基于货运车辆预警数据的高速公路危险驾驶热点路段识别

本文以货运车辆车联网数据中的车辆碰撞预警、疲劳预警和超速预警数据为研究对象,针对G4高速公路北京和河北境内道路上的三种预警热点路段进行识别。运用全局空间自相关性分析方法得出三种预警数据在道路空间中不是随机分布,而是集中在某一个或几个路段上;进一步采用局部空间相关性分析方法识别出三种不同预警类型下对应的热点路段。交通管理部门可根据识别出的预警热点路段提出针对性的管控措施,有助于缓解交通事故的发生,提高货运安全水平。     

A distributed VNS algorithm for optimizing dial-a-ride problems in large-scale scenarios

These days, transportation and logistic problems in large cities are demanding smarter transportation services that provide flexibility and adaptability. A possible solution to this arising problem is to compute the best routes for each new scenario. In this problem, known in the literature as the dial-a-ride problem, a number of passengers are transported between pickup and delivery locations trying to minimize the routing costs while respecting a set of prespecified constraints. This problem has been solved in the literature with several approaches from small to medium sized problems. However, few efforts have dealt with large scale problems very common in massive scenarios (big cities or highly-populated regions). In this study, a new distributed algorithm based on the partition of the requests space and the combination of the routes is presented and tested on a set of 24 different scenarios of a large-scale problem (up to 16,000 requests or 32,000 locations) in the city of San Francisco. The results show that, not only the distributed algorithm is able to solve large problem instances that the corresponding sequential algorithm is unable to solve in a reasonable time, but also to have an average improvement of 9% in the smaller problems. The results have been validated by means of statistical procedures proving that the distributed algorithm can be an effective way to solve high dimensional dial-a-ride problems.     

铁道车辆轮重测定仪检定装置的研制

为满足铁路货场铁路车辆轮重测定仪的现场检测与维修工作,研制了体积小、重量轻、结构简单的LJ-B型铁路车辆轮重测定仪检定装置。从研制目标、系统结构与原理、研制方案和技术难点及实际运用情况等方面,详细介绍LJ-B型铁路车辆轮重测定仪检定装置。     

呼准铁路建设工程投资控制研究

地方铁路建设是我国铁路路网的重要组成部分,随着地方铁路建设的快速发展,对建设项目的投资控制提出了更高的要求。此文在阐述地方铁路建设投资控制意义的基础上,对进行工程投资控制在项目决策阶段、设计阶段、施工阶段这几个主要阶段所采取的创新投资和管理模式、做好建设项目的融资、重视建设过程的管理与控制等措施及对策进行详细论述。并对通过学习掌握的先进经验、制定和完善的规章制度、引进和培养人才所取得的成果进行总结。     

基于深度优先搜索的铁路站场遍历算法研究

在列车运行控制系统中,及时准确地了解列车所在位置是非常重要的。卫星定位技术GNSS(GlobalNavigation Satellite System)已经逐渐应用到列车定位技术中,在对列车进行定位时需要对定位数据的可靠性进行验证。车站的定位数据可靠性验证工作建立在对车站站场进行多次遍历获得定位数据的基础上。利用计算机仿真技术模拟列车在站场内遍历能够对现场很难实现的遍历情况进行重复测试。本文对铁路站场与有向图的相似性进行研究,建立基于铁路站场结构的拓扑模型,在此基础上结合站场实际遍历的情况与图的深度优先遍历算法思路,提出一种基于链表数据结构的连续遍历站场的算法。结合实际站场图进行遍历算法仿真,较好地实现了连续遍历并获取较高的遍历效率。     

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内陆集装箱空箱站场是集装箱运输公司的内陆空箱调运网络的节点，其布局合理与否对集装箱运输公司降低成本、提高服务质量有重大影响．本文通过对传统的站场选址方法进行研究，根据内陆空箱站场选址的具体特征，建立了内陆集装箱空箱站场选址的混合整数规划模型，并对传统的求解该问题的禁忌搜索算法进行了改进．通过试验表明该改进算法具有较好的全局寻优能力，为集装箱空箱站场选址优化提供了一种新的思路和方法．     

进路搜索数据结构与算法研究

在深入研究站场信号平面图的基础上,对站场信号平面图建立有向无环图的模型,阐述一种在有向无环图中动态生成二叉树的进路搜索算法。验证算法的有效性和实用性。     

JD多车型配送车辆调度问题的模型及其禁忌搜索算法研究

在建立多车型配送车辆调度问题的数学模型的基础上,通过设计一种新的解的表示方法建立了求解该问题的禁忌搜索算法.实验计算证明,该算法不仅可以取得很好的计算结果,而且计算效率较高,收敛速度较快,计算结果也较稳定.     

METADATA EXPANDED SEMANTICALLY BASED RESOURCE SEARCH IN EDUCATION GRID

With the rapid increase of educational resources, how to search for necessary educational resource quickly is one of most important issues. Educational resources have the characters of distribution and heterogeneity, which are the same as the characters of Grid resources. Therefore, the technology of Grid resources search was adopted to implement the educational resources search. Motivated by the insufficiency of currently resources search methods based on metadata, a method of extracting semantic relations between words constituting metadata is proposed. We mainly focus on acquiring synonymy, hyponymy, hypernymy and parataxis relations. In our schema, we extract texts related to metadata that will be expanded from text spatial through text extraction templates. Next, metadata will be obtained through metadata extraction templates. Finally, we compute semantic similarity to eliminate false relations and construct a semantic expansion knowledge base. The proposed method in this paper has been applied on the education grid.     

Local Search Algorithm with Hybrid Neighborhood and Its Application to Job Shop Scheduling Problem

A new local search method with hybrid neighborhood for Job shop scheduling problem is developed. The proposed hybrid neighborhood is not only efficient in local search, but also can help overcome entrapments while search procedure get trapped at local optima and carry the search to areas of the feasible set with better prospect. New strategies used for breaking out of entrapments are presented and they are helpful for the procedure to improve local optima. A performance comparison of the proposed method with some best-performing algorithms on all 10-job, 10-machine benchmark problems and the other two problems generated by Fisher and Thompson ( ie. , FT6 and FT20) is made. The experiment results show the better optimal performance of the proposed algorithm.