Search Results (18)

Inland water transport, a critical component of integrated transportation systems, relies on the unobstructed status of critical waterways to ensure network efficiency. Firstly, a weighted topological network was constructed based on waterway class and length. The Leiden algorithm was then employed to divide the inland waterway network into communities, with community bridges identified as critical waterways. Finally, attack simulation experiments were conducted to verify the methodology. Results revealed that the Jiangsu inland waterway network exhibits a distinct community structure, and the regional division is closely aligned with the actual river system. The rapid performance degradation under community bridge attacks confirmed the validity of the critical waterway identification method. Furthermore, a recommended method for waterway class assignment was explored in the inland waterway weighting network. The innovative identification and assessment of critical waterways from the perspective of community detection breaks through the limitations of traditional methods that rely on betweenness centrality and waterway class. Vessel traffic flow across different waterway classes was analyzed using the Automatic Identification System (AIS) data, enabling tailored management strategies for critical waterways. This research provides theoretical support for an in-depth understanding of the structure and function of the inland waterway network, guiding policymaking and promoting the efficiency and security of inland water transport. Full article

Regional comprehensive transportation infrastructures constitute the fundamental basis for the development of inland urban agglomerations. To elucidate the role of comprehensive transportation in shaping the spatial organization and expansion of urban agglomerations, this study takes the Yangtze River Middle Reaches Urban Agglomeration (YRMRUA) as a case example. It examines the spatial relationships between transportation network layout and spatial expansion patterns using fractal dimension based on traffic accessibility, traffic-weighted linear density, and Pearson correlation analysis. The key findings of this study are as follows: (1) The YRMRUA exhibits a partial fractal growth pattern influenced by transportation development, which indicates that the comprehensive transportation has a significant but limited impact on YRMRUA. (2) There is a moderate correlation between traffic-weighted linear density and spatial expansion intensity within YRMRUA. (3) Specific groups such as the Wuhan–Ezhou–Huanggang–Huangshi group, Changsha–Zhuzhou–Xiangtan group, and Nanchang–Yichun group have formed in areas where transportation development and spatial expansion are at the forefront. (4) Different modes of transportation, including waterway transportation, railway transportation, and road transportation, have varying effects on spatial expansion. The integration of these modes forms the fundamental framework of urban agglomerations. Full article

The inherent complexity of traffic patterns in inland river tributary waterways presents significant challenges in predicting ship behavior, resulting in elevated accident risks compared to general waterways. With the intelligent development of inland navigation, conducting research on autonomous navigation decision-making for tributary waterway ships is crucial to improving navigation safety and efficiency. Based on the characteristics of the navigation environment, a digital traffic environment model for inland waterways with tributaries is constructed to meet the information requirements of autonomous navigation decision-making. The ship encounter process is analyzed, and a collision risk identification model based on trajectory derivation is proposed, which accounts for the uncertainty of ship maneuvering in tributary waterways. Under the premise of compliance with the “Rules of the People’s Republic of China for Inland River Collision Avoidance” (RPRCIRCA) and adherence to good seamanship, an autonomous navigation decision-making method is developed by integrating an improved Line-of-Sight tracking model with a collision avoidance strategy based on exhaustive course-speed maneuver combinations. The system’s performance is validated through simulation experiments, with trajectory tracking deviations demonstrated to remain below 49 m under wind-current disturbances while minimum encounter distances with target ships are maintained above 48 m. Adaptive response capabilities to maneuvering variations of target ships are confirmed, along with the preservation of navigation precision in complex tributary environments. Full article

Inland water transportation is regarded as a crucial component of global trade, yet its reliability has been increasingly challenged by uncertainties such as extreme weather, port congestion, and geopolitical tensions. Although substantial research has focused on the structural characteristics of inland water transportation networks, the dynamic responses of these networks to disruptions remain insufficiently explored. This gap in understanding is critical for enhancing the resilience of global transportation systems as trade volumes grow and risks intensify. In this study, percolation theory was applied to evaluate the reliability of the Yangtze River transportation network. Ship voyage data from 2019 were used to construct a complex network model, and simulations of node removal were performed to identify key vulnerabilities within the network. The results showed that the failure of specific nodes significantly impacts the network’s connectivity, suggesting which nodes should be prioritized for protection. This research offers a dynamic framework for the assessment of inland water transportation network reliability and provides new insights that could guide policy decisions to improve the resilience of critical waterway systems. By identifying potential points of failure, this study contributes to the development of a more robust global trade infrastructure. Full article

Inland waterways, characterized by their complex, narrow paths, see significantly higher traffic volumes compared to maritime routes, increasing the regulatory demands on traffic management. Predictive modeling of ship traffic flows, utilizing real AIS historical data, enhances route and docking planning for ships and port managers. This approach boosts transportation efficiency and safety in inland waterway navigation. Nevertheless, AIS data are flawed, marred by noise, disjointed paths, anomalies, and inconsistent timing between points. This study introduces a data processing technique to refine AIS data, encompassing segmentation, outlier elimination, missing point interpolation, and uniform interval resampling, aiming to enhance trajectory analysis reliability. Utilizing this refined data processing approach on ship trajectory data yields independent, complete motion profiles with uniform timing. Leveraging the Transformer model, denoted TRFM, this research integrates processed AIS data from the Yangtze River to create a predictive dataset, validating the efficacy of our prediction methodology. A comparative analysis with advanced models such as LSTM and its variants demonstrates TRFM’s superior accuracy, showcasing lower errors in multiple metrics. TRFM’s alignment with actual trajectories underscores its potential for enhancing navigational planning. This validation not only underscores the method’s precision in forecasting ship movements but also its utility in risk management and decision-making, contributing significantly to the advancement in maritime traffic safety and efficiency. Full article

Identifying the main sources of risk for different types of waterways helps to develop targeted risk control strategies for different river segments. To improve the level of risk management in inland waterways for sustainable development, a two-stage risk evaluation model is proposed in this study by integrating a fuzzy rule base and Bayesian networks. The model evaluates risk sources from the following four dimensions: probability of occurrence, visibility, probability of causing accidents, and consequences. Typical river sections in the upper, middle, and lower reaches of the Yangtze River were selected as cases, and 19 risk sources were identified and comparatively analyzed from the perspectives of humans, ships, the environment, and management. The fuzzy rule base is employed to compare expert opinions, yielding three key risk sources for each section based on their risk values. The findings reveal certain commonalities in the principal risk sources across sections. For example, natural disasters (landslides, earthquakes, and extreme hydrological conditions) are present in both the middle and lower reaches, and an insufficient channel width is common in the upper and middle reaches. However, the key risk sources differ among the sections. The upper reaches are primarily threatened by the improper management of affiliated vessels and adverse weather, while the middle reaches suffer from insufficient channel width surplus, and the lower reaches are mainly threatened by high vessel traffic density and low-quality crews. The results of the study show that the key risk sources in each section of the Yangtze River have obvious differences and need to be assessed according to the characteristics of different sections. This study can provide a reference for decision-making in inland waterway risk management by maritime safety authorities. Full article

Motivated by the operational scenarios of lock scheduling, we propose a serial-lock chain navigation problem (SLCNP) modeled on the Three Gorges-Gezhouba Dam (TGGD) for the first time. Ship grouping, synchronized moving, and grouped waiting operations are integrated into the ship navigation process. A mixed integer programming (MIP) model that incorporates real-world constraints such as ship priority, service fairness, traffic flow equilibrium, and phased ship placement is presented to optimize ship throughput and ship stay time. To solve the SLCNP, a sort-pick strategy-based swarm intelligence algorithm (SPSSIA) framework is developed that integrates the characteristics of SLCNP through a hybrid multi-section encoding method and a two-stage heuristic decoding approach. A swarm intelligence evolution mechanism is used to improve the search ability and robustness of the framework. Several instances are generated based on real data to verify the correctness and effectiveness of the model and algorithm. Computational results demonstrate the applicability and effectiveness of the proposed SPSSIA. Further analysis of the experimental results indicates that the key impact factors significantly influence the navigational performance of the TGGD system. The results of this study will provide practical guidance for the operational processes of inland river hubs with comparable characteristics. Full article

Compared to maritime vessel traffic accidents, there is a scarcity of available, and only incomplete, accident data for inland waterway accidents. Additionally, the characteristics of different waterway segments vary significantly, and the factors affecting navigation safety risks and their mechanisms may also differ. Meanwhile, in recent years, extreme weather events have been frequent in inland waterways, and there has been a clear trend towards larger vessels, bringing about new safety hazards and management challenges. Currently, research on inland waterway navigation safety risks mainly focuses on risk assessment, with scarce quantitative studies on risk mitigation measures. This paper proposes a new method for improving inland waterway traffic safety, based on a cost-effectiveness trade-off approach to mitigate the risk of vessel traffic accidents. The method links the effectiveness and cost of measures and constructs a comprehensive cost-benefit evaluation model using fuzzy Bayesian and quantification conversion techniques, considering the reduction effects of risk mitigation measures under uncertain conditions and the various costs they may incur. Taking the upper, middle, and lower reaches of the Yangtze River as examples, this research evaluates key risk mitigation measures for different waterway segments and provides the most cost-effective strategies. Findings reveal that, even if different waterways share the same key risk sources, the most cost-effective measures vary due to environmental differences. Moreover, there is no inherent correlation between the best-performing measures in terms of benefits and the lowest-cost measures, nor are they necessarily recommended. The proposed method and case studies provide theoretical support for scientifically formulating risk mitigation measures in complex environments and offer guidance for inland waterway management departments to determine future key work directions. Full article

Exploring the dynamic variation in ecosystem services and clarifying the driving mechanism will help with the formulation of effective ecological environment protection policies. Accordingly, this study sought to reveal the complex variability in ecosystem services in the Yellow River Delta (YRD) at a higher temporal resolution and the transition between the main driving factors in different periods. To this end, we used the economic equivalent factor valuation method to quantify the ecosystem service value from 2000 to 2019 at 5-year intervals. Furthermore, the Geo-detector model was used to identify the main driving factors and interaction between the driving factors of ecosystem service value variations. Then, we analyzed the temporal and spatial dynamic variations in the ecosystem service value and the transitions between the main driving factors in different periods. The main results are as follows: (1) From 2000 to 2019, the ecosystem service value of the YRD showed an increasing trend followed by a decline, whereby water and construction land increased and the other classes of land decreased. Overall, the inland and coastal distribution patterns exhibited low and high values, respectively. (2) The main driving factors of ecosystem service value variations were the NDVI and topographical factors (aspect, slope, elevation), which had q values that were stable and greater than those of the other factors. Although human activity, tourist resource concentration and traffic convenience factors had a comparatively minor effect on ecosystem services, we noted a trend where their effects increased from 2000 to 2019. (3) The detection of interactions revealed complex mechanisms affecting the variation in the YRD. Interactions between variables had a stronger influence than individual effects. The interactions between the Normalized Difference Vegetation Index (NDVI) and other factors consistently had the most significant impact. These interactions primarily shaped the spatial and temporal distribution of ecosystem services. The NDVI and human activities exhibited nonlinear enhancement. These results contribute to improving our cognition of the factors and mechanisms influencing ecosystem services, offering theoretical support for the improvement of ecosystem services in the YRD. Full article

Due to the difference of industrialization degree and meteorological conditions, there are obvious differences in the composition of air pollution between islands and inland areas. With Zhoushan (ZS) and Nanjing (NJ) representing islands and inland cities in the Yangtze River Delta, the differences in vertical distribution of atmospheric components were investigated. A combination of multi-axial differential optical absorption spectroscopy (MAX-DOAS), weather research and forecasting (WRF), and potential source contribution function (PSCF) models were used to obtain vertical distribution data for aerosols, nitrogen dioxide (NO₂) and formaldehyde (HCHO), meteorological factors, and pollution sources in summer 2019. The findings indicate that, except for the aerosol extinction coefficient (AE), the atmospheric composition at the ZS site was not significantly stratified. However, the AE, NO₂, and HCHO at NJ all displayed a decreasing trend with altitude. Here is the interesting finding that the ZS site has a higher AE value than the NJ site, while NJ displays higher NO₂ and HCHO columns than the ZS site. This discrepancy was primarily attributable to Zhoushan City’s extremely low traffic emissions when compared to inland cities. In addition, HCHO in the YRD region was significantly affected by human activities. Analysis of potential pollution sources found that regional transport contributed to differences in atmospheric composition at different altitudes in different regions. Aerosols, NO₂, and HCHO in Nanjing were significantly affected by transport in inland areas. Aerosols in Zhoushan were easily affected by transport in the Yellow Sea and East China Sea, and NO₂ and HCHO were significantly affected by transport contributions from surrounding areas in inland areas. The study strongly suggests that land and sea breezes play an important role in the vertical distribution of aerosols over island regions. Full article

Accurate vessel travel time estimation is crucial for optimizing port operations and ensuring port safety. Existing vessel travel time prediction models primarily rely on path-finding algorithms and corresponding distance/speed relationships to calculate travel time. However, these models overlook the complex nature of vessel travel time, which is influenced by multiple traffic-related factors such as collision avoidance, shortest path selection, and vessel personnel performance. The lack of consideration for these specific aspects limits the accuracy and applicability of current models. We propose a novel context-aware deep learning approach for inland vessel travel time prediction. Firstly, we introduce a complex network that captures vessel–vessel interaction contexts, providing valuable traffic environment information as an input for the deep learning model. Additionally, we employ a convolutional neural network to extract spatial trajectory information, which is then integrated with interaction contexts and indirect context information. In the vessel travel time prediction procedure, we utilize a long short-term memory network to capture the temporal dependence within consecutive channel sections’ fused multiple context feature sets. Extensive experiments incorporating historical data from the Wuhan section of the Yangtze River in China demonstrate the superiority of our proposed model over classical models in predicting vessel travel time. Importantly, our model accounts for the specific traffic contexts that had previously been overlooked, leading to improved accuracy and applicability in inland vessel travel time prediction. Full article

Ship position prediction is the key to inland river and sea navigation warning. Maritime traffic control centers, according to ship position monitoring, ship position prediction and early warning, can effectively avoid collisions. However, the prediction accuracy and computational efficiency of the ship’s future position are the key problems to be solved. In this paper, a path prediction model (GA–ACO–BP) combining a genetic algorithm, an ant colony algorithm and a BP neural network is proposed. The model is first used to perform deep pretreatment of raw AIS data, with the main body of the BP neural network as a prediction model, focused on the complementarity between genetic and ant colony algorithms, to determine the ant colony initialization pheromone concentration by the genetic algorithm, design the hybrid genetic–ant colony algorithm, and optimize this to the optimal weight and threshold of the BP neural network, in order to improve the convergence speed and effect of the traditional BP neural network. The test results show that the model greatly improves the fitness of track prediction, with higher accuracy and within a shorter time, and has a certain real-time and extensibility for track prediction of different river segments. Full article

The novel coronavirus (COVID-19) epidemic broke out in Wuhan at the end of 2019 and spread around the whole of China in 2020. In order to reduce the spread of COVID-19, transportation and industrial activities in different regions were limited to varying degrees. This study uses bivariate concentration polar plots, integrated with k-means clustering and temporal variation analyses for PM_2.5 time series data, to understand the PM_2.5 source characteristics in Shanghai during the COVID-19 pandemic in the winter of 2020. Our findings show that 34.33% of the PM_2.5 particles arise from external sources while 65.67% are from local sources. The results of source apportionment combined with land use, wind speed, and direction data are further used to locate the most likely directions of different source categories and geographic origins of PM_2.5. During the lockdown period in 2020, traffic and industrial activity were still primary local sources of PM_2.5 emissions in Shanghai. The growth of motor vehicle ownership, limited public transport, and a large volume of freight transport in Shanghai result in a higher level of PM_2.5 concentrations on weekends than in midweeks. On the other hand, the regional-scale transport of air pollutants from the Yangtze River Delta, the Central Plains, the inland area of northern China, and coastal cities in the north and south of Shanghai aggravates PM_2.5 pollution in Shanghai under unfavorable meteorological conditions. The methods and results presented here lay a basis for further study on the complicated effects of meteorological and anthropogenic factors on PM_2.5 pollution and on the development of detailed and urgent strategies for the improvement of air quality. Full article

Building inland waterways affects the natural structure, formation, and extent of the riverbed and riparian zone. It alters the hydrology and sediment deposition conditions and hence damages the aquatic ecosystem. To address the effects of the construction of inland waterways on the riverine biome, benthic macroinvertebrate communities were compared at different building stages of inland waterways along a gradient of shipping traffic density at two montane rivers in China. The Shannon–Wiener diversity index of the benthic macroinvertebrate communities ranged from 0.4 to 1.6; the lowest value was recorded in the completed inland waterway, while the highest value was recorded in the unaffected stretch. Principal component analysis and canonical correlation analysis showed the communities in the inland waterways to be distinct from those in the natural riparian habitats. Our results suggest that benthic macroinvertebrate communities can reflect the damage done by the hydromorphological modifications caused by building inland waterways. Benthic macroinvertebrate diversity and abundance should therefore be included when assessing the impact of building and operating inland waterways. Full article

The causes of maritime accidents are complex, mostly due to the coupling of four types of factors: human-ship-environmental-management. To effectively analyze the causes of maritime accidents in China, and reveal the risk coupling characteristics of accidents, this paper establishes the N-K model of maritime accident, and calculates and analyzes the four types of coupling of risk factors affecting safety in maritime traffic. This paper collects 922 maritime accidents that occurred in China from 2000 to 2020, and analyzes the location, type, and level of accidents and uses the trigger principle to describe the process of accidents. For marine and inland river accidents, this paper calculates the four types of coupling values of risk factors (single-factor coupling, two-factor coupling, three-factor coupling, four-factor coupling) for comparison and analysis. In addition, this paper calculates the coupling values of six typical maritime accidents of collision, sinking, contact, fire/explosion, stranding, grounding. According to the coupling values and the frequency of sub-factors, this paper analyzes the coupling characteristics of maritime accidents. The results show that in maritime accidents, as the number of risk factors participating in the coupling increases, the coupling value increases, and the multi-factor coupling is more likely to cause accidents. The overall situation of risk coupling causes of maritime accidents is basically consistent with inland river accidents, but they have their own characteristics in the specific degree of risk coupling and the dominant risk elements. In different types of maritime accidents, the risk coupling has different characteristics, and the dominant risk factors are also different. Full article