A model for the evaluation of environmental impact indicators for a sustainable maritime transportation systems

Maritime shipping is considered the most efficient, low-cost means for transporting large quantities of freight over significant distances. However, this process also causes negative environmental and societal impacts. Therefore, environmental sustainability is a pressing issue for maritime shipping management, given the interest in addressing important issues that affect the safety, security, and air and water quality as part of the efficient movement of freight throughout the coasts and waterways and associated port facilities worldwide. In-depth studies of maritime transportation systems (MTS) can be used to identify key environmental impact indicators within the transportation system. This paper develops a tool for decision making in complex environments; this tool will quantify and rank preferred environmental impact indicators within a MTS. Such a model will help decision-makers to achieve the goals of improved environmental sustainability. The model will also provide environmental policy-makers in the shipping industry with an analytical tool that can evaluate tradeoffs within the system and identify possible alternatives to mitigate detrimental effects on the environment.     

Bayesian networks for maritime traffic accident prevention: Benefits and challenges

Bayesian networks are quantitative modeling tools whose applications to the maritime traffic safety context are becoming more popular. This paper discusses the utilization of Bayesian networks in maritime safety modeling. Based on literature and the author's own experiences, the paper studies what Bayesian networks can offer to maritime accident prevention and safety modeling and discusses a few challenges in their application to this context. It is argued that the capability of representing rather complex, not necessarily causal but uncertain relationships makes Bayesian networks an attractive modeling tool for the maritime safety and accidents. Furthermore, as the maritime accident and safety data is still rather scarce and has some quality problems, the possibility to combine data with expert knowledge and the easy way of updating the model after acquiring more evidence further enhance their feasibility. However, eliciting the probabilities from the maritime experts might be challenging and the model validation can be tricky. It is concluded that with the utilization of several data sources, Bayesian updating, dynamic modeling, and hidden nodes for latent variables, Bayesian networks are rather well-suited tools for the maritime safety management and decision-making.     

A risk analysis of winter navigation in Finnish sea areas

Winter navigation is a complex but common operation in north-European sea areas. In Finnish waters, the smooth flow of maritime traffic and safety of vessel navigation during the winter period are managed through the Finnish–Swedish winter navigation system (FSWNS). This article focuses on accident risks in winter navigation operations, beginning with a brief outline of the FSWNS. The study analyses a hazard identification model of winter navigation and reviews accident data extracted from four winter periods. These are adopted as a basis for visualizing the risks in winter navigation operations. The results reveal that experts consider ship independent navigation in ice conditions the most complex navigational operation, which is confirmed by accident data analysis showing that the operation constitutes the type of navigation with the highest number of accidents reported. The severity of the accidents during winter navigation is mainly categorized as less serious. Collision is the most typical accident in ice navigation and general cargo the type of vessel most frequently involved in these accidents. Consolidated ice, ice ridges and ice thickness between 15 and 40 cm represent the most common ice conditions in which accidents occur. Thus, the analysis presented in this article establishes the key elements for identifying the operation types which would benefit most from further safety engineering and safety or risk management development.     

Evaluating the efficiency of local municipalities in providing traffic safety using the Data Envelopment Analysis

The purpose of this study was to estimate the relative efficiency of 197 local municipalities in traffic safety in Israel during 2004–2009, using Data Envelopment Analysis (DEA). DEA efficiency is based on multiple inputs and multiple outputs, when their weights are unknown. We used here inputs reflecting the resources allocated to the local municipalities (such as funding), outputs include measures that reflect reductions in accidents (such as accidents per population), and intermediate variables known as safety performance indicators (SPI): measures that are theoretically linked to crash and injury reductions (such as use of safety belts). Some of the outputs are undesirable. Using DEA, the local municipalities were rank-scaled from the most efficient to the least efficient and required improvements for inefficient municipalities were calculated. We found that most of the improvements were required in two intermediate variables related to citations for traffic violations. Several DEA versions were used including a two-stage model where in the first stage the intermediate variables are the outputs, and in the second stage they are the inputs. Further analyses utilizing multiple regressions were performed to verify the effect of various demographic parameters on the efficiency of the municipalities. The demographic parameters tested for each local municipality were related to the size, age, and socio-economic level of the population. The most significant environmental variable affecting the efficiency of local municipalities in preventing road accidents is the population size of the local authority; the size has a negative effect on the efficiency. As far as we could determine, this is the first time that the DEA is used to measure the efficiency of local municipalities in improving traffic safety.     

A fuzzy-logic-based approach to qualitative safety modelling for marine systems

Safety assessment based on conventional tools (e.g. probability risk assessment (PRA)) may not be well suited for dealing with systems having a high level of uncertainty, particularly in the feasibility and concept design stages of a maritime or offshore system. By contrast, a safety model using fuzzy logic approach employing fuzzy IF–THEN rules can model the qualitative aspects of human knowledge and reasoning processes without employing precise quantitative analyses. A fuzzy-logic-based approach may be more appropriately used to carry out risk analysis in the initial design stages. This provides a tool for working directly with the linguistic terms commonly used in carrying out safety assessment. This research focuses on the development and representation of linguistic variables to model risk levels subjectively. These variables are then quantified using fuzzy sets. In this paper, the development of a safety model using fuzzy logic approach for modelling various design variables for maritime and offshore safety based decision making in the concept design stage is presented. An example is used to illustrate the proposed approach.     

基于AIS数据的航运物流港口调度优化研究

目的 针对宁波舟山港区的复杂航道水域与密集物流交通流,研究更加有效的调度方案,达成调度时间和等待时间最小化,即效率最大化。方法 分析宁波舟山港区航道的航行情况,提出交会处复杂航道水域存在的问题,以调度时间和等待时间最小为目标的多目标函数,建立复杂航道水域船舶调度模型。针对大量的船舶AIS数据,构建基于神经网络的航道水域调度模型,对不同类型、不同大小的船舶建立速度变化和船舶预测模型,实现对船舶调度状态的预测。设计以传统粒子群算法为基础的改良版船舶调度算法。结果 算法对模型求解表明,根据不同船长与间距可判别交通流拥挤程度进而对船舶进行调度。通过模型预测到可能产生拥挤,则应当选择小型船只走条帚门航道,大型船只走虾峙门航道,并且尽量避免产生拥堵。结论 使用该模型与算法可以有效地提升船舶调度效率,为复杂航运物流港口调度优化研究提供了一定理论基础。     

Multivariate crash modeling for motor vehicle and non-motorized modes at the macroscopic level

Macroscopic traffic crash analyses have been conducted to incorporate traffic safety into long-term transportation planning. This study aims at developing a multivariate Poisson lognormal conditional autoregressive model at the macroscopic level for crashes by different transportation modes such as motor vehicle, bicycle, and pedestrian crashes. Many previous studies have shown the presence of common unobserved factors across different crash types. Thus, it was expected that adopting multivariate model structure would show a better modeling performance since it can capture shared unobserved features across various types. The multivariate model and univariate model were estimated based on traffic analysis zones (TAZs) and compared. It was found that the multivariate model significantly outperforms the univariate model. It is expected that the findings from this study can contribute to more reliable traffic crash modeling, especially when focusing on different modes. Also, variables that are found significant for each mode can be used to guide traffic safety policy decision makers to allocate resources more efficiently for the zones with higher risk of a particular transportation mode.     

基于IPSO-BP的船舶航迹预测研究

目的 面对复杂的海上交通及密集的物流交通流,及时有效地对船舶航迹进行跟踪预测显得尤为重要,针对传统船舶航迹预测方法精确度低且效率低下的问题,提出一种改进方法。方法 在船舶自动识别系统（AutomaticIdentificationSystem,AIS）数据的基础上,建立改进粒子群算法（IPSO）与BP神经网络相结合的船舶轨迹预测模型,利用船舶历史航行轨迹数据,实现对未来船舶运动的预测。选取宁波舟山港的船舶历史轨迹数据进行实验,并将IPSO-BP模型的实验结果与其他模型进行比较。结果 不同模型航迹预测对比结果表明,IPSO-BP模型的性能较好,其预测精度较高,适用于船舶轨迹预测。结论 使用IPSO-BP模型能够更加精准地预测船舶航迹,在船舶危险预警、船舶异常监测等方面具有重要的指导作用。     

Towards the development of intelligent navigation support systems for group shipping and global marine traffic control

Computerised navigation is a developing technology in a branch of autonomous and intelligent engineering systems. It aims to enhance safety and security at sea and to improve the reliability of ship-to-ship operations and global marine traffic control. A recently standardised automatic identification system (AIS) takes full advantage of GPS based real-time positioning and timing information of ships. The use of the AIS is steadily growing to meet the needs of the merchant shipping and fishing industries. Nevertheless, a significant decrease in the number of ship accidents and hazardous incidents in open waterways has yet to occur. The work gathered in this paper is to establish a proactive means to prevent marine accidents by the use of the full potential of the AIS. Specifically, this paper describes the development of an AIS synthesis capable navigation support system enhanced with a degree of intelligence built from a physical model suitable to capture the dynamics of group based navigation of ocean-going vessels. The system envisioned is to provide adequate decision making support for group shipping, especially during the critical maneuvering in restricted areas such as channel passing and harbour approaches. Foundations for and experimental results of this study, including an analysis with the simulated traffic of Tokyo Uraga Channel route, are presented.     

Road network safety evaluation using Bayesian hierarchical joint model

Safety and efficiency are commonly regarded as two significant performance indicators of transportation systems. In practice, road network planning has focused on road capacity and transport efficiency whereas the safety level of a road network has received little attention in the planning stage. This study develops a Bayesian hierarchical joint model for road network safety evaluation to help planners take traffic safety into account when planning a road network. The proposed model establishes relationships between road network risk and micro-level variables related to road entities and traffic volume, as well as socioeconomic, trip generation and network density variables at macro level which are generally used for long term transportation plans. In addition, network spatial correlation between intersections and their connected road segments is also considered in the model.A road network is elaborately selected in order to compare the proposed hierarchical joint model with a previous joint model and a negative binomial model. According to the results of the model comparison, the hierarchical joint model outperforms the joint model and negative binomial model in terms of the goodness-of-fit and predictive performance, which indicates the reasonableness of considering the hierarchical data structure in crash prediction and analysis. Moreover, both random effects at the TAZ level and the spatial correlation between intersections and their adjacent segments are found to be significant, supporting the employment of the hierarchical joint model as an alternative in road-network-level safety modeling as well.     

Risk assessment in maritime transportation

A review is presented of different approaches to quantify the risk in maritime transportation. The discussion of several accident statistics provides a global assessment of the risk levels and its differentiation in ship types and main types of ship losses. Early studies in the probability of ship loss by foundering and capsizing are reviewed. The approaches used to assess the risk of structural design are addressed. Finally a brief account is given of recent development of using formal safety assessments to support decision making on legislation applicable internationally to maritime transportation.     

Formal safety assessment based on relative risks model in ship navigation

Formal safety assessment (FSA) is a structured and systematic methodology aiming at enhancing maritime safety. It has been gradually and broadly used in the shipping industry nowadays around the world. On the basis of analysis and conclusion of FSA approach, this paper discusses quantitative risk assessment and generic risk model in FSA, especially frequency and severity criteria in ship navigation. Then it puts forward a new model based on relative risk assessment (MRRA). The model presents a risk-assessment approach based on fuzzy functions and takes five factors into account, including detailed information about accident characteristics. It has already been used for the assessment of pilotage safety in Shanghai harbor, China. Consequently, it can be proved that MRRA is a useful method to solve the problems in the risk assessment of ship navigation safety in practice.     

Proposing integrated Shannon’s entropy–inverse data envelopment analysis methods for resource allocation problem under a fuzzy environment

In this study, a two-phase methodology for resource allocation problems under a fuzzy environment is proposed. In the first phase, the imprecise Shannon’s entropy method and the acceptability index are suggested, for the first time in the literature, to select input and output variables to be used in the data envelopment analysis (DEA) application. In the second step, an interval inverse DEA model is executed for resource allocation in a short run. In an effort to exemplify the practicality of the proposed fuzzy model, a real case application has been conducted involving 16 cement firms listed in Borsa Istanbul. The results of the case application indicated that the proposed hybrid model is a viable procedure to handle input–output selection and resource allocation problems under fuzzy conditions. The presented methodology can also lend itself to different applications such as multi-criteria decision-making problems.     

Predicting crash risk and identifying crash precursors on Korean expressways using loop detector data

In order to improve traffic safety on expressways, it is important to develop proactive safety management strategies with consideration for segment types and traffic flow states because crash mechanisms have some differences by each condition. The primary objective of this study is to develop real-time crash risk prediction models for different segment types and traffic flow states on expressways. The mainline of expressways is divided into basic segment and ramp vicinity, and the traffic flow states are classified into uncongested and congested conditions. Also, Korean expressways have irregular intervals between loop detector stations. Therefore, we investigated on the effect and application of the detector stations at irregular intervals for the crash risk prediction on expressways. The most significant traffic variables were selected by conditional logistic regression analysis which could control confounding factors. Based on the selected traffic variables, separate models to predict crash risk were developed using genetic programming technique. The model estimation results showed that the traffic flow characteristics leading to crashes are differed by segment type and traffic flow state. Especially, the variables related to the intervals between detector stations had a significant influence on crash risk prediction under the uncongested condition. Finally, compared with the single model for all crashes and the logistic models used in previous studies, the proposed models showed higher prediction performance. The results of this study can be applied to develop more effective proactive safety management strategies for different segment types and traffic flow states on expressways with loop detector stations at irregular intervals.     

带有初始缺陷船用加筋板极限强度的不确定性分析

由于实际工程结构尺寸及材料性能等存在一定的变异性,这将导致其极限承载能力具有不确定性。首先基于逐步破坏理论,采用非线性有限元方法对带有初始缺陷的典型船用加筋板的极限强度进行研究。采用Monte Carlo数值模拟结合拟合技术建立结构不确定性分析模型。以此为基础,考虑结构尺寸、材料参数及初始缺陷的随机性,对于加筋板极限强度的不确定性进行探讨。为在计算量较小的前提下得到准确的随机统计规律,提出基于可靠性反问题的对数正态三参数拟合方法,并与传统拟合方法进行比较分析。结果表明对数正态三参数法可高效准确地对于加筋板极限强度的统计特性进行预报,为船舶的安全性分析提供基础。     

舰船辐射噪声广义多尺度数学形态学特征提取与应用研究

为稳定提取复杂水声环境下舰船辐射噪声的有效特征,在数学形态学方法的基础上提出一种广义多尺度数学形态腐蚀谱熵(generalized multiscale pattern erosion spectrum entropy, GMPESE)的舰船辐射噪声非线性特征提取方法。通过对千岛湖及东海实测舰船辐射噪声处理,验证了不同环境下该特征提取方法的可行性,分析了相关参数选取对特征区分度的影响,并比较了该特征提取方法与多尺度熵(multiscale sample entropy, MSE)特征的识别性能。数据处理结果表明,综合比较运算耗时、提取稳定的特征所需信号时长及复杂环境下目标识别准确率,GMPESE特征提取方法具有更大的优势。     

Efficiency measurement of research groups using Data Envelopment Analysis and Bayesian networks

Applications of non-parametric frontier production methods such as Data Envelopment Analysis (DEA) have gained popularity and recognition in scientometrics. DEA seems to be a useful method to assess the efficiency of research units in different fields and disciplines. However, DEA results give only a synthetic measurement that does not expose the multiple relationships between scientific production variables by discipline. Although some papers mention the need for studies by discipline, they do not show how to take those differences into account in the analysis. Some studies tend to homogenize the behaviour of different practice communities. In this paper we propose a framework to make inferences about DEA efficiencies, recognizing the underlying relationships between production variables and efficiency by discipline, using Bayesian Network (BN) analysis. Two different DEA extensions are applied to calculate the efficiency of research groups: one called CCRO and the other Cross Efficiency (CE). A BN model is proposed as a method to analyze the results obtained from DEA. BNs allow us to recognize peculiarities of each discipline in terms of scientific production and the efficiency frontier. Besides, BNs provide the possibility for a manager to propose what-if scenarios based on the relations found.     

Data envelopment analysis of AGV fleet sizing at a port container terminal

A decision-making approach based on Data Envelopment Analysis (DEA) for determining the efficient container handling processes (considering the number of employed Automated Guided Vehicles (AGVs)) at a port container terminal (PCT) is presented in this paper. Containers are unloaded from the ship by quay cranes and transported to the storage area by AGVs. We defined performance measures of proposed container handling processes and analysed the effects when changing the number of AGVs. The values of performance measures were collected and/or calculated from simulation. Container handling process, with a fixed number of quay cranes, when a different number of AGVs is used to transport containers from berth to assigned locations within storage area, represents a decision-making unit (DMU). We applied the basic CCR (Charnes, Cooper and Rhodes) DEA model with two inputs: average ship operating delay costs and average operating costs of employed equipment at a PCT, and two outputs: average number of handled import containers per ship and weighted average utilisation rate of equipment at a PCT. DEA method proved to be useful when testing different DMUs and when determining efficient DMUs for planning purposes. This study shows that efficiency evaluation of AGV fleet sizing and operations is useful for planning purposes at PCTs.     

数据扩充下0-1膨胀几何分布的客观贝叶斯分析及应用

在交通安全、公共卫生、风险管理等实际应用领域，经常会遇到0观测值、1观测值出现较多的样本数据。为了更深入地研究这类数据集，本文提出了0-1膨胀几何分布模型，通过设计数据扩充策略和构造完全似然函数，得到了Jeffreys先验和reference先验。设置不同的样本容量和参数真值，对不同的客观先验进行数值仿真和比较分析。最后，利用0-1膨胀几何分布模型对底特律城市交通事故死亡数据集进行了分析。研究表明，采用客观贝叶斯方法比主观贝叶斯方法能够实现更佳的拟合效果。     

Autonomous radiation monitoring of small vessels

Small private vessels are one avenue by which nuclear materials may be smuggled across international borders. While one can contemplate using the land-based approach of radiation portal monitors on the navigable waterways that lead to many ports, these systems are ill-suited to the problem. In contrast to roadways, where lanes segregate vehicles, and motion is well controlled by inspection booths; channels, inlets, and rivers present chaotic traffic patterns populated by vessels of all sizes. A unique solution to this problem is based on a portal-less portal monitor designed to handle free-flowing traffic on roadways with up to five-traffic lanes. The instrument uses a combination of visible-light and gamma-ray imaging to acquire and link radiation images to individual vehicles. This paper presents the results of a recent test of the system in a maritime setting.