Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems

Map-matching (MM) algorithms integrate positioning data from a Global Positioning System (or a number of other positioning sensors) with a spatial road map with the aim of identifying the road segment on which a user (or a vehicle) is travelling and the location on that segment. Amongst the family of MM algorithms consisting of geometric, topological, probabilistic and advanced, topological MM (tMM) algorithms are relatively simple, easy and quick, enabling them to be implemented in real-time. Therefore, a tMM algorithm is used in many navigation devices manufactured by industry. However, existing tMM algorithms have a number of limitations which affect their performance relative to advanced MM algorithms. This paper demonstrates that it is possible by addressing these issues to significantly improve the performance of a tMM algorithm. This paper describes the development of an enhanced weight-based tMM algorithm in which the weights are determined from real-world field data using an optimisation technique. Two new weights for turn-restriction at junctions and link connectivity are introduced to improve the performance of matching, especially at junctions. A new procedure is developed for the initial map-matching process. Two consistency checks are introduced to minimise mismatches. The enhanced map-matching algorithm was tested using field data from dense urban areas and suburban areas. The algorithm identified 96.8% and 95.93% of the links correctly for positioning data collected in urban areas of central London and Washington, DC, respectively. In case of suburban area, in the west of London, the algorithm succeeded with 96.71% correct link identification with a horizontal accuracy of 9.81 m (2σ). This is superior to most existing topological MM algorithms and has the potential to support the navigation modules of many Intelligent Transport System (ITS) services.     

大型集装箱码头陆域布局优化

为解决宁波大榭招商国际集装箱码头现有陆域布局散乱、土地利用不充分、堆场容量不足的问题,对码头陆域布局进行优化研究,以期集约化利用土地,增强码头的竞争力和发展潜力。基于调研和分析,梳理当前陆域布局中存在的主要问题;对泊位通过能力和堆场容量的计算分析,合理确定码头总体的运量规模,并以此指导陆域布局调整和优化工作。从土地利用和堆场容量两个方面进行评价,优化工作取得良好的效果。研究提出一种大型集装箱码头陆域布局优化的可行思路,可为类似项目研究提供借鉴。     

富春江船闸扩建改造工程创新设计与实践

针对碍航闸坝瓶颈问题,以富春江船闸为例,全面总结已建枢纽实施船闸扩建改造工程的主要关键技术与创新设计,包括保留原船闸并紧接新建一座大尺度船闸的总体布置、船闸结构设计、新老船闸衔接输水系统、老船闸加固改造、坝下施工围堰等,达到既满足通航安全要求,又避免对大坝安全、防洪安全、电站发电等不利影响,对解决我国众多枢纽船闸碍航问题具有典型示范作用。     

嘉兴港乍浦三期港区美福项目(码头工程)平面布置特点

根据工程实例,介绍石化码头平面布置特点。码头采用大、小作业平台相结合的布置形式,不仅满足大宗成品油装卸要求,还适用于散化品小批量、多货种的装卸。     

京杭运河的船闸设计研究

简要介绍京杭运河的建设情况,分析京杭运河船闸的总平面布置设计要点及船闸输水系统和主体机构的设计要点。     

哈大高铁影响下东北城市群交通网络空间结构特征研究

哈大高铁的运行缩短了东北城市群之间的相对距离,优化了东北城市群城市交通网络空间结构.通过建立向心性分形模型以及相关性分形模型,以交通距离代替空间的实际距离来研究城市群之间分形特征,通过城市间高速铁路时间距离和高速公路时间距离进行对比,计算高铁开通前后东北城市群交通空间聚集维数和空间关联维数,分析东北城市群交通网络空间结...     

基于 GIS-BIM 技术的山地城市长大通道选线方案及空间协同论证研究

山地城市长大通道具有地形条件复杂、里程长、控制因素多、工程投资大的特征。在国土空间规划“三区三线”划定落地的背景下,通道选线阶段应提前做好与“三区三线”的协同对接,集约利用土地等资源。针对山地城市长大通道的特征,分析线路设计方法与原则。以重庆中心城区—綦万快速通道工程为例,运用三维GIS技术建立基于大范围地形数据的基础模型,对路线方案进行三维可视化模拟分析,进行山地城市长大通道选线及空间协同论证实例分析。     

曲线连续刚构桥力学分析

随着我国交通事业的快速发展,曲线梁桥被广泛应用。文中对珠三角某立交匝道桥进行仿真模拟,对内外腹板应力和主应力及内外侧支座反力进行了比较。     

集装箱码头布局方案综合评价指标体系及指标权值确定方法研究

介绍了集装箱码头布局方案综合评价指标体系的建立方法,并提出了一种专家咨询法和模糊层次分析法相结合的评价指标权值确定方法,应用该方法评价集装箱码头布局方案,其结果更趋合理.     

Efficient missing data imputing for traffic flow by considering temporal and spatial dependence

The missing data problem remains as a difficulty in a diverse variety of transportation applications, e.g. traffic flow prediction and traffic pattern recognition. To solve this problem, numerous algorithms had been proposed in the last decade to impute the missed data. However, few existing studies had fully used the traffic flow information of neighboring detecting points to improve imputing performance. In this paper, probabilistic principle component analysis (PPCA) based imputing method, which had been proven to be one of the most effective imputing methods without using temporal or spatial dependence, is extended to utilize the information of multiple points. We systematically examine the potential benefits of multi-point data fusion and study the possible influence of measurement time lags. Tests indicate that the hidden temporal–spatial dependence is nonlinear and could be better retrieved by kernel probabilistic principle component analysis (KPPCA) based method rather than PPCA method. Comparison proves that imputing errors can be notably reduced, if temporal–spatial dependence has been appropriately considered.