利用GPS轨迹的转向级交通拥堵精细分析

目前,基于GPS轨迹探测城市交通状态的研究缺乏对不同行驶方向的交通拥堵精细感知。针对此问题,本文提出一种基于出租车GPS轨迹的转向级交通拥堵事件探测方法。该方法首先在分析出租车运营行为特征的基础上,采用特征聚类方法滤选出能够反映真实交通状态的有效轨迹段;然后基于滤选后轨迹分析当前道路交通运行状态,探测城市路网中轻度、中度、重度3种不同强度的交通拥堵事件;最后基于拥堵事件轨迹分析交叉口不同转向的拥堵时间、拥堵强度和拥堵距离等转向级精细交通拥堵状态。试验结果表明,本文方法不仅能有效探测路网中不同强度的拥堵事件,而且能实现交叉口转向级拥堵事件的精细分析。     

Application of GPS in Traffic Management Systems

Integrated Traffic Management Systems (ITMS) need reliable, accurate, and real-time data. Travel time, speed, and delay are three of the most important factors used in ITMS for monitoring, quantifying, and controlling congestion. GPS has recently become available for civil applications. Because it provides real-time spatial and time measurements, it has an increasing use in conducting different transportation studies. This article presents the application of GPS in collecting travel time, speed, and delay information of 64 major roads in the state of Delaware. A comparative statistical analysis was performed on data collected by GPS, with data collected simultaneously by the conventional method. The GPS data proved to be at least as accurate as the data collected by the conventional method, and it was 50% more efficient in terms of manpower. Moreover, the sample-size requirement was determined to maintain 95% confidence level throughout the controlled test. Benefiting from the Geographic Information System's dynamic segmentation tool, our travel time, delay, and speed information were integrated with other relevant traffic data. This was presented graphically on the Internet for public use. Statistical trend analysis for the data collected in 1997, 1998, 1999, and 2000 are also presented and applications on the overall ITMS are discussed. ? 2002 Wiley Periodicals, Inc.     

Multi-Scale Visualisation of Inbound and Outbound Traffic Delays in London

Abstract

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Traffic delays can be used to quantify traffic congestion on the road. However, traffic delay data itself cannot provide readily useful content about congestion directly. The aim of this research is to develop a multi-scale visualisation of traffic delay (excess travel time in minutes per kilometre) derived from automatic number plate reading (ANPR) data, to enable traffic commuting patterns to and from Central London to be understood and analysed further.     

利用轨迹大数据进行城市道路交叉口识别及结构提取

交叉口是城市交通路网生成、更新的重要组成部分。本文基于车辆时空轨迹大数据,提出了一种城市交叉口自动识别方法。该方法首先通过轨迹跟踪识别轨迹数据中包含的车辆转向点对;然后基于距离和角度的生长聚类方法进行转向点对的空间聚类,并采用基于局部点连通性的聚类方法识别交叉口;最后利用交叉口范围圆和转向点对提取城市各级别路网下的交叉口结构。以武汉市出租车轨迹大数据为例,对武汉市城区内189个交叉口进行了探测。试验结果表明,本文所提方法可以准确地从轨迹大数据中识别出城市交叉口及其结构。     

GPS/VRS 参考站网络的对流层误差建模技术研究

在虚拟参考站(VRS)技术中,参考站与流动站间的高程差异会引起对流层误差改正数中存在系统偏差影响,使对流层改正精度降低。在常规网络内插改正模型的基础上,借助先验对流层模型,提出了一种能自主修正高程偏差的距离相关对流层网络内插模型(MHDIM)。基于四川GPS连续运行参考站网络(Sichuan GPS Reference Station Network-SGRSN)实际测试数据的分析表明,对于地形变化较大的中长距离稀疏GPS/VRS参考站网络(如流动站与参考站间高程差异大于500m),应用MHDIM模型可使对流层延迟误差改正精度提高到2到3cm并实现cm级网络RTK定位结果,适用于GPS/VRS参考站网络cm级实时动态定位要求。     

利用浮动车大数据进行稀疏路段行程时间推断

针对利用实时浮动车数据估计路段行程时间时存在的数据稀疏性问题,提出了构建三层神经网络模型,以目标路段与邻接路段间的特征关系为输入、目标路段与邻接路段行程时间比值为输出,利用浮动车历史大数据获取路段之间的交通时空关联关系,继而用于路段行程时间的推断。采用武汉市2014年3~7月的浮动车GPS历史数据进行验证,得到的路段行程时间估计值的平均绝对百分比误差小于25%,证明了所提方法的有效性。     

基于地理格网的复杂路线车辆通行时间估算方法

车辆通行时间隐含了特定时隙的交通状况，准确地计算该时间在交通监测和路径规划中具有重要意义。现有研究通常利用车辆历史轨迹估算一定距离内选定路径的通行时间，然而当路径距离较长时，限于很难找到完整穿越指定路径的历史轨迹而无法对其通行时间进行准确估计；此外，海量历史轨迹在估计路径通行时间时会产生巨大的数据管理和计算压力。因此，本文引入地理格网，首先构建统一的时空索引，将路网及其历史轨迹分别划分为一系列落在地理格网单元（Cell）中的路段模式及轨迹段；然后利用一系列频繁共享轨迹在Cell中的停留时间计算车辆在当前路段模式的通行时间；最后通过一组历史时段相似路径模式的通行时间估算较长路线的车辆通行时间。通过对北京市10 000辆出租车一周的轨迹数据进行试验，验证了本文方法在处理海量历史轨迹数据上的有效性，以及在估算较长路径上车辆通行时间的优越性。     

Detecting vehicle traffic patterns in urban environments using taxi trajectory intersection points

Abstract

Detecting and describing movement of vehicles in established transportation infrastructures is an important task. It helps to predict periodical traffic patterns for optimizing traffic regulations and extending the functions of established transportation infrastructures. The detection of traffic patterns consists not only of analyses of arrangement patterns of multiple vehicle trajectories, but also of the inspection of the embedded geographical context. In this paper, we introduce a method for intersecting vehicle trajectories and extracting their intersection points for selected rush hours in urban environments. Those vehicle trajectory intersection points (TIP) are frequently visited locations within urban road networks and are subsequently formed into density-connected clusters, which are then represented as polygons. For representing temporal variations of the created polygons, we enrich these with vehicle trajectories of other times of the day and additional road network information. In a case study, we test our approach on massive taxi Floating Car Data (FCD) from Shanghai and road network data from the OpenStreetMap (OSM) project. The first test results show strong correlations with periodical traffic events in Shanghai. Based on these results, we reason out the usefulness of polygons representing frequently visited locations for analyses in urban planning and traffic engineering.     

基于网页旅游攻略的旅行线路规划

为帮助旅行者规划旅行线路,以旅游攻略为数据源,针对旅行线路规划问题中可选景点的动态性问题,根据传统车辆路径问题求解方法中两阶段法的先分组再定路线的策略,重点研究并设计改进了一种基于攻略中景点出现频率的先分组再定路线的启发式旅行线路规划策略用于自动规划旅游行程。试验结果表明,使用本文所提出的方法生成的旅游线路,时间分配更加合理,游览的景点间路程更短,线路的游览时间与景点间距离的费效比更高,对于游客规划旅行线路有着很大的现实意义。     

New Perspectives on the Use of GPS and GIS to Support a Highway Performance Study

In recent decades, rapid growth of travel volume has resulted in a significant increase in traffic congestion, accidents, environmental pollution and energy consumption. Accurate traffic data are drastically needed for effective evaluation of traffic systems in order to alleviate the impacts of increasing travel volume of the quality of life and economic development of urban areas. This article provides a discussion on a data acquisition methodology for highway traffic pattern recognition and congestion analysis by integrating data from the global positioning system (GPS) with a geographic information system (GIS). The GPS technology is a powerful tool in capturing continuous positioning and timing information, whereas the GIS is capable of storing, managing, manipulating, analyzing and displaying the acquired spatial information. Compared to previous studies, the effective integration of the two technologies allows for traffic analysis to be conducted at a finer resolution. The proposed method is illustrated with a case study on multiple major highway segments in Columbus, Ohio.     

浮动车行程速度探索性分析

城市交通系统是一个有人参与的、动态的和开放的复杂系统。传统的均值、方差等统计方法已难以直观地表达和发现城市交通流的潜在特征。而目前基于确定的交通流数学模型虽然能定量地揭示其非线性动力学特征,但存在着表达式复杂却精确度不够且灵活性欠缺和通用性不强的问题。本文探讨了城市行车速度的探索性分析方法的可行性,并以北京市四环内路段行车速度为例进行试验,结果表明,浮动车行车速度的探索性分析方法是有效的和实用的,可为交通流的特征分析特别是实时交通异常检测的研究提供参考思路。     

Ionospheric effects on differential GPS applications during auroral substorm activity

The use of the Global Positioning System (GPS) technology has become increasingly incorporated into airborne remote sensing applications over the past decade. While GPS positioning results may prove adequate for several applications at present, users should expect to experience degraded positioning accuracies over the next few years due to auroral substorm activity. Such degraded accuracies will arise from increased spatial decorrelation of ionosphere range delay errors in differential GPS applications, as the ionospheric activity increases during solar maximum. In this paper, the spatial decorrelation of ionospheric range delay is estimated during a substorm event and compared with “quiet” time values. Positional errors (in both vertical and horizontal measurements) in the range 60–80 cm RMSE were observed during a 1997 substorm event that is representative of the activity anticipated at solar maximum around the year 2000.     

城市公交乘客下车站点推算方法和有效性评价

特大型城市的公交车上普遍安装了自动售票（auto fare collection,AFC）系统和车载GPS导航定位设备,记录了乘客出行与行车轨迹、时间等数据,连续运行的公交车和众多的出行乘客形成了城市公共交通运行和出行大数据。如何高效、准确地从公交大数据中识别公交乘客下车站点,对于提高交通运行效率、科学布局组织交通具有重要的意义和作用。基于深圳市公交车AFC和GPS数据,利用时间匹配和基于带噪声空间密度聚类的方法判别上车站点;在仅有的公交数据基础上,通过分析乘客出行行为,根据乘客多天的出行以及各站点的上车频率,利用乘客高频站点和下游站点吸引权,提出一种推算方法,实现乘客下车站点的推断。算法模型的检验和实例分析表明该方法的有效性。     

Combining Geo‐SOM and Hierarchical Clustering to Explore Geospatial Data

Geo‐SOM is a useful geovisualization technique for revealing patterns in spatial data, but is ineffective in supporting interactive exploration of patterns hidden in different Geo‐SOM sizes. Based on the divide and group principle in geovisualization, the article proposes a new methodology that combines Geo‐SOM and hierarchical clustering to tackle this problem. Geo‐SOM was used to “divide” the dataset into several homogeneous subsets; hierarchical clustering was then used to “group” neighboring homogeneous subsets for pattern exploration in different levels of granularity, thus permitting exploration of patterns at multiple scales. An artificial dataset was used for validating the method's effectiveness. As a case study, the rush hour motorcycle flow data in Taipei City, Taiwan were analyzed. Compared with the best result generated solely by Geo‐SOM, the proposed method performed better in capturing the homogeneous zones in the artificial dataset. For the case study, the proposed method discovered six clusters with unique data and spatial patterns at different levels of granularity, while the original Geo‐SOM only identified two. Among the four hierarchical clustering methods, Ward's clustering performed the best in pattern discovery. The results demonstrated the effectiveness of the approach in visually and interactively exploring data and spatial patterns in geospatial data.     

一种城市公共自行车接驳地铁出行的时空可视化分析方法

公共自行车是城市轨道交通的重要接驳方式。为了更加清晰、直观地展示公共自行车接驳地铁出行的时空特征，本文提出了一种公共自行车租赁点接驳地铁出行客流量的时空可视化分析方法。该方法首先将地铁客流与公共自行车的IC卡数据进行预处理与时序分析，初步分析了两者之间的时序相似性；通过对地铁客流的时间聚类进行分析，得到了3种类型的出行日；然后分别对各类型出行日的相关性计算结果进行了特征可视化与空间信息可视化，实现对公共自行车接驳地铁出行时空特征的综合分析；最后运用该方法以北京市西城区宣武门地铁站为例，对该站合理范围内的公共自行车接驳地铁出行情况进行了时空可视化分析。结果表明，对地铁客流与公共自行车IC卡数据的融合可视化分析可更加全面、动态地掌握城市公共自行车接驳地铁出行的时空分布情况，该方法可为城市轨道交通站点处公共自行车租赁点的规划及建设提供量化依据。     

Online monitoring of local taxi travel momentum and congestion effects using projections of taxi GPS-based vector fields

Ubiquitous taxi trajectory data has made it possible to apply it to different types of travel analysis. Of interest is the need to allow someone to monitor travel momentum and associated congestion in any location in space in real time. However, despite an abundant literature in taxi data visualization and its applicability to travel analysis, no easy method exists. To measure taxi travel momentum at a location, current methods require filtering taxi trajectories that stop at a location at a particular time range, which is computationally expensive. We propose an alternative, computationally cheaper way based on preprocessing vector fields from the trajectories. Algorithms are formalized for generating vector kernel density to estimate a travel-model-free vector field-based representation of travel momentum in an urban space. The algorithms are shared online as an open source GIS 3D extension called VectorKD. Using 17 million daily taxi GPS points within Beijing over a 4-day period, we demonstrate how to generate in real time a series of projections from a continuously updated vector field of taxi travel momentum to query a point of interest anywhere in a city, such as the CBD or the airport. This method allows a policy-maker to automatically identify temporal net influxes of travel demand to a location. The proposed methodology is shown to be over twenty times faster than a conventional selection query of trajectories. We also demonstrate, using taxi data entering the Beijing Capital International Airport and the CBD, how we can quantify in nearly real time the occurrence and magnitude of inbound or outbound queueing and congestion periods due to taxis cruising or waiting for passengers, all without having to fit any mathematical queueing model to the data.     

无人机大比例尺免像控关键技术探讨

针对无人机摄影飞行速度不稳定导致影像获取时刻曝光误差不统一问题,提出无人机大比例尺免像控方法．首先,探讨分析影像镜头畸变误差模型以及曝光延迟带来无人机曝光未同步问题;然后,对传统的GPS辅助光束法平差模型中引入曝光延迟模型,提出引入曝光延迟的GPS辅助光束法平差方法;最后,以工程实例验证该方法的有效性,实验结果表明本文方法可以在无像控点参与情况下满足1∶500的大比例测图精度要求.      

LIDAR Validation Using GIS: A Case Study Comparison between Two LIDAR Collection Methods

Abstract

In the summer of 2000, the Annapolis Valley of Nova Scotia, Canada was selected for a high‐resolution elevation survey utilizing LIDAR (Light Detection And Ranging). Two different LIDAR systems were used to acquire data for the area. The vertical accuracy specification for the survey called for heights to be within an average of 15 cm of measured GPS heights and 95% of the data to be within 30 cm. Prior to the application of these data to geoscientific problems, extensive validation procedures were employed. High precision GPS and traditional surveys were conducted to collect height validation checkpoints. Two validation methods were developed in a GIS environment that involved comparing the checkpoints to the original LIDAR points and to an interpolated “bald earth” DEM. A systematic height error between flight lines for one of the LIDAR methods was detected that related to the calibration procedures used in the survey. This study highlights the differences between laser systems, calibration and deployment methodologies and emphasizes the necessity for independent validation data.     

面向交通信息与改道建议实时发布的数据收集建模研究——以上海市延安西路为例

随着经济的快速发展,城市居民出行量迅猛上升。车辆安装GPS导航系统日渐成为趋势,在交通运行方面显示出一定优势。该项目旨在通过感应系统收集交通拥堵状况信息进行量化分析,根据分析结果对车辆路线选择提出建议,以提高交通运行效率。本项目选择上海延安西路的一个区段为例,通过连续一周各个时段的GPS定点道路流量数据收集,获得实时交通信息样例。项目对数据进行时空聚类分析,判别主路和辅路中各个路口的交通拥堵时段,为可否换道省时的时空决策提供依据。案例表明,本研究采用的集成3S技术的时空数据分析和决策方法,可为上海交通状况优化和管理效率提高提供有价值的参考意见。     

Estimating traffic speed and speeding using passively collected big mobility data and a distributed computing framework

With the increasing availability of location-aware devices, passively collected big GPS trajectory data offer new opportunities for analyzing human mobility. Processing big GPS trajectory data, especially extracting information from billions of trajectory points and assigning information to corresponding road segments in road networks, is a challenging but necessary task for researchers to take full advantage of big data. In this research, we propose an Apache Spark and Sedona-based computing framework that is capable of estimating traffic speeds for statewide road networks from GPS trajectory data. Taking advantage of spatial resilient distributed datasets supported by Sedona, the framework provides high computing efficiency while using affordable computing resources for map matching and waypoint gap filling. Using a mobility dataset of 126 million trajectory points collected in California, and a road network inclusive of all road types, we computed hourly speed estimates for approximately 600,000 segments across the state. Comparing speed estimates for freeway segments with speed limits, our speed estimates showed that speeding on freeways occurred mostly during the nighttime, while analysis of travel on residential roads showed that speeds were relatively stable over the 24-h period.