A probabilistic stop and move classifier for noisy GPS trajectories

Stop and move information can be used to uncover useful semantic patterns; therefore, annotating GPS trajectories as either stopping or moving is beneficial. However, the task of automatically discovering if the entity is stopping or moving is challenging due to the spatial noisiness of real-world GPS trajectories. Existing approaches classify each entry definitively as being either a stop or a move: hiding all indication that some classifications can be made with more certainty than others. Such an indication of the “goodness of classification” of each entry would allow the user to filter out certain stop classifications that appear too ambiguous for their use-case, which in a data-mining context may ultimately lead to less false patterns. In this work we propose such an approach that takes a noisy GPS trajectory as input and calculates the stop probability at each entry. Through the use of a minimum stop probability parameter our proposed approach allows the user to directly filter out any classified stops that are of an unacceptable probability for their application. Using several real-world and synthetic GPS trajectories (that we have made available) we compared the classification effectiveness, parameter sensitivity, and running time of our approach to two well-known existing approaches SMoT and CB-SMoT. Experimental results indicated the efficiency, effectiveness, and sampling rate robustness of our approach compared to the existing approaches. The results also demonstrated that the user can increase the minimum stop probability parameter to easily filter out low probability stop classifications—which equated to effectively reducing the number of false positive classifications in our ground truth experiments. Lastly, we proposed estimation heuristics for each our approaches’ parameters and empirically demonstrated the effectiveness of each heuristic using real-world trajectories. Specifically, the results revealed that even when all of the parameters were estimated the classification effectiveness of our approach was higher than existing approaches across a range of sampling rates.     

类自动车牌识别轨迹数据的伴随车辆组挖掘

自动车牌识别（ANPR）数据比私人全球定位系统（GPS）数据更易获得,且包含更有用的信息,但是相对成熟的针对GPS轨迹数据挖掘伴随车辆组方法并不适用于自动车牌识别数据,现有的少量自动车牌识别数据伴随车辆组挖掘算法存在重视轨迹相似而忽视时间因素的缺陷,因此提出一种基于轨迹特征的聚类方法挖掘伴随车辆组。针对自动车牌识别数据中采样点固定而采样时间不定的特点,通过轨迹中共现的次数判定两个对象构成伴随模式。该共现定义引入豪斯多夫距离,综合考虑轨迹的地点、方向和时间特征,旨在挖掘数据中采样点不同但采样点距离近且轨迹相似的伴随车辆组,以此提高伴随车辆组挖掘效率。实验结果表明,所提方法较现有方法更能有效挖掘伴随车辆组,识别非伴随模式数据,效率提升了近两倍。     

考虑时空特性的动态权重实时地图匹配算法

针对当前实时地图匹配算法难以同时保证匹配高准确性和高实时性的问题,提出一种基于动态权重的实时地图匹配改进算法。首先,算法考虑了相邻全球定位系统（GPS）轨迹点在时间、速度和方向上的约束关系,以及道路网拓扑结构,并基于时空特性分析,建立了距离权重、方位权重、方向权重和连通性权重组成的权重模型;然后,根据GPS轨迹点自身属性信息,建立了动态权重系数模型;最后,根据置信度水平选择最佳匹配路段。用三条总长36 km的重庆城市公交车行驶轨迹进行测试,结果显示：所提算法平均匹配正确率达到97.31%,单个轨迹点匹配平均延迟为17.9 ms。新算法匹配正确率和实时性较高,在Y形路口和平行路段的匹配效果上优于对比算法。     

基于速度的轨迹停留点识别算法

轨迹中的停留点识别是将空间轨迹转换为语义轨迹的关键步骤.当前轨迹停留点识别方法缺少对轨迹记录点时间连续性的考虑,导致识别出的停留点缺乏时间信息.同时,在轨迹点缺失的情况下,停留点信息也无法被准确识别.针对上述问题,本文提出一种基于速度的时空聚类方法,首先通过缺失轨迹的时空特性确定真实缺失子轨迹,并根据缺失轨迹的平均速度对其进行插值填充,再结合轨迹速度特征和时空特性识别轨迹中的停留点.实验采用GeoLife轨迹数据集对所提出的方法进行验证,结果表明,该算法能够有效地识别用户的停留点,并对轨迹中的干扰具有一定的鲁棒性.     

GPS coordinates estimation and camera calibration from solar shadows

In this paper, we discuss the issue of camera parameter estimation (intrinsic and extrinsic parameters), along with estimation of the geo-location of the camera by using only the shadow trajectories. By observing stationary objects over a period of time, it is shown that only six points on the trajectories formed by tracking the shadows of the objects are sufficient to estimate the horizon line of the ground plane. This line is used along with the extracted vertical vanishing point to calibrate the stationary camera. The method requires as few as two shadow casting objects in the scene and a set of six or more points on the shadow trajectories of these objects. Once camera intrinsic parameters are recovered, we present a novel application where one can accurately determine the geo-location of the camera up to a longitude ambiguity using only three points from these shadow trajectories without using any GPS or other special instruments. We consider possible cases where this ambiguity can also be removed if additional information is available. Our method does not require any knowledge of the date or the time when the images are taken, and recovers the date of acquisition directly from the images. We demonstrate the accuracy of our technique for both steps of calibration and geo-temporal localization using synthetic and real data.     

Analyzing movement predictability using human attributes and behavioral patterns

The ability to predict human mobility, i.e., transitions between a user's significant locations (the home, workplace, etc.) can be helpful in a wide range of applications, including targeted advertising, personalized mobile services, and transportation planning. Most studies on human mobility prediction have focused on the algorithmic perspective rather than on investigating human predictability. Human predictability has great significance, because it enables the creation of more robust mobility prediction models and the assignment of more accurate confidence scores to location predictions. In this study, we propose a novel method for detecting a user's stay points from millions of GPS samples. Then, after detecting these stay points, a long short-term memory (LSTM) neural network is used to predict future stay points. We explore the use of two types of stay point prediction models (a general model that is trained in advance and a personal model that is trained over time) and analyze the number of previous locations needed for accurate prediction. Our evaluation on two real-world datasets shows that by using our preprocessing approach, we can detect stay points from routine trajectories with higher accuracy than the methods commonly used in this domain, and that by utilizing various LSTM architectures instead of the traditional Markov models and advanced deep learning models, our method can predict human movement with high accuracy of more than 40% when using the Acc@1 measure and more than 59% when using the Acc@3 measure. We also demonstrate that the movement prediction accuracy varies for different user populations based on their trajectory characteristics and demographic attributes.     

Social itinerary recommendation from user-generated digital trails

Planning travel to unfamiliar regions is a difficult task for novice travelers. The burden can be eased if the resident of the area offers to help. In this paper, we propose a social itinerary recommendation by learning from multiple user-generated digital trails, such as GPS trajectories of residents and travel experts. In order to recommend satisfying itinerary to users, we present an itinerary model in terms of attributes extracted from user-generated GPS trajectories. On top of this itinerary model, we present a social itinerary recommendation framework to find and rank itinerary candidates. We evaluated the efficiency of our recommendation method against baseline algorithms with a large set of user-generated GPS trajectories collected from Beijing, China. First, systematically generated user queries are used to compare the recommendation performance in the algorithmic level. Second, a user study involving current residents of Beijing is conducted to compare user perception and satisfaction on the recommended itinerary. Third, we compare mobile-only approach with Mobile+Cloud architecture for practical mobile recommender deployment. Lastly, we discuss personalization and adaptation factors in social itinerary recommendation throughout the paper.     

基于R-Tree的高效异常轨迹检测算法

提出了异常轨迹检测算法,通过检测轨迹的局部异常程度来判断两条轨迹是否全局匹配,进而检测异常轨迹.算法要点如下:(1) 为了有效地表示轨迹的局部特征,以k个连续轨迹点作为基本比较单元,提出一种计算两个基本比较单元间不匹配程度的距离函数,并在此基础上定义了局部匹配、全局匹配和异常轨迹的概念;(2) 针对异常轨迹检测算法普遍存在计算代价高的不足,提出了一种基于R-Tree的异常轨迹检测算法,其优势在于利用R-Tree和轨迹间的距离特征矩阵找出所有可能匹配的基本比较单元对,然后再通过计算距离确定其是否局部匹配,从而消除大量不必要的距离计算.实验结果表明,该算法不仅具有很好的效率,而且检测出来的异常轨迹也具有实际意义.     

基于SparkStreaming的海量GPS数据实时地图匹配算法

浮动车GPS数据作为交通信息处理的基础,随着被监控车辆数量的高速增长,产生了海量GPS数据,对地图匹配提出了高挑战,为了解决传统匹配方法难以满足匹配效率和精度的不足,提出一种针对于海量GPS数据的实时并行地图匹配算法,能够同时保证较高匹配精度和运算效率。构建一种面向实时数据流的高效、准确实时地图匹配算法,首先通过引入速度、方向综合权重因子对依赖历史轨迹的离线地图匹配算法进行重构,进而引入Spark Streaming分布式计算框架,实现地图匹配算法的实时、并行运算,大幅提升实时地图匹配效率。实验结果表明,该算法在复杂路段的匹配准确率较常规拓扑匹配算法提高10%以上,整体匹配准确率达到95%以上;在匹配效率方面,较同等数量的单机服务器可提高效率4倍左右。实验结果表明,该算法在由11台机器组成的计算集群上实现8 000万个GPS数据点的实时地图匹配,证明了该算法可以完成城市地区的实时车辆匹配。     

DNA片段拼接中重复序列算法研究

本文主要研究DNA片断拼接中重复序列信息识别算法。包含大量重复信息的DNA序列,其重构是大规模DNA片段拼接所面临的实际困难之一。针对目前大多数拼接算法对于重复段的处理采用效率较低的反复迭代算法的特点,提出了基于k-mer子串的重复段分析方法,充分考虑了拼接中可能的分割点,设计与分析了识别重复序列并提高序列一致性的高效算法。     

基于深度学习的GPS轨迹去匿名研究

移动互联网和LBS技术的高速发展使得位置服务提供商可以轻松收集到大量用户位置轨迹数据,近期研究表明,深度学习方法能够从轨迹数据集中提取出用户身份标识等隐私信息.然而现有工作主要针对社交网络采集的签到点轨迹,针对GPS轨迹的去匿名研究则较为缺乏.因此,对基于深度学习的GPS轨迹去匿名技术开展研究.首先提出一种GPS轨迹数...     

基于盲目搜索的半自动栅格矢量转换方法的研究

从与传统方法不同的角度着手,以盲目搜索为指导思想,研究了一种新的半自动的将栅格数据转换为矢量数据的方法。与传统转换方法相比,该方法不需对栅格数据进行预处理,可以方便地处理最多到32位真彩色图像,特别适合交叉点多,拓扑复杂的大图像的处理。该方法转换效率高、速度快,并在试验和实践中取得了很好的效果。     

基于GPS轨迹的盲用导航算法与实现

利用在Windows CE嵌入式平台下接收到的全球定位系统(GPS)轨迹数据,提出了一种适用于盲人以及视障人士的室外GPS导航算法。通过对已知GPS轨迹数据的分析,判断出轨迹上的拐点位置以及拐弯方向,在实际导航时采用通过语音播报的方式向盲人提供实时导航信息。实验测试结果表明,该算法能够有效筛选出GPS轨迹中的拐点,并为用户播报有效实用的导航信息,指导其安全准确地到达目的地。     

Time-optimal parabolic interpolation with velocity,acceleration, and minimum-switch-time constraints

Time-optimal trajectories with bounded velocities and accelerations are known to be parabolic, i.e. piecewise constant in acceleration. An important characteristic of this class of trajectories is the distribution of the switch points – the time instants when the acceleration of any robot joint changes. When integrating parabolic trajectory generation into a motion planning pipeline, especially one that involves a shortcutting procedure, resulting trajectories usually contain a large number of switch points with a dense distribution. This high frequency acceleration switching intensifies joint motor wear as well as hampers the robot performance. In this paper, we propose an algorithm for planning parabolic trajectories subject to both physical bounds, i.e. joint velocity and acceleration limits, and the minimum-switch-time constraint. The latter constraint ensures that the time duration between any two consecutive switch points is always greater than a given minimum value. Analytic derivations are given, as well as comparisons with other methods to demonstrate the efficiency of our approach.     

3D Visual Odometry for Road Vehicles

This paper describes a method for estimating the vehicle global position in a network of roads by means of visual odometry. To do so, the ego-motion of the vehicle relative to the road is computed using a stereo-vision system mounted next to the rear view mirror of the car. Feature points are matched between pairs of frames and linked into 3D trajectories. Vehicle motion is estimated using the non-linear, photogrametric approach based on RANSAC. This iterative technique enables the formulation of a robust method that can ignore large numbers of outliers as encountered in real traffic scenes. The resulting method is defined as visual odometry and can be used in conjunction with other sensors, such as GPS, to produce accurate estimates of the vehicle global position. The obvious application of the method is to provide on-board driver assistance in navigation tasks, or to provide a means for autonomously navigating a vehicle. The method has been tested in real traffic conditions without using prior knowledge about the scene nor the vehicle motion. We provide examples of estimated vehicle trajectories using the proposed method and discuss the key issues for further improvement.     

基于路网的GPS轨迹在线压缩方法

在传统的GPS轨迹压缩中,其压缩的结果与原始轨迹差别较大,在压缩过程中不同程度的忽略了轨迹点的速度信息、方向信息以及轨迹的形状,在本文中,在保证压缩率的前提下将保存速度、方向、轨迹形状等GPS轨迹特征信息,作为重点研究的问题.本文算法基于路网信息、OW(Opening Window,开放窗口)算法、关键点法以及停留点法之上提出了一种能够保存GPS轨迹时空特性的在线压缩算法.实验结果表明,该压缩算法较现有的压缩算法在保证压缩率的前提下,在保留轨迹时空特性的基础上有所改进.     

Generation of road maps from trajectories collected with smartphone – A method based on Genetic Algorithm

Smartphones and automotive GPS have considerably boosted the use of digital road maps. For this reason, they must be updated regularly with accurate new data. The methods currently used to generate maps — photogrammetry and collaborative editing — have low frequency of update because they depend on manual intervention. By using an automated method it should be possible to improve map update speeds while maintaining similar level of accuracy. The literature presents some approaches for automatic road map creation using moving objects, but none of them is prepared for continuous update. Therefore, this work aims to propose a new automated method that uses trajectories provided by GPS receivers integrated in smartphones. It is assumed that the points that represent the center of the roads can be found through approximations provided by Genetic Algorithm. After that, these points are combined to generate the road map. However, the use of trajectories collected with smartphones provides some challenges, such as: elimination of data with bad accuracy, identification of the means of transport used and reduction of the volume of data processed. Thus, the objective of this work is to propose a method that cleans, analyzes and enriches data from smartphones to generate accurate road maps that can be continuously updated, using Genetic Algorithm. Tests indicate that the proposed method can generate maps with quality similar to the reference maps with less than 2 m of difference in average. Additionally, a comparison between the Fuzzy C-Means algorithm and the Genetic Algorithm shows that the later is a little slower but generates more accurate results.     

Crowdsourcing turning-restrictions from map-matched trajectories

The availability of GPS-enabled devices has generated massive amounts of GPS tracking data produced by vehicles traversing the road-network. While initially used for improving traffic estimation and routing, only recently has this data been used for map-construction efforts. This work focuses on the specific aspect of identifying turning restrictions in the underlying road-network graph. We propose a novel, efficient and straightforward method to deduce turning restrictions for OpenStreetMap data, by mining historic map-matched trajectories from an existing fleet-management service. Our extensive experimental evaluation and verification process utilizing online map-services, satellite imagery, street view and public map-data APIs proves the efficiency and reliability of the proposed method.     

基于浮动网格的路段检索方法

地图匹配是将车辆原始的GPS轨迹数据映射到实际道路网络上的过程,其中为GPS轨迹点检索候选路段是地图匹配的首要环节,然而不同的候选路段检索方式会直接影响地图匹配的准确性和效率.本文针对城市路网环境下的低频采样GPS轨迹数据,提出了一种基于浮动网格的路段检索方法.该方法利用GeoHash网格编码,采用浮动GeoHash网格的方式,为轨迹点检索候选路段.其次为了验证方法的可行性,本文通过隐马尔可夫模型,结合道路网络的拓扑结构以及轨迹的时空约束条件,采用增量的方式,利用维特比算法计算得到局部最优解.最后使用贪心策略,从已经得到的局部最优解中依次延伸得到全局最佳匹配路径.     

基于步行GPS轨迹的路网提取方法

准确提取和及时更新路网信息,对于道路规划和车辆导航等方面至关重要。目前,基于GPS轨迹的路网提取方法一般是从浮动车或出租车的GPS轨迹中挖掘城市主干路网。然而,现有方法忽略了小路的自动提取,它对于抗震救灾、小区导航或乡村游览等场合非常重要。因此,本文提出基于步行GPS轨迹的路网提取方法,分为数据预处理、道路中心线生成和路网精度评价3个部分。其中,先后采用轨迹点聚类、聚类点分割和中心线拟合等方法生成道路中心线。通过自行采集的步行GPS数据进行实验,结果表明,本文方法能够准确提取路网,覆盖率可达96.21%,而误检率仅3.26%;并且能够提取小路和更新路网。