scholarly journals Extracting Stops from Spatio-Temporal Trajectories within Dynamic Contextual Features

2021 ◽  
Vol 13 (2) ◽  
pp. 690
Author(s):  
Tao Wu ◽  
Huiqing Shen ◽  
Jianxin Qin ◽  
Longgang Xiang
Keyword(s):  
Moving Objects ◽  
Major Effect ◽  
Location Based Services ◽  
Trajectory Data ◽  
The Road ◽  
Contextual Features ◽  
Novel Approach ◽  
Gps Trajectories ◽  
Time Distance ◽  
Spatio Temporal

Identifying stops from GPS trajectories is one of the main concerns in the study of moving objects and has a major effect on a wide variety of location-based services and applications. Although the spatial and non-spatial characteristics of trajectories have been widely investigated for the identification of stops, few studies have concentrated on the impacts of the contextual features, which are also connected to the road network and nearby Points of Interest (POIs). In order to obtain more precise stop information from moving objects, this paper proposes and implements a novel approach that represents a spatio-temproal dynamics relationship between stopping behaviors and geospatial elements to detect stops. The relationship between the candidate stops based on the standard time–distance threshold approach and the surrounding environmental elements are integrated in a complex way (the mobility context cube) to extract stop features and precisely derive stops using the classifier classification. The methodology presented is designed to reduce the error rate of detection of stops in the work of trajectory data mining. It turns out that 26 features can contribute to recognizing stop behaviors from trajectory data. Additionally, experiments on a real-world trajectory dataset further demonstrate the effectiveness of the proposed approach in improving the accuracy of identifying stops from trajectories.

scholarly journals Modelling imperfect knowledge via location semantics for realistic privacy risks estimation in trajectory data

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Stefano Bennati ◽  
Aleksandra Kovacevic
Keyword(s):  
Service Providers ◽  
Flow Analysis ◽  
Location Based Services ◽  
Location Based Service ◽  
Mobility Patterns ◽  
Trajectory Data ◽  
Imperfect Knowledge ◽  
Spatio Temporal ◽  
Definition Of ◽  
Privacy Risks

AbstractMobility patterns of vehicles and people provide powerful data sources for location-based services such as fleet optimization and traffic flow analysis. Location-based service providers must balance the value they extract from trajectory data with protecting the privacy of the individuals behind those trajectories. Reaching this goal requires measuring accurately the values of utility and privacy. Current measurement approaches assume adversaries with perfect knowledge, thus overestimate the privacy risk. To address this issue, we introduce a model of an adversary with imperfect knowledge about the target. The model is based on equivalence areas, spatio-temporal regions with a semantic meaning, e.g. the target’s home, whose size and accuracy determine the skill of the adversary. We then derive the standard privacy metrics of k-anonymity, l-diversity and t-closeness from the definition of equivalence areas. These metrics can be computed on any dataset, irrespective of whether and what kind of anonymization has been applied to it. This work is of high relevance to all service providers acting as processors of trajectory data who want to manage privacy risks and optimize the privacy vs. utility trade-off of their services.

A Novel Approach for Mining Time and Space Proximity-based Frequent Sequential Patterns from Trajectory Data

2020 ◽  
Vol 19 (04) ◽  
pp. 2050040
Author(s):  
Rayanoothala Praneetha Sree ◽  
D. V. L. N. Somayajulu ◽  
S. Ravichandra
Keyword(s):  
Moving Objects ◽  
Sequential Patterns ◽  
Computational Time ◽  
Manhattan Distance ◽  
Trajectory Data ◽  
Space Partitioning ◽  
Novel Approach ◽  
Time Stamps ◽  
Text Length ◽  
Hidden Patterns

Trajectory Data have been considered as a treasure for various hidden patterns which provide deeper understanding of the underlying moving objects. Several studies are focused to extract repetitive, frequent and group patterns. Conventional algorithms defined for Sequential Patterns Mining problems are not directly applicable for trajectory data. Space Partitioning strategies were proposed to capture space proximity first and then time proximity to discover the knowledge in the data. Our proposal addresses time proximity first by identifying trajectories which meet at a minimum of [Formula: see text] time stamps in sequence. A novel tree structure is proposed to ease the process. Our method investigates space proximity using Mahalanobis distance (MD). We have used the Manhattan distance to form prior knowledge that helps the supervised learning-based MD to derive the clusters of trajectories along the true spreads of the objects. With the help of minsup threshold, clusters of frequent trajectories are found and then in sequence they form [Formula: see text] length Sequential Patterns. Illustrative examples are provided to compare the MD metric with Euclidean distance metric, Synthetic dataset is generated and results are presented considering the various parameters such as number of objects, minsup, [Formula: see text] value, number of hops in any trajectory and computational time. Experiments are done on available real-time dataset, taxi dataset, too. Sequential Patterns are proved to be worthy of knowledge to understand dynamics of the moving objects and to recommend the movements in constrained networks.

scholarly journals Road Congestion Detection Based on Trajectory Stay-Place Clustering

2019 ◽  
Vol 8 (6) ◽  
pp. 264 ◽  
Author(s):  
Qingying Yu ◽  
Yonglong Luo ◽  
Chuanming Chen ◽  
Xiaoyao Zheng
Keyword(s):  
Traffic Management ◽  
Moving Objects ◽  
Real Life ◽  
Equivalence Classes ◽  
Trajectory Data ◽  
Road Congestion ◽  
Trajectory Mining ◽  
Congestion Detection ◽  
Rational Planning ◽  
Novel Approach

The results of road congestion detection can be used for the rational planning of travel routes and as guidance for traffic management. The trajectory data of moving objects can record their positions at each moment and reflect their moving features. Utilizing trajectory mining technology to effectively identify road congestion locations is of great importance and has practical value in the fields of traffic and urban planning. This paper addresses the issue by proposing a novel approach to detect road congestion locations based on trajectory stay-place clustering. First, this approach estimates the speed status of each time-stamped location in each trajectory. Then, it extracts the stay places of the trajectory, each of which is denoted as a seven-tuple containing information such as starting and ending time, central coordinate, average direction difference, and so on. Third, the time-stamped locations included in stay places are partitioned into different stay-place equivalence classes according to the timestamps. Finally, stay places in each equivalence class are clustered to mine the congestion locations of multiple trajectories at a certain period of time. Visual representation and experimental results on real-life cab trajectory datasets show that the proposed approach is suitable for the detection of congestion locations at different timestamps.

Moving Beyond Traditional Decision Support Systems

2017 ◽  
Vol 8 (2) ◽  
pp. 74-87
Author(s):  
Noura Azaiez ◽  
Jalel Akaichi ◽  
Jeffrey Hsu
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Moving Objects ◽  
Temporal Data ◽  
Trajectory Data ◽  
Reduced Costs ◽  
Spatio Temporal ◽  
Traditional Approaches ◽  
Dairy Milk ◽  
Made In

Integrating the concept of mobility into the professional and organizational realm offers the possibility of reducing geographical disparities related to organization services. The advances made in technology, geographic information systems and pervasive systems equipped with global positioning (GPS) technologies have been able to bring about an evolution from classic data approaches towards the modeling of trajectory data resulting from moving activities of moving objects. As such, trajectory data needs first to be loaded into a Data Warehouse for analysis purposes. However, the traditional approaches used are poorly suited to handle spatio-temporal data features and also the decision making tasks related to mobility issues. Because of this mismatch, the authors propose to move beyond traditional approaches and propose a repository that is able to analyse trajectories of moving objects. Improving decision making and extracting pertinent knowledge with reduced costs and time expended are the main goals of this revised analysis approach. Thus, the authors propose an approach in which they employ the Bottom-up approach to modeling a Decision Support System which is designed to support Trajectory Data. As an example to illustrate this approach, the authors use a creamery and dairy milk mobile cistern application to demonstrate the effectiveness of their approach.

scholarly journals Privacy preserving semantic trajectory data publishing for mobile location-based services

2019 ◽  
Vol 26 (8) ◽  
pp. 5551-5560 ◽  
Author(s):  
Rong Tan ◽  
Yuan Tao ◽  
Wen Si ◽  
Yuan-Yuan Zhang
Keyword(s):  
Moving Objects ◽  
Information Source ◽  
Location Based Services ◽  
Data Publishing ◽  
Trajectory Data ◽  
Similarity Comparison ◽  
Mobile Location ◽  
Motion Modes ◽  
Network Topologies ◽  
Rich Information

Abstract The development of wireless technologies and the popularity of mobile devices is responsible for generating large amounts of trajectory data for moving objects. Trajectory datasets have spatiotemporal features and are a rich information source. The mining of trajectory data can reveal interesting patterns of human activities and behaviors. However, trajectory data can also be exploited to disclose users’ privacy information, e.g., the places they live and work, which could be abused by a malicious user. Therefore, it is very important to protect the users’ privacy before publishing any trajectory data. While most previous research on this subject has only considered the privacy protection of stay points, this paper distinguishes itself by modeling and processing semantic trajectories, which not only contain spatiotemporal data but also involve POI information and the users’ motion modes such as walking, running, driving, etc. Accordingly, in this research, semantic trajectory anonymizing based on the k-anonymity model is proposed that can form sensitive areas that contain k − 1 POI points that are similar to the sensitive points. Then, trajectory ambiguity is executed based on the motion modes, road network topologies and road weights in the sensitive area. Finally, a similarity comparison is performed to obtain the recordable and releasable anonymity trajectory sets. Experimental results show that this method performs efficiently and provides high privacy levels.

Research on Modeling and Indexing of Trajectories of Moving Objects in Road Networks

2013 ◽  
Vol 756-759 ◽  
pp. 1234-1239
Author(s):  
Yan Ling Zheng
Keyword(s):  
Road Network ◽  
Moving Objects ◽  
Road Networks ◽  
Experimental Results ◽  
Index Structure ◽  
Index Structures ◽  
The Road ◽  
The Past ◽  
Spatio Temporal ◽  
Temporal Trajectory

Proposed a new index structure, named MG2R*, can efficiently store and retrieve the past, present and future positions of network-constrained moving objects. It is a two-tier structure. The upper is a MultiGrid-R*-Tree (MGRT for short) that is used to index the road network. The lower is a group of independent R*-Tree. Each R*-Tree is relative to a route in the road network, can index the spatiotemporal trajectory of the moving objects in the road. Moreover, moving objects query is implemented based on this index structure. It compared to other index structures for road-network-based moving objects, such as MON-Tree, the experimental results shown that the MG2R* can effectively improve the query performance of the spatio-temporal trajectory of network-constrained moving objects.

scholarly journals A Survey on Big Data for Trajectory Analytics

2020 ◽  
Vol 9 (2) ◽  
pp. 88
Author(s):  
Damião Ribeiro de Almeida ◽  
Cláudio de Souza Baptista ◽  
Fabio Gomes de Andrade ◽  
Amilcar Soares
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Moving Objects ◽  
Research Field ◽  
Temporal Data ◽  
Trajectory Data ◽  
Management Technology ◽  
Spatial Big Data ◽  
Analytical Processing ◽  
Spatio Temporal

Trajectory data allow the study of the behavior of moving objects, from humans to animals. Wireless communication, mobile devices, and technologies such as Global Positioning System (GPS) have contributed to the growth of the trajectory research field. With the considerable growth in the volume of trajectory data, storing such data into Spatial Database Management Systems (SDBMS) has become challenging. Hence, Spatial Big Data emerges as a data management technology for indexing, storing, and retrieving large volumes of spatio-temporal data. A Data Warehouse (DW) is one of the premier Big Data analysis and complex query processing infrastructures. Trajectory Data Warehouses (TDW) emerge as a DW dedicated to trajectory data analysis. A list and discussions on problems that use TDW and forward directions for the works in this field are the primary goals of this survey. This article collected state-of-the-art on Big Data trajectory analytics. Understanding how the research in trajectory data are being conducted, what main techniques have been used, and how they can be embedded in an Online Analytical Processing (OLAP) architecture can enhance the efficiency and development of decision-making systems that deal with trajectory data.

scholarly journals A Review of GPS Trajectories Classification Based on Transportation Mode

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3741 ◽  
Author(s):  
Xue Yang ◽  
Kathleen Stewart ◽  
Luliang Tang ◽  
Zhong Xie ◽  
Qingquan Li
Keyword(s):  
Data Acquisition ◽  
Mixed Mode ◽  
Moving Objects ◽  
Single Mode ◽  
Point Of View ◽  
Location Based Services ◽  
Gps Data ◽  
Transportation Mode ◽  
Gps Trajectories ◽  
Transition Points

GPS trajectories generated by moving objects provide researchers with an excellent resource for revealing patterns of human activities. Relevant research based on GPS trajectories includes the fields of location-based services, transportation science, and urban studies among others. Research relating to how to obtain GPS data (e.g., GPS data acquisition, GPS data processing) is receiving significant attention because of the availability of GPS data collecting platforms. One such problem is the GPS data classification based on transportation mode. The challenge of classifying trajectories by transportation mode has approached detecting different modes of movement through the application of several strategies. From a GPS data acquisition point of view, this paper macroscopically classifies the transportation mode of GPS data into single-mode and mixed-mode. That means GPS trajectories collected based on one type of transportation mode are regarded as single-mode data; otherwise it is considered as mixed-mode data. The one big difference of classification strategy between single-mode and mixed-mode GPS data is whether we need to recognize the transition points or activity episodes first. Based on this, we systematically review existing classification methods for single-mode and mixed-mode GPS data and introduce the contributions of these methods as well as discuss their unresolved issues to provide directions for future studies in this field. Based on this review and the transportation application at hand, researchers can select the most appropriate method and endeavor to improve them.

Moving Beyond Traditional Decision Support Systems

2021 ◽  
pp. 1431-1445
Author(s):  
Noura Azaiez ◽  
Jalel Akaichi ◽  
Jeffrey Hsu
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Data Warehouse ◽  
Moving Objects ◽  
Trajectory Data ◽  
Global Positioning ◽  
Reduced Costs ◽  
Spatio Temporal ◽  
Traditional Approaches ◽  
Dairy Milk

Integrating the concept of mobility into the professional and organizational realm offers the possibility of reducing geographical disparities related to organization services. The advances made in technology, geographic information systems and pervasive systems equipped with global positioning (GPS) technologies have been able to bring about an evolution from classic data approaches towards the modeling of trajectory data resulting from moving activities of moving objects. As such, trajectory data needs first to be loaded into a Data Warehouse for analysis purposes. However, the traditional approaches used are poorly suited to handle spatio-temporal data features and also the decision making tasks related to mobility issues. Because of this mismatch, the authors propose to move beyond traditional approaches and propose a repository that is able to analyse trajectories of moving objects. Improving decision making and extracting pertinent knowledge with reduced costs and time expended are the main goals of this revised analysis approach. Thus, the authors propose an approach in which they employ the Bottom-up approach to modeling a Decision Support System which is designed to support Trajectory Data. As an example to illustrate this approach, the authors use a creamery and dairy milk mobile cistern application to demonstrate the effectiveness of their approach.

A State-of-the-Art in Spatio-Temporal Data Warehousing, OLAP and Mining

Data Mining ◽  
2013 ◽  
pp. 2021-2056
Author(s):  
Leticia Gómez ◽  
Bart Kuijpers ◽  
Bart Moelans ◽  
Alejandro Vaisman
Keyword(s):  
Moving Objects ◽  
Data Warehousing ◽  
Route Planning ◽  
Geographic Information ◽  
Moving Object ◽  
Trajectory Data ◽  
Attribute Data ◽  
Object Databases ◽  
Spatio Temporal ◽  
The University

Geographic Information Systems (GIS) have been extensively used in various application domains, ranging from economical, ecological and demographic analysis, to city and route planning. Nowadays, organizations need sophisticated GIS-based Decision Support System (DSS) to analyze their data with respect to geographic information, represented not only as attribute data, but also in maps. Thus, vendors are increasingly integrating their products, leading to the concept of SOLAP (Spatial OLAP). Also, in the last years, and motivated by the explosive growth in the use of PDA devices, the field of moving object data has been receiving attention from the GIS community, although not much work has been done to provide moving object databases with OLAP capabilities. In the first part of this paper we survey the SOLAP literature. We then address the problem of trajectory analysis, and review recent efforts regarding trajectory data warehousing and mining. We also provide an in-depth comparative study between two proposals: the GeoPKDD project (that makes use of the Hermes system), and Piet, a proposal for SOLAP and moving objects, developed at the University of Buenos Aires, Argentina. Finally, we discuss future directions in the field, including SOLAP analysis over raster data.

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