scholarly journals Extracting Interesting Regions and Trips from Taxi Trajectory Data

2019 ◽  
Vol 13 (2) ◽  
pp. 258
Author(s):  
Ammar M. Huneiti ◽  
Omar Y. Adwan
Keyword(s):  
Moving Objects ◽  
Self Organizing Map ◽  
Trajectory Data ◽  
Useful Knowledge ◽  
Processing Step ◽  
Edge Location ◽  
Gps Devices ◽  
Research Initiatives ◽  
Spatial Trajectories ◽  
The City

The increasing availability of cutting-edge location-acquisition technologies such as GPS devices, has led to the generation of huge datasets of spatial trajectories. These trajectories store important information regarding the movement of people, vehicles, robots, animals, users of social networks, etc. Many research initiatives have applied data mining techniques in order to extract useful knowledge from this data.  An important, and yet complicated, pre-processing step in mining patterns from trajectory data, is the identification of the Regions of Interest (RoI) that have been collectively navigated by a set of trajectories. The RoI’s are being manually and subjectively pre-defined by a group of experts as popular regions, regardless of the actual behaviour of the moving objects. This research emphasizes the usefulness of applying an unsupervised machine learning technique, namely Self Organizing Map (SOM), in order to identify the RoI’s associated with a trajectory dataset depending on the moving objects’ behaviour. The research experiments were conducted using 180 thousand of the trajectories generated by 442 taxis running in the city of Porto, in Portugal, and they demonstrate the ability of SOM in identifying the RoI’s and interesting taxi trips within the city.

scholarly journals An Overview of Moving Object Trajectory Compression Algorithms

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Penghui Sun ◽  
Shixiong Xia ◽  
Guan Yuan ◽  
Daxing Li
Keyword(s):  
Moving Objects ◽  
Moving Object ◽  
Trajectory Data ◽  
Compression Algorithms ◽  
Valuable Data ◽  
Implementation Processes ◽  
Exponential Increase ◽  
Validation Criteria ◽  
Gps Devices ◽  
Moving Object Trajectory

Compression technology is an efficient way to reserve useful and valuable data as well as remove redundant and inessential data from datasets. With the development of RFID and GPS devices, more and more moving objects can be traced and their trajectories can be recorded. However, the exponential increase in the amount of such trajectory data has caused a series of problems in the storage, processing, and analysis of data. Therefore, moving object trajectory compression undoubtedly becomes one of the hotspots in moving object data mining. To provide an overview, we survey and summarize the development and trend of moving object compression and analyze typical moving object compression algorithms presented in recent years. In this paper, we firstly summarize the strategies and implementation processes of classical moving object compression algorithms. Secondly, the related definitions about moving objects and their trajectories are discussed. Thirdly, the validation criteria are introduced for evaluating the performance and efficiency of compression algorithms. Finally, some application scenarios are also summarized to point out the potential application in the future. It is hoped that this research will serve as the steppingstone for those interested in advancing moving objects mining.

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.

Forest path condition monitoring based on crowd-based trajectory data analysis

2020 ◽  
pp. 1-18
Author(s):  
Francisco Arcas-Tunez ◽  
Fernando Terroso-Saenz
Keyword(s):  
Information Acquisition ◽  
Road Network ◽  
Road Networks ◽  
Trajectory Data ◽  
Country Road ◽  
Wide Range ◽  
Sport Activities ◽  
Path Condition ◽  
Spatio Temporal ◽  
The City

The development of Road Information Acquisition Systems (RIASs) based on the Mobile Crowdsensing (MCS) paradigm has been widely studied for the last years. In that sense, most of the existing MCS-based RIASs focus on urban road networks and assume a car-based scenario. However, there exist a scarcity of approaches that pay attention to rural and country road networks. In that sense, forest paths are used for a wide range of recreational and sport activities by many different people and they can be also affected by different problems or obstacles blocking them. As a result, this work introduces SAMARITAN, a framework for rural-road network monitoring based on MCS. SAMARITAN analyzes the spatio-temporal trajectories from cyclists extracted from the fitness application Strava so as to uncover potential obstacles in a target road network. The framework has been evaluated in a real-world network of forest paths in the city of Cieza (Spain) showing quite promising results.

scholarly journals An Approach to Spatiotemporal Trajectory Clustering Based on Community Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xin Wang ◽  
Xinzheng Niu ◽  
Jiahui Zhu ◽  
Zuoyan Liu
Keyword(s):  
Community Detection ◽  
Trajectory Analysis ◽  
Moving Objects ◽  
Clustering Algorithms ◽  
Similarity Measures ◽  
Detection Algorithm ◽  
Similarity Matrix ◽  
Trajectory Data ◽  
Trajectory Similarity ◽  
Community Detection Algorithm

Nowadays, large volumes of multimodal data have been collected for analysis. An important type of data is trajectory data, which contains both time and space information. Trajectory analysis and clustering are essential to learn the pattern of moving objects. Computing trajectory similarity is a key aspect of trajectory analysis, but it is very time consuming. To address this issue, this paper presents an improved branch and bound strategy based on time slice segmentation, which reduces the time to obtain the similarity matrix by decreasing the number of distance calculations required to compute similarity. Then, the similarity matrix is transformed into a trajectory graph and a community detection algorithm is applied on it for clustering. Extensive experiments were done to compare the proposed algorithms with existing similarity measures and clustering algorithms. Results show that the proposed method can effectively mine the trajectory cluster information from the spatiotemporal trajectories.

scholarly journals AUTOMATIC BLOCKED ROADS ASSESSMENT AFTER EARTHQUAKE USING HIGH RESOLUTION SATELLITE IMAGERY

2015 ◽  
Vol XL-1-W5 ◽  
pp. 601-605 ◽  
Author(s):  
H. Rastiveis ◽  
E. Hosseini-Zirdoo ◽  
F. Eslamizade
Keyword(s):  
High Resolution ◽  
World View ◽  
Satellite Image ◽  
Data Set ◽  
The Road ◽  
Vector Map ◽  
Processing Step ◽  
Image Quality Improvement ◽  
High Resolution Satellite Imagery ◽  
The City

In 2010, an earthquake in the city of Port-au-Prince, Haiti, happened quite by chance an accident and killed over 300000 people. According to historical data such an earthquake has not occurred in the area. Unpredictability of earthquakes has necessitated the need for comprehensive mitigation efforts to minimize deaths and injuries. Blocked roads, caused by debris of destroyed buildings, may increase the difficulty of rescue activities. In this case, a damage map, which specifies blocked and unblocked roads, can be definitely helpful for a rescue team. <br><br> In this paper, a novel method for providing destruction map based on pre-event vector map and high resolution world view II satellite images after earthquake, is presented. For this purpose, firstly in pre-processing step, image quality improvement and co-coordination of image and map are performed. Then, after extraction of texture descriptor from the image after quake and SVM classification, different terrains are detected in the image. Finally, considering the classification results, specifically objects belong to “debris” class, damage analysis are performed to estimate the damage percentage. In this case, in addition to the area objects in the “debris” class their shape should also be counted. The aforementioned process are performed on all the roads in the road layer.In this research, pre-event digital vector map and post-event high resolution satellite image, acquired by Worldview-2, of the city of Port-au-Prince, Haiti's capital, were used to evaluate the proposed method. The algorithm was executed on 1200×800 m2 of the data set, including 60 roads, and all the roads were labelled correctly. The visual examination have authenticated the abilities of this method for damage assessment of urban roads network after an earthquake.

scholarly journals Interesting Activities Discovery for Moving Objects Based on Collaborative Filtering

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Guan Yuan ◽  
Shixiong Xia ◽  
Yanmei Zhang
Keyword(s):  
Collaborative Filtering ◽  
Moving Objects ◽  
Experimental Results ◽  
Location Based Service ◽  
Trajectory Data ◽  
Hybrid Collaborative Filtering

With the development of location-based service, more and more moving objects can be traced, and a great deal of trajectory data can be collected. Finding and studying the interesting activities of moving objects from these data can help to learn their behavior very well. Therefore, a method of interesting activities discovery based on collaborative filtering is proposed in this paper. First, the interesting degree of the objects' activities is calculated comprehensively. Then, combined with the newly proposed hybrid collaborative filtering, similar objects can be computed and all kinds of interesting activities can be discovered. Finally, potential activities are recommended according to their similar objects. The experimental results show that the method is effective and efficient in finding objects' interesting activities.

Discovering Gathering Pattern Using a Taxicab Service Rate Analysis Method based on Neural Network

2020 ◽  
pp. 408-428
Author(s):  
Junming Zhang ◽  
Jinglin Li
Keyword(s):  
Neural Network ◽  
Moving Objects ◽  
High Volume ◽  
Mean Value ◽  
Service Rate ◽  
Trajectory Data ◽  
Rate Analysis ◽  
The Neural Network ◽  
Time Period ◽  
Effectiveness And Efficiency

Moving objects gathering pattern represents a group events or incidents that involve congregation of moving objects, enabling the analysis of traffic system. However, how to improve the effectiveness and efficiency of the gathering pattern discovering method still remains as a challenging issue since the large number of moving objects will generate high volume of trajectory data. In order to address this issue, the authors propose a method to discovering the gathering pattern by analyzing the taxicab demand. This paper first introduces the concept of Taxicab Service Rate (TSR). In this method, they use the KS measures to test the distribution of TSR and calculate the mean value of the TSR of a certain time period. Then, the authors use a neural network based method Neural Network Gathering Discovering (NNGD) to detect the gathering pattern. The neural network is based on the knowledge of historical gathering pattern data. The authors have implemented their method with experiments based on real trajectory data. The results show the both effectiveness and efficiency of their method.

Mining Partners in Trajectories

2020 ◽  
Vol 16 (1) ◽  
pp. 22-38
Author(s):  
Diego Vilela Monteiro ◽  
Rafael Duarte Coelho dos Santos ◽  
Karine Reis Ferreira
Keyword(s):  
Moving Objects ◽  
R Package ◽  
Moving Object ◽  
Spatiotemporal Data ◽  
Data Preparation ◽  
Trajectory Data ◽  
Gps Satellites ◽  
Object Trajectories ◽  
Moving Object Trajectory ◽  
Moving Object Trajectories

Spatiotemporal data is everywhere, being gathered from different devices such as Earth Observation and GPS satellites, sensor networks and mobile gadgets. Spatiotemporal data collected from moving objects is of particular interest for a broad range of applications. In the last years, such applications have motivated many pieces of research on moving object trajectory data mining. In this article, it is proposed an efficient method to discover partners in moving object trajectories. Such a method identifies pairs of trajectories whose objects stay together during certain periods, based on distance time series analysis. It presents two case studies using the proposed algorithm. This article also describes an R package, called TrajDataMining, that contains algorithms for trajectory data preparation, such as filtering, compressing and clustering, as well as the proposed method Partner.

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.

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