates the practical value of combining AIS-derived behavioral indicators with quasi-experimental policy evaluation. At the same time, the results should be interpreted with caution, because near-miss counts are structurally related to traffic intensity and traffic composition, and the policy period overlaps with the seasonal fishing moratorium. The proposed framework nevertheless offers a useful basis for evidence-based evaluation of non-engineering maritime safety interventions in complex mixed-traffic environments.

Ship–bridge collisions in cross-sea bridge waterways are rare but potentially catastrophic events, making conventional accident-based risk assessment difficult to implement effectively. Existing AIS-based indicators capture vessel behaviors but insufficiently quantify bridge-passage safety margins, especially dynamic aspects such as crossing posture and readiness prior to bridge transit. To address this limitation, this study proposes a data-driven framework for detecting unsafe ship–bridge passages using two bridge-passage-oriented surrogate safety measures (SSMs) and extreme value theory (EVT). The Bridge-passage Lateral Clearance Margin (BLCM) quantifies the effective lateral safety margin retained during the realized bridge-crossing stage, while the Bridge-passage Readiness Lead Time (BRLT) measures how early a vessel becomes stably prepared for bridge passage before crossing. The Peaks Over Threshold (POT) model is first used to characterize the marginal extremes of the two indicators, and a bivariate threshold exceedance model (BTE) is then established to examine their joint risk behavior. Case studies of the Jintang Bridge and Zhoudai Bridge waterways demonstrate that the proposed framework can effectively screen and identify trajectories with unsafe or margin-deficient bridge-passage characteristics. The results show that unsafe passages are typically associated with both reduced lateral clearance and insufficient preparation time, and that joint modeling of the two indicators improves risk identification performance. The findings suggest that ship–bridge risk is better interpreted from the perspective of passage quality deficiency rather than simple geometric proximity. The proposed framework provides an interpretable tool for retrospective unsafe passage screening, traffic monitoring support, and post-event safety analysis in complex bridge waterways.

This study examines the probabilistic patterns associated with casualty severity in collisions between commercial and fishing vessels in China’s coastal waters. Using 137 official accident investigation reports from 2013 to 2022, a structured dataset capturing vessel characteristics, environmental conditions, and human liability factors was constructed. A Tree-Augmented Bayesian Network (TAN-BN) was developed to model the probabilistic interactions among these variables and to identify the key drivers of casualty severity. Sensitivity analysis based on mutual information indicates that fishing vessel length is the most influential factor affecting casualty outcomes (MI = 0.322), followed by time of occurrence, wind speed, visibility, and season. Scenario analysis using MPE indicates that severe casualty scenarios are associated with adverse temporal and environmental conditions such as nighttime, poor visibility, and open-water environments, while liability-specific analysis further shows that collisions attributed primarily to commercial vessel errors are most likely to result in 4–10 casualties. The results highlight the structural vulnerability of small fishing vessels and the critical role of environmental exposure in heterogeneous vessel encounters. This study provides an interpretable probabilistic framework for examining casualty severity patterns in maritime collisions and offers risk-informed insights for improving sustainable maritime safety management in mixed-traffic coastal waters.

To address the issue that static strategies, such as frequent closure or fixed-time opening, in emergency lane management on freeways struggle to adapt to dynamic traffic demands, a control method for Dynamic Time-Division Multiplexing of Emergency Lanes (DTMEL) is proposed. Based on real-time changes in the traffic status of social vehicles and emergency vehicles, the method designs a dynamic time slice division strategy to achieve dynamic response in road right allocation and ensure the priority of emergency vehicle. By constructing a dynamic spatial slice model that integrates driver decision-making behavior and different car-truck ratios, it provides a quantitative design basis for lane usage under the heterogeneity of social vehicles. Simulation experimental results show that in different car-truck ratios, the average speed of social vehicles under the DTMEL control method is 8.16–15.02% higher than that under the frequent closure strategy of emergency lanes and 2.15–3.29% higher than that under the fully open strategy control. In addition, compared to the fully opening strategy, the traffic capacity under DTMEL strategy control has increased by about 6.59–8.08%. The proposed method reduced the delay rate of emergency vehicles, verifying its feasibility in improving freeway operation efficiency and ensuring the priority of emergency vehicles.

Building world-class ports requires not only scale expansion but also sustainable structural capability. However, the concept of port “hardcore strength” remains insufficiently clarified and operationalized in existing sustainability and port evaluation research. In this study, port hardcore strength is understood as an integrated capability framework covering infrastructure efficiency and logistics capability, connectivity and regional integration, maritime services and industrial clustering, strategic leadership and innovation capability, and sustainable governance and green port development. This study proposes a sustainability-oriented evaluation framework for assessing the “hardcore strength” of world-class ports through a multi-dimensional indicator system. Methodologically, the study integrates the EWM and CRITIC, and introduces Bland–Altman analysis to examine whether the EWM and CRITIC weight vectors exhibit an obvious systematic bias prior to game-theoretic integration. Using 18 representative global ports from 2019 to 2023 as a case study, the results show that the overall ranking structure remains broadly stable, with Singapore Port and Shanghai Port consistently ranking first and second, respectively, while some middle-ranked ports exhibit moderate positional changes. The findings suggest that differences in world-class port development are rooted not only in operational scale, but also in the coordination of multiple capability dimensions. The study enriches the understanding of world-class port evaluation from a sustainability-oriented perspective.

Online ride-hailing platforms increasingly rely on differentiated incentive mechanisms to regulate driver participation and balance supply and demand. However, drivers’ adaptive responses to such incentives introduce dynamic feedback and uncertainty that static equilibrium models fail to capture. This study develops a dual-layer Stackelberg–evolutionary game framework in which the platform acts as a strategic leader setting the order allocation rates and prices, while heterogeneous drivers adapt their working-hour strategies through evolutionary dynamics. Using operational data from Ningbo, China, we calibrated the demand elasticity and driver cost parameters and identified endogenous fatigue-cost thresholds that govern regime shifts in strategy dominance. Simulation results show that uniform incentives tend to drive the system toward single-strategy lock-in, whereas differentiated order allocation and pricing effectively sustain multi-strategy coexistence and mitigate extreme supply polarization. The findings reveal how platform-led differentiation reshapes the evolutionary fitness landscape of drivers, providing actionable guidance for incentive design aimed at stabilizing supply structures, improving platform revenue, and protecting driver welfare.