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1.
In this paper, we propose a method for predicting the distributions of people’s trajectories on the road network throughout a city. Specifically, we predict the number of people who will move from one area to another, their probable trajectories, and the corresponding likelihoods of those trajectories in the near future, such as within an hour. With this prediction, we will identify the hot road segments where potential traffic jams might occur and reveal the formation of those traffic jams. Accurate predictions of human trajectories at a city level in real time is challenging due to the uncertainty of people’s spatial and temporal mobility patterns, the complexity of a city level’s road network, and the scale of the data. To address these challenges, this paper proposes a method which includes several major components: (1) a model for predicting movements between neighboring areas, which combines both latent and explicit features that may influence the movements; (2) different methods to estimate corresponding flow trajectory distributions in the road network; (3) a MapReduce-based distributed algorithm to simulate large-scale trajectory distributions under real-time constraints. We conducted two case studies with taxi data collected from Beijing and New York City and systematically evaluated our method.  相似文献   

2.
Mobile devices are becoming very popular in recent years, and large amounts of trajectory data are generated by these devices. Trajectories left behind cars, humans, birds or other objects are a new kind of data which can be very useful in the decision making process in several application domains. These data, however, are normally available as sample points, and therefore have very little or no semantics. The analysis and knowledge extraction from trajectory sample points is very difficult from the user's point of view, and there is an emerging need for new data models, manipulation techniques, and tools to extract meaningful patterns from these data. In this paper we propose a new methodology for knowledge discovery from trajectories. We propose through a semantic trajectory data mining query language several functionalities to select, preprocess, and transform trajectory sample points into semantic trajectories at higher abstraction levels, in order to allow the user to extract meaningful, understandable, and useful patterns from trajectories. We claim that meaningful patterns can only be extracted from trajectories if the background geographical information is considered. Therefore we build the proposed methodology considering both moving object data and geographic information. The proposed language has been implemented in a toolkit in order to provide a first software prototype for trajectory knowledge discovery.  相似文献   

3.
Spatial clustering can be used to discover hotspots in trajectory data. A trajectory clustering approach based on decision graph and data field is proposed as an effective method to select parameters for clustering, to determine the number of clusters, and to identify cluster centers. Synthetic data and real-world taxi trajectory data are utilized to demonstrate the effectiveness of the proposed approach. Results show that the proposed method can automatically determine the parameters for clustering as well as perform efficiently in trajectory clustering. Hotspots are identified and visualized during different times of a single day and at the same times on different days. The dynamic patterns of hotspots can be used to identify crowded areas and events, which are crucial for urban transportation planning and management.  相似文献   

4.
Social media applications are widely deployed in mobile platforms equipped with built-in GPS tracking devices, and these devices have led to an unprecedented collection of geolocated data (geo-tags). Geo-tags, along with place names, offer new opportunities to explore the trajectory and mobility patterns of social media users. However, trajectory data captured by social media are sparsely and irregularly spaced and therefore have varying degrees of resolution in both space and time. Previous studies on next location prediction are mostly applicable for detecting the upcoming location of a moving object using dense GPS trajectories where locations are recorded at regular time intervals (e.g., 1 minute). Additionally, point features are commonly used to represent the locations of visits, but using point features cannot capture the variability of human mobility. This article introduces a new methodology to predict an individual’s next location based on sparse footprints accumulated over a long time period using social networks, and uses polygons to represent the location corresponding to the physical activity area of individuals. First, the density-based spatial clustering algorithm is employed to discover the most representative activity zones that an individual frequently visits on a daily basis, and a polygon-based region is then derived for each representative activity zone. A sparse mobility Markov chain model considering both the movements and online behaviors of the social media user is trained and used to predict the user’s next location. Initial experiments with a group of Washington DC Twitter users demonstrate that the proposed methodology successfully discovers the activity regions and predicts the user’s next location with accuracy approaching 78.94%.  相似文献   

5.
This paper presents an original approach to dynamic anomalous behavior detection in individual trajectory using a recursive Bayesian filter. The anomalous pattern detection is of great interest for navigation, driver assistance systems, surveillance as well as crisis management. In this work, we focus on the GPS trajectories of automobiles finding where the driver’s behavior shows anomalies. Such anomalous behaviors can happen in many cases, especially when the driver encounters orientation problems, i.e., taking a wrong turn, performing a detour, or losing the way. First, three high-level features, i.e., turns and their density, detour factor, and route repetition are extracted from the given trajectory geometry, for which a long-term perspective is required to observe data sequences of a significant length instead of individual time stamps. We therefore employ high-order Markov chains with a ‘dynamic memory’ to model the trajectory integrating these long-term features. The Markov model is processed by a proposed recursive Bayesian filter to infer an optimal probability distribution of the potential anomalous driving behaviors dynamically over time. The filter performs unsupervised detection in single trajectories based on local features only. No training process is required to characterize the anomalous behaviors. By analyzing the detection results of individual trajectories, collective behaviors can be derived indicating traffic issues such as congestions and turn restrictions. Experiments are performed on volunteered geographic information (VGI) data, self-acquired trajectories, and open trajectory datasets to demonstrate the potential of the proposed approach.  相似文献   

6.
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7.
人类活动轨迹的分类、模式和应用研究综述   总被引：4，自引：3，他引：1  
各种传感器的应用与发展,如车载GPS、手机、公交卡、银行卡等,记录了人类的活动轨迹。这些海量的人类活动轨迹数据中蕴含着人类行为的时空分布模式。通过对这些轨迹的研究可以挖掘个体轨迹模式,理解人类动力学特征,进而为对轨迹预测、城市规划、交通监测等提供支持。因此,研究各类传感器记录的人类活动轨迹数据成为当前的研究热点。本文对人类活动轨迹的获取与表达方式进行剖析,并将人类的活动轨迹按照采样方式和驱动因素的不同分为基于时间间隔采样、基于位置采样和基于事件触发采样等3类轨迹数据。由于各类轨迹数据均由起始点、锚点和一般节点等构成,因而将轨迹模式挖掘的研究按照锚点、出行范围、形状模式、OD流模式、时间模式等进行组织,研究成果揭示人类活动轨迹在时间、空间的从聚模式、周期性等特点。在此基础上,将人类活动轨迹在城市研究中的应用,按照用户轨迹预测、城市动态景观、城市交通模拟与监控、城市功能单元识别以及城市中其他方面的研究应用进行系统综述,认为人类活动模式挖掘是城市规划、城市交通、公共安全等方面应用的基础。  相似文献   

8.
大多数旅游需求预测研究是基于目的地游客总数或消费总量开展的,尚未按不同的旅游目的或客源地细分进行预测.以天津欢乐谷主题公园为案例地,选择2014年第40周到2015年第26周为研究时段,利用通信大数据,提出了一种面向客源地的聚类-ARIMA组合预测模型.通过对不同客源地的时序数据进行聚类,选取各类别中的代表性客源地分别构建ARIMA预测模型.结果表明:对欢乐谷主题公园各客源地分别建模与聚类后通过6个代表客源地建模得到的结果一致;后者可以降低80%的预测成本.该方法具有较高的预测精度和较低的计算成本,适合面向客源地的短期旅游需求预测,可为旅游目的地提供更具针对性的旅游需求管理、分析与决策支撑.  相似文献   

9.
We present an extensible, generic, spatio-temporal trajectory simplification framework that modularises trajectory simplification into the stages of normalising, ranking, and reduction. We combine a range of ranking strategies and scoring heuristics – some from the literature and some new – into our framework modules and create a variety of spatio-temporal trajectory simplification methods. These trajectory simplification methods are experimented upon using real world and synthetic datasets, measuring running time, geometric displacement, and region-of-interest visitation. The results indicate that our proposed framework creates a number of efficient and effective spatio-temporal trajectory simplification methods.  相似文献   

10.
基于两维图论聚类方法,将主成分分析与两维图论聚类分析有机结合,建立区域旅游目的地竞争优势综合评判模型.首先,建立区域旅游业竞争优势的评价指标体系;接着,利用主成分分析方法对区域旅游竞争优势进行综合评估,并将其应用于浙江省旅游竞争优势评价;最后,以各个旅游目的地的主成分因子为分类对象,采用两维图论聚类法,对各地的旅游竞争优势进行空间聚类分析.研究结果表明,主成分分析与两维图论聚类分析有机结合研究区域旅游目的地的竞争优势是可行的,两维图论聚类能较好地反映区域旅游竞争优势和空间相关性,其结果将有助于决策者制定区域旅游发展战略.  相似文献   

11.
Geo-tagged travel photos on social networks often contain location data such as points of interest (POIs), and also users’ travel preferences. In this paper, we propose a hybrid ensemble learning method, BAyes-Knn, that predicts personalized tourist routes for travelers by mining their geographical preferences from these location-tagged data. Our method trains two types of base classifiers to jointly predict the next travel destination: (1) The K-nearest neighbor (KNN) classifier quantifies users’ location history, weather condition, temperature and seasonality and uses a feature-weighted distance model to predict a user’s personalized interests in an unvisited location. (2) A Bayes classifier introduces a smooth kernel function to estimate a-priori probabilities of features and then combines these probabilities to predict a user’s latent interests in a location. All the outcomes from these subclassifiers are merged into one final prediction result by using the Borda count voting method. We evaluated our method on geo-tagged Flickr photos and Beijing weather data collected from 1 January 2005 to 1 July 2016. The results demonstrated that our ensemble approach outperformed 12 other baseline models. In addition, the results showed that our framework has better prediction accuracy than do context-aware significant travel-sequence-patterns recommendations and frequent travel-sequence patterns.  相似文献   

12.
旅游地形象随时间变化的研究是旅游地形象研究的7类主题之一。文章基于旅游地形象构成模型,提出了从整体角度评估旅游前后旅游地形象随时间变化的研究思路,拓展了注重构成旅游地形象多维属性变化的旅游地形象评估方法。采用主成分分析、两配对样本t检验等定量方法,分别评估对比分析了构成旅游地形象的认知形象、情感形象以及整体形象三大组分在旅游前后的变化与差异程度,并以旅游前后安徽天堂寨风景区形象感知差异为例进行了实证研究。(1)验证了旅游地形象具有一定的稳定性。(2)从整体角度比较旅游前后旅游地形象的变化态势以及变化差异的显著程度是可行的。  相似文献   

13.
旅游地的发展演化过程研究大多采用Bulter 的生命周期理论路径, 少有文献从波动的视角理解和分析旅游地的发展演化过程。本文以黄山风景区为例, 采用经验模态分解方法(EMD)尝试从波动的视角分析景区客流波动特征, 并利用波动性特征对其发展进行组合预测(经验模态分解方法和最小二乘支持向量机方法的组合)。研究结果表明:黄山景区客流波动呈现出多种形态, 在增长趋势的基础上叠加了季节性波动、景区旅游周期波动和景区经济周期波动。其与最小二乘支持向量机组合预测模型能够对景区客流进行有效预测, 并且运算速度快, 预测精度有所提高;与生命周期曲线相比较更加直观、微观、准确, 并且能够进行较为准确的客流预报, 有助于景区规划管理和战略决策。  相似文献   

14.
城市道路数据的完整性和实时性是保障位置服务和规划导航路径的关键支撑。该文提出一种基于共享单车轨迹数据的新增自行车骑行道路自动检测和更新方法:首先,结合缓冲区方法和轨迹—路网几何特征检测增量轨迹;其次,基于分段—聚类—聚合策略提取更新路段,利用多特征融合密度聚类算法与最小外包矩形骨架线法提取增量道路中心线;最后,基于拓扑规则完成道路更新。以广州市共享单车轨迹为例,将该方法与传统栅格细化法进行实验对比,结果表明:该方法能有效更新道路网络,且在2 m和5 m精细尺度范围内提取的新增道路覆盖精度提升14%左右;在7 m尺度下精度达90%以上,在10 m尺度下精度达96%以上。  相似文献   

15.
旅游目的地居民的旅游亲和力从根本上看既是影响游客旅游体验质量的关键,也是目的地旅游发展的核心竞争力。以武汉市为例,通过对来过武汉旅游的游客进行访谈搜集资料,运用扎根理论研究方法对访谈数据进行处理与分析。研究发现:(1)旅游目的地居民的旅游亲和力主要由地方认同、生活态度、言语交际、公共秩序以及奉献精神5个方面构成;(2)地方认同和奉献精神可以对游客感知到的旅游亲和力的主要内容进行概括。  相似文献   

16.
ABSTRACT

The increasing popularity of Location-Based Social Networks (LBSNs) and the semantic enrichment of mobility data in several contexts in the last years has led to the generation of large volumes of trajectory data. In contrast to GPS-based trajectories, LBSN and context-aware trajectories are more complex data, having several semantic textual dimensions besides space and time, which may reveal interesting mobility patterns. For instance, people may visit different places or perform different activities depending on the weather conditions. These new semantically rich data, known as multiple-aspect trajectories, pose new challenges in trajectory classification, which is the problem that we address in this paper. Existing methods for trajectory classification cannot deal with the complexity of heterogeneous data dimensions or the sequential aspect that characterizes movement. In this paper we propose MARC, an approach based on attribute embedding and Recurrent Neural Networks (RNNs) for classifying multiple-aspect trajectories, that tackles all trajectory properties: space, time, semantics, and sequence. We highlight that MARC exhibits good performance especially when trajectories are described by several textual/categorical attributes. Experiments performed over four publicly available datasets considering the Trajectory-User Linking (TUL) problem show that MARC outperformed all competitors, with respect to accuracy, precision, recall, and F1-score.  相似文献   

17.
ABSTRACT

An increasing number of social media users are becoming used to disseminate activities through geotagged posts. The massive available geotagged posts enable collections of users’ footprints over time and offer effective opportunities for mobility prediction. Using geotagged posts for spatio-temporal prediction of future location, however, is challenging. Previous studies either focus on next-place prediction or rely on dense data sources such as GPS data. Introduced in this article is a novel method for future location prediction of individuals based on geotagged social media data. This method employs the hierarchical density-based clustering algorithm with adaptive parameter selection to identify the regions frequently visited by a social media user. A multi-feature weighted Bayesian model is then developed to forecast users’ spatio-temporal locations by combining multiple factors affecting human mobility patterns. Further, an updating strategy is designed to efficiently adjust, over time, the proposed model to the dynamics in users’ mobility patterns. Based on two real-life datasets, the proposed approach outperforms a state-of-the-art method in prediction accuracy by up to 5.34% and 3.30%. Tests show prediction reliability is high with quality predictions, but low in the identification of erroneous locations.  相似文献   

18.
卢岩君  秦承志  邱维理  朱阿兴 《地理科学》2011,31(12):1549-1554
针对基于少量典型样点土壤属性空间分布推测模型中的土壤属性参数敏感性问题,以坡位渐变信息结合典型土壤样点的加权平均模型为例,利用东北地形平缓小流域的土壤表层有机质含量样点集,使用阶乘设计、箱线图分析、扰动分析法和本文新设计的MR指数评价该模型的参数敏感性。结果表明,该模型中土壤属性参数敏感性较大,其大小与典型样点空间分布有关。敏感性主要由应用该模型时采用的坡位分类体系的不确定性引起。该文的分析方法可用于对基于少量典型样点的土壤属性空间分布推测模型进行参数敏感性综合分析。  相似文献   

19.
ABSTRACT

Regionalization attempts to group units into a few subsets to partition the entire area. The results represent the underlying spatial structure and facilitate decision-making. Massive amounts of trajectories produced in the urban space provide a new opportunity for regionalization from human mobility. This paper proposes and applies a novel regionalization method to cluster similar areal units and visualize the spatial structure by considering all trajectories in an area into a word embedding model. In this model, nodes in a trajectory are regarded as words in a sentence, and nodes can be clustered in the feature space. The result depicts the underlying socio-economic structure at multiple spatial scales. To our knowledge, this is the first regionalization method from trajectories with natural language processing technology. A case study of mobile phone trajectory data in Beijing is used to validate our method, and then we evaluate its performance by predicting the next location of an individual’s trajectory. The case study indicates that the method is fast, flexible and scalable to large trajectory datasets, and moreover, represents the structure of trajectory more effectively.  相似文献   

20.
X. Yao  L.G. Tham  F.C. Dai 《Geomorphology》2008,101(4):572-582
The Support Vector Machine (SVM) is an increasingly popular learning procedure based on statistical learning theory, and involves a training phase in which the model is trained by a training dataset of associated input and target output values. The trained model is then used to evaluate a separate set of testing data. There are two main ideas underlying the SVM for discriminant-type problems. The first is an optimum linear separating hyperplane that separates the data patterns. The second is the use of kernel functions to convert the original non-linear data patterns into the format that is linearly separable in a high-dimensional feature space. In this paper, an overview of the SVM, both one-class and two-class SVM methods, is first presented followed by its use in landslide susceptibility mapping. A study area was selected from the natural terrain of Hong Kong, and slope angle, slope aspect, elevation, profile curvature of slope, lithology, vegetation cover and topographic wetness index (TWI) were used as environmental parameters which influence the occurrence of landslides. One-class and two-class SVM models were trained and then used to map landslide susceptibility respectively. The resulting susceptibility maps obtained by the methods were compared to that obtained by the logistic regression (LR) method. It is concluded that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, which only requires failed cases, has an advantage over the other two methods as only “failed” case information is usually available in landslide susceptibility mapping.  相似文献   

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