An advanced solution for efficient logistics management at both company and network level

This paper proposes an advanced solution for efficient logistics management in urban areas based on a unified scheme able to address both static and dynamic decision making at a company and network level. The proposed solution generates the most efficient urban distribution plan utilizing an evolutionary metaheuristic approach and a backpressure framework that provide competitive scheduling and routing decisions. An empirical study based on real data is conducted assessing the performance of the proposed advanced solution and the reported results of the evaluation experiments demonstrate its generality and robustness.     

REASON模型在航空维修事故调查中的应用

分析和运用Reason模型,从系统安全角度对一起典型航空维修事故案例,按照事件链因果逻辑展开调查分析,构建出事故致因分析模型。     

A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction

With the availability of large volumes of real-time traffic flow data along with traffic accident information, there is a renewed interest in the development of models for the real-time prediction of traffic accident risk. One challenge, however, is that the available data are usually complex, noisy, and even misleading. This raises the question of how to select the most important explanatory variables to achieve an acceptable level of accuracy for real-time traffic accident risk prediction. To address this, the present paper proposes a novel Frequent Pattern tree (FP tree) based variable selection method. The method works by first identifying all the frequent patterns in the traffic accident dataset. Next, for each frequent pattern, we introduce a new metric, herein referred to as the Relative Object Purity Ratio (ROPR). The ROPR is then used to calculate the importance score of each explanatory variable which in turn can be used for ranking and selecting the variables that contribute most to explaining the accident patterns. To demonstrate the advantages of the proposed variable selection method, the study develops two traffic accident risk prediction models, based on accident data collected on interstate highway I-64 in Virginia, namely a k-nearest neighbor model and a Bayesian network. Prior to model development, two variable selection methods are utilized: (1) the FP tree based method proposed in this paper; and (2) the random forest method, a widely used variable selection method, which is used as the base case for comparison. The results show that the FP tree based accident risk prediction models perform better than the random forest based models, regardless of the type of prediction models (i.e. k-nearest neighbor or Bayesian network), the settings of their parameters, and the types of datasets used for model training and testing. The best model found is a FP tree based Bayesian network model that can predict 61.11% of accidents while having a false alarm rate of 38.16%. These results compare very favorably with other accident prediction models reported in the literature.     

Reducing the energy consumption of heavy goods vehicles through the application of lightweight trailers: Fleet case studies

Reducing the empty weight of articulated heavy goods vehicle trailers is one avenue that needs to be explored in reducing the carbon footprint of the road freight industry as a whole. A statistical analysis of two heavy goods vehicle fleets operating in the United Kingdom has helped to identify double-deck trailers used in grocery haulage and ‘walking-floor’ trailers used in bulk haulage as two examples of trailers that can benefit significantly from lightweighting. Energy consumption of numerous articulated heavy goods vehicles is quantified through an idealised drive cycle analysis reflecting a long haul journey over a highway. This energy analysis allows for a mass energy performance index to be established. The analysis has shown that reducing the empty weight of trailers by 30% can cause reductions of up to 18% and 11% in mass energy performance index for double-deck trailers and ‘walking-floor’ trailers respectively. Using this approach, trailers that will benefit the most from weight reduction can be identified systematically, allowing for lightweighting strategies to be implemented more effectively. Strategies to reduce empty trailer weight and improve vehicle utilisation are also discussed.     

焦作市道路交通安全状况分析与改进

针对焦作市道路交通安全现状，采用道路安全综合指标K值法，依据事故统计数据确定事故频发路段并排序，对事故频发的时间分布规律和主客观影响因素展开探讨，并提出针对性的改进对策，研究结论可为提高焦作市道路交通安全水平提供参考。     

A traffic accident morphology diagnostic model based on a rough set decision tree

ABSTRACT

To build a traffic safety feature model and to quantify accident influences caused by some traffic violation behaviors of drivers, an accident diagnostic decision-making model is established. For the purpose of diagnosing accident morphologies, rough set theory is applied and the influence of traffic factors of different accident morphologies is quantified through calculating the degree of attribute importance, selecting core traffic factors and adopting a C4.5 decision tree algorithm. In the paper, road traffic accident data from 2008 to 2013 in Anhui Province are used. Typical rules are selected, targeted strategy proposals are put forward, and then, a scientific and reasonable diagnostic basis is provided for the diagnosis of traffic safety risks and the prediction of potential traffic accidents.     

依托“三互”深化海运危险货物谎报匿报治理工作实践

针对海事机构在海运危险货物谎报匿报查处中存在的缺乏源头信息来源、案件侦办过程复杂、单方面执法缺乏认可度等问题,大连海事局通过与海关部门建立“三互”合作机制,在信息交换、执法互助、联合研判和监管互认等方面进行合作,成立“三互联合查验工作室”,有效解决了上述问题,共同查处了多起谎报匿报危险货物案件,监管成效明显,具有较强的实践指导意义。文中就目前合作中存在的问题进行了分析,提出相应的管理建议,为“三互”合作机制长期运行提供可复制的实践经验。     

A scalable communication middleware for real-time data collection of dangerous goods vehicle activities

Recently, real-time monitoring of Dangerous Goods Transport has drawn a lot of attention, thanks to its capability to provide a better visibility on dynamically moving vehicles, particularly through a Web Mapping application. Yet, one of the challenges to be faced designing such a system is an effective architecture for real-time collection of telemetry and event data conveyed by the vehicle on-board system, such the Global Positioning System coordinates. In this paper, we have focused on optimizing the process for managing a large quantity of data transmitted via network sockets that use the Transmission Control Protocol. Then we prove the process efficiency through performance and scalability tests. The middleware is being implemented as a part of a project that aims to monitor the Italian petrochemical company Eni’s oil trucks shipment along Europe and USA territories.     

驾驶员心理应激与事故倾向性的相关研究

以240名职业驾驶员为对象研究其心理应激及其相关因素，结果表明：驾驶员的主要应激源是工作学习及家庭等有关问题。在社会支持上，主观支持分明显高于客观支持分。在心理控制感上，驾驶员明显趋于外控。SCL-90各因子分均值与全国常模相比，除躯体化、强迫、焦虑等项因子均值较高外，其他无显著差异。事故组与安全组驾驶员在应激性生活事件、中介变量、应激反应上，均存在显著差异。行车事故与应激性生活事件、积极应对方式呈显著正相关，与EPQ-N（神经质）、SCL-90诸因子呈非常显著负相关。     

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列检布局的好坏对于铁路货车运用工作的质量有重要影响，为此列检布局有效性的后评价成为车辆部门选择列检布局调整时机的重要依据。本文提出采用列检布局有效性指数作为后评价的指标，借助DEA方法建立了列检所有效性的分析模型，并利用列检所投入产出数据进行了横纵向比较。结果显示列检布局对货物列车正点率影响最大，而且采用本文方法获得的列检布局有效性评价结果与我国铁路列检布局的实际情况相吻合，评价效果良好。这说明本文提出的后评价方法是有效的，可供车辆部门进行列检布局后评价时使用。