Towards semantic‐aware multiple‐aspect trajectory similarity measuring

The large amount of semantically rich mobility data becoming available in the era of big data has led to a need for new trajectory similarity measures. In the context of multiple‐aspect trajectories, where mobility data are enriched with several semantic dimensions, current state‐of‐the‐art approaches present some limitations concerning the relationships between attributes and their semantics. Existing works are either too strict, requiring a match on all attributes, or too flexible, considering all attributes as independent. In this article we propose MUITAS, a novel similarity measure for a new type of trajectory data with heterogeneous semantic dimensions, which takes into account the semantic relationship between attributes, thus filling the gap of the current trajectory similarity methods. We evaluate MUITAS over two real datasets of multiple‐aspect social media and GPS trajectories. With precision at recall and clustering techniques, we show that MUITAS is the most robust measure for multiple‐aspect trajectories.     

载体轨迹停留信息提取的核密度法及其可视化

轨迹停留蕴含重要语义信息,其有效提取是开展轨迹Stop/Move模型分析的前提。本文首先依据核密度思想,通过累计邻域点时空贡献来定义轨迹点的停留指数,在此基础上设计了停留指数图,以图形方式直观表达轨迹点的时空聚集程度变化。进一步针对源于停留指数的潜在停留段,提出了一种基于潜在停留段时空临近关系的逐级合并算法,以自动发现和提取停留。试验表明,该算法兼顾停留识别的完整性和准确性,可以有效识别复杂多样的轨迹停留,即使面对噪声严重的轨迹,停留提取的正确率依然较高。     

Addressing location uncertainties in GPS‐based activity monitoring: A methodological framework

Location uncertainty has been a major barrier in information mining from location data. Although the development of electronic and telecommunication equipment has led to an increased amount and refined resolution of data about individuals’ spatio‐temporal trajectories, the potential of such data, especially in the context of environmental health studies, has not been fully realized due to the lack of methodology that addresses location uncertainties. This article describes a methodological framework for deriving information about people's continuous activities from individual‐collected Global Positioning System (GPS) data, which is vital for a variety of environmental health studies. This framework is composed of two major methods that address critical issues at different stages of GPS data processing: (1) a fuzzy classification method for distinguishing activity patterns; and (2) a scale‐adaptive method for refining activity locations and outdoor/indoor environments. Evaluation of this framework based on smartphone‐collected GPS data indicates that it is robust to location errors and is able to generate useful information about individuals’ life trajectories.     

An intersection‐based trajectory‐region movement study

In order to better understand the movement of an object with respect to a region, we propose a formal model of the evolving spatial relationships that transition between local topologies with respect to a trajectory and a region as well as develop a querying mechanism to analyze movement patterns. We summarize 12 types of local topologies built on trajectory‐region intersections, and derive their transition graph; then we capture and model evolving local topologies with two types of trajectory‐region strings, a movement string and a stop‐move string. The stop‐move string encodes the stop information further during a trajectory than the movement string. Such a string‐format expression of trajectory‐region movement, although conceptually simple, carries unprecedented information for effectively interpreting how trajectories move with respect to regions. We also design the corresponding Finite State Automations for a movement string as well as a stop‐move string, which are used not only to recognize the language of trajectory‐region strings, but also to deal effectively with trajectory‐region pattern queries. When annotated with the time information of stops and intersections, a trajectory‐region movement snapshot and its evolution during a time interval can be inferred, and even the relationships among trajectories with respect to the same region can be explored.     

Analytical queries on semantic trajectories using graph databases

This article studies the analysis of moving object data collected by location‐aware devices, such as GPS, using graph databases. Such raw trajectories can be transformed into so‐called semantic trajectories, which are sequences of stops that occur at “places of interest.” Trajectory data analysis can be enriched if spatial and non‐spatial contextual data associated with the moving objects are taken into account, and aggregation of trajectory data can reveal hidden patterns within such data. When trajectory data are stored in relational databases, there is an “impedance mismatch” between the representation and storage models. Graphs in which the nodes and edges are annotated with properties are gaining increasing interest to model a variety of networks. Therefore, this article proposes the use of graph databases (Neo4j in this case) to represent and store trajectory data, which can thus be analyzed at different aggregation levels using graph query languages (Cypher, for Neo4j). Through a real‐world public data case study, the article shows that trajectory queries are expressed more naturally on the graph‐based representation than over the relational alternative, and perform better in many typical cases.     

An innovative GPS trajectory data based model for geographic recommendation service

Geographic services based on GPS trajectory data, such as location prediction and recommender services, have received increasing attention because of their potential social and commercial benefits. In this study, a Geographic Service Recommender Model (GSRM) is proposed, which loosely comprises three essential steps. Firstly, location sequences are obtained through a clustering operation on GPS locations. To improve efficiency, a programming model with a distributed algorithm is employed to accelerate the clustering. Secondly, in order to mine spatial and temporal information from the cluster trajectory, an algorithm (MiningMP) is designed. Last but not least, the next possible location to which the user will travel is predicted. An integrated framework of GSRM could then be constructed and provide the appropriate geographic recommendation service by considering location sequences as well as other related semantic information. Experiments were conducted based on real GPS trajectories from Microsoft Research Asia (182 users within a period of five years). The experimental results clearly demonstrate that our proposed GSRM model is effective and efficient at predicting locations and can provide users with personalized smart recommendation services in the following possible position with excellent performance in scalability, adaptability, and quality of service.     

Path segmentation for movement trajectories with irregular sampling frequency using space‐time interpolation and density‐based spatial clustering

Path segmentation methods have been developed to distinguish stops and moves along movement trajectories. However, most studies do not focus on handling irregular sampling frequency of the movement data. This article proposes a four‐step method to handle various time intervals between two consecutive records, including parameter setting, space‐time interpolation, density‐based spatial clustering, and integrating the geographic context. The article uses GPS tracking data provided by HOURCAR, a non‐profit car‐sharing service in Minnesota, as a case study to demonstrate our method and present the results. We also implement the DB‐SMoT algorithm as a comparison. The results show that our four‐step method can handle various time intervals between consecutive records, group consecutive stops close to each other, and distinguish different types of stops and their inferred activities. These results can provide novel insights into car‐sharing behaviors such as trip purposes and activity scheduling.     

Discovering Spatial Patterns in Origin‐Destination Mobility Data

Mobility and spatial interaction data have become increasingly available due to the wide adoption of location‐aware technologies. Examples of mobility data include human daily activities, vehicle trajectories, and animal movements, among others. In this article we focus on a special type of mobility data, i.e. origin‐destination pairs, and present a new approach to the discovery and understanding of spatio‐temporal patterns in the movements. Specifically, to extract information from complex connections among a large number of point locations, the approach involves two steps: (1) spatial clustering of massive GPS points to recognize potentially meaningful places; and (2) extraction and mapping of the flow measures of clusters to understand the spatial distribution and temporal trends of movements. We present a case study with a large dataset of taxi trajectories in Shenzhen, China to demonstrate and evaluate the methodology. The contribution of the research is two‐fold. First, it presents a new methodology for detecting location patterns and spatial structures embedded in origin‐destination movements. Second, the approach is scalable to large data sets and can summarize massive data to facilitate pattern extraction and understanding.     

面向GeoGlobe的Oracle GeoRaster应用研究

GeoRaster是Oracle公司最新推出的用于栅格数据组织与管理的技术模块,其底层数据模型和高层开发接口非常适合于海量栅格数据的管理、应用和共享。本文讨论了如何在＂天地图＂核心支撑软件——GeoGlobe中接入Oracle GeoRaster栅格数据源,设计了面向GeoGlobe的库表结构和集成框架,针对影像黑边和内外码转换等问题,提出了相应的解决方案,实现了GeoGobe与Oracle GeoRaster的集成原型。结果表明：Oracle GeoRaster技术可以胜任GeoGlobe中栅格数据的组织与管理工作。     

Extracting commuter-specific destination hotspots from trip destination data – comparing the boro taxi service with Citi Bike in NYC

ABSTRACT

Taxi trajectories from urban environments allow inferring various information about the transport service qualities and commuter dynamics. It is possible to associate starting and end points of taxi trips with requirements of individual groups of people and even social inequalities. Previous research shows that due to service restrictions, boro taxis have typical customer destination locations on selected Saturdays: many drop-off clusters appear near the restricted zone, where it is not allowed to pick up customers and only few drop-off clusters appear at complicated crossing. Detected crossings imply recent infrastructural modifications. We want to follow up on these results and add one additional group of commuters: Citi Bike users. For selected Saturdays in June 2015, we want to compare the destinations of boro taxi and Citi Bike users. This is challenging due to manifold differences between active mobility and motorized road users, and, due to the fact that station-based bike sharing services are restricted to stations. Start and end points of trips, as well as the volumes in between rely on specific numbers of bike sharing stations. Therefore, we introduce a novel spatiotemporal assigning procedure for areas of influence around static bike sharing stations for extending available computational methods.     

A novel trajectory similarity measurement method based on node-sequence hierarchical digraph

Trajectory similarity measurement is a basic and vital task in trajectory data mining, which has attracted extensive research in the past decades. Recent works focused on the sequence and hierarchy property of trajectories to construct similarity measurements. However, these methods ignore the user information on the visiting locations, such as semantic and time distribution. In light of this, a novel trajectory similarity measurement based on Node-Sequence Hierarchical Digraph (NSHD) framework is proposed in this article. We first propose a Time-Weighted Stay Point Detection (TWSPD) method to extract real visiting locations of users more accurately. Then, the nodes of digraph are obtained by clustering users' stay points and the edges of digraph are sequence information that users move between these nodes. An Advanced Earth Mover's Distance (AEMD) is proposed to measure the node similarity between users, considering visiting time distribution and semantic information simultaneously. Both node and sequence similarities are used to calculate the similarity score to obtain the final trajectory similarity measurement. Experiments on Geolife and T-Drive datasets show that our proposed method offers competitive performance with mean reciprocal rank values reaching 96.01 and 81.26%, which outperforms related trajectory similarity measurements by more than 10 and 15%.     

Trajectory analysis at intersections for traffic rule identification

In this paper,we focus on trajectories at intersections regulated by various regulation types such as traffic lights,priority/yield signs,and right-of-way rules.We test some methods to detect and recognize movement patterns from GPS trajectories,in terms of their geometrical and spatio-temporal components.In particular,we first find out the main paths that vehicles follow at such locations.We then investigate the way that vehicles follow these geometric paths(how do they move along them).For these scopes,machine learning methods are used and the performance of some known methods for trajectory similarity measurement(DTW,Hausdorff,and Fréchet distance)and clustering(Affinity propagation and Agglomerative clustering)are compared based on clustering accuracy.Afterward,the movement behavior observed at six different intersections is analyzed by identifying certain movement patterns in the speed-and time-profiles of trajectories.We show that depending on the regulation type,different movement patterns are observed at intersections.This finding can be useful for intersection categorization according to traffic regulations.The practicality of automatically identifying traffic rules from GPS tracks is the enrichment of modern maps with additional navigation-related information(traffic signs,traffic lights,etc.).     

Personalized travel route recommendation using collaborative filtering based on GPS trajectories

Travelling is a critical component of daily life. With new technology, personalized travel route recommendations are possible and have become a new research area. A personalized travel route recommendation refers to plan an optimal travel route between two geographical locations, based on the road networks and users’ travel preferences. In this paper, we define users’ travel behaviours from their historical Global Positioning System (GPS) trajectories and propose two personalized travel route recommendation methods – collaborative travel route recommendation (CTRR) and an extended version of CTRR (CTRR+). Both methods consider users’ personal travel preferences based on their historical GPS trajectories. In this paper, we first estimate users’ travel behaviour frequencies by using collaborative filtering technique. A route with the maximum probability of a user’s travel behaviour is then generated based on the naïve Bayes model. The CTRR+ method improves the performances of CTRR by taking into account cold start users and integrating distance with the user travel behaviour probability. This paper also conducts some case studies based on a real GPS trajectory data set from Beijing, China. The experimental results show that the proposed CTRR and CTRR+ methods achieve better results for travel route recommendations compared with the shortest distance path method.     

A multi‐modal transportation data‐driven approach to identify urban functional zones: An exploration based on Hangzhou City,China

Recent urban studies have used human mobility data such as taxi trajectories and smartcard data as a complementary way to identify the social functions of land use. However, little work has been conducted to reveal how multi‐modal transportation data impact on this identification process. In our study, we propose a data‐driven approach that addresses the relationships between travel behavior and urban structure: first, multi‐modal transportation data are aggregated to extract explicit statistical features; then, topic modeling methods are applied to transform these explicit statistical features into latent semantic features; and finally, a classification method is used to identify functional zones with similar latent topic distributions. Two 10‐day‐long “big” datasets from the 2,370 bicycle stations of the public bicycle‐sharing system, and up to 9,992 taxi cabs within the core urban area of Hangzhou City, China, as well as point‐of‐interest data are tested to reveal the extent to which different travel modes contribute to the detection and understanding of urban land functions. Our results show that: (1) using latent semantic features delineated from the topic modeling process as the classification input outperforms approaches using explicit statistical features; (2) combining multi‐modal data visibly improves the accuracy and consistency of the identified functional zones; and (3) the proposed data‐driven approach is also capable of identifying mixed land use in the urban space. This work presents a novel attempt to uncover the hidden linkages between urban transportation patterns with urban land use and its functions.     

How to Apply the Geospatial Data Abstraction Library (GDAL) Properly to Parallel Geospatial Raster I/O?

Input/output (I/O) of geospatial raster data often becomes the bottleneck of parallel geospatial processing due to the large data size and diverse formats of raster data. The open‐source Geospatial Data Abstraction Library (GDAL), which has been widely used to access diverse formats of geospatial raster data, has been applied recently to parallel geospatial raster processing. This article first explores the efficiency and feasibility of parallel raster I/O using GDAL under three common ways of domain decomposition: row‐wise, column‐wise, and block‐wise. Experimental results show that parallel raster I/O using GDAL under column‐wise or block‐wise domain decomposition is highly inefficient and cannot achieve correct output, although GDAL performs well under row‐wise domain decomposition. The reasons for this problem with GDAL are then analyzed and a two‐phase I/O strategy is proposed, designed to overcome this problem. A data redistribution module based on the proposed I/O strategy is implemented for GDAL using a message‐passing‐interface (MPI) programming model. Experimental results show that the data redistribution module is effective.     

车辆GPS轨迹兴趣区域提取算法研究

车辆行驶轨迹是驾驶员主观意愿和路网客观约束综合作用的结果,从海量轨迹中挖掘兴趣区域可为车辆提供更深层次、更有效的位置服务。文中深入分析车辆GPS轨迹特征,在基于时间的聚类算法中引入路网约束,实现车辆GPS轨迹的兴趣点提取和噪点剔除,基于DBSCAN算法生成兴趣区域,采用Google Geocoding反向地理编码发掘并合并语义重复区域,在语义层次上实现兴趣区域提取。实验表明,该算法可在语义层次有效提取兴趣区域。     

Trajectories of Moving Objects on a Network: Detection of Similarities,Visualization of Relations,and Classification of Trajectories

Development in techniques of spatial data acquisition enables us to easily record the trajectories of moving objects. Movement of human beings, animals, and birds can be captured by GPS loggers. The obtained data are analyzed by visualization, clustering, and classification to detect patterns frequently or rarely found in trajectories. To extract a wider variety of patterns in analysis, this article proposes a new method for analyzing trajectories on a network space. The method first extracts primary routes as subparts of trajectories. The topological relations among primary routes and trajectories are visualized as both a map and a graph‐based diagram. They permit us to understand the spatial and topological relations among the primary routes and trajectories at both global and local scales. The graph‐based diagram also permits us to classify trajectories. The representativeness of primary routes is evaluated by two numerical measures. The method is applied to the analysis of daily travel behavior of one of the authors. Technical soundness of the method is discussed as well as empirical findings.     

Extracting spatial patterns in bicycle routes from crowdsourced data

Much is done nowadays to provide cyclists with safe and sustainable road infrastructure. Its development requires the investigation of road usage and interactions between traffic commuters. This article is focused on exploiting crowdsourced user‐generated data, namely GPS trajectories collected by cyclists and road network infrastructure generated by citizens, to extract and analyze spatial patterns and road‐type use of cyclists in urban environments. Since user‐generated data shows data‐deficiencies, we introduce tailored spatial data‐handling processes for which several algorithms are developed and implemented. These include data filtering and segmentation, map‐matching and spatial arrangement of GPS trajectories with the road network. A spatial analysis and a characterization of road‐type use are then carried out to investigate and identify specific spatial patterns of cycle routes. The proposed analysis was applied to the cities of Amsterdam (The Netherlands) and Osnabrück (Germany), proving its feasibility and reliability in mining road‐type use and extracting pattern information and preferences. This information can help users who wish to explore friendlier and more interesting cycle patterns, based on collective usage, as well as city planners and transportation experts wishing to pinpoint areas most in need of further development and planning.     

一种众源车载GPS轨迹大数据自适应滤选方法

基于同步高低精度GPS轨迹数据的空间特征和GPS误差分布原理,提出了一种众源GPS车载轨迹大数据自适应分割-滤选模型。该模型首先通过角度、距离约束将完整的车载GPS轨迹数据进行分割,以轨迹分割段作为基本滤选单元;然后通过对比轨迹分割段内GPS轨迹向量与其参考基线间的相似度,按照相似度与GPS定位精度之间的量化关系指导滤选。试验结果表明,该方法可以实现车载轨迹大数据按信息提取精度需求的滤选。     

Multidimensional Similarity Measuring for Semantic Trajectories

Most existing approaches aiming at measuring trajectory similarity are focused on two‐dimensional sequences of points, called raw trajectories. However, recent proposals have used background geographic information and social media data to enrich these trajectories with a semantic dimension, giving rise to the concept of semantic trajectories. Only a few works have proposed similarity measures for semantic trajectories or multidimensional sequences, having limitations such as predefined weight of the dimensions, sensitivity to noise, tolerance for gaps with different sizes, and the prevalence of the worst dimension similarity. In this article we propose MSM, a novel similarity measure for multidimensional sequences that overcomes the aforementioned limitations by considering and weighting the similarity in all dimensions. MSM is evaluated through an extensive experimental study that, based on a seed trajectory, creates sets of semantic trajectories with controlled transformations to introduce different kinds and levels of dissimilarity. For each set, we compute the similarity between the seed and the transformed trajectories, using different measures. The results showed that MSM was more robust and efficient than related approaches in the domain of semantic trajectories.