scholarly journals A Hybrid Aggregate Index Method for Trajectory Data

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Yaqing Shi ◽  
Song Huang ◽  
Changyou Zheng ◽  
Haijin Ji
Keyword(s):  
Road Network ◽  
Moving Objects ◽  
Trajectory Data ◽  
Index Method ◽  
Continuous Sampling ◽  
Aggregate Index ◽  
Hybrid Framework ◽  
Aggregate Queries ◽  
Aggregate Query ◽  
Processing Component

The aggregate query of moving objects on road network keeps being popular in the ITS research community. The existing methods often assume that the sampling frequency of the positioning devices like GPS or roadside radar is dense enough, making the result’s uncertainty negligible. However, such assumption is not always tenable, especially in the extreme occasions like wartime. Regarding this issue, a hybrid aggregate index framework is proposed in this paper, in order to perform aggregate queries on massive trajectories that are sampled sparsely. Firstly, this framework uses an offline batch processing component based on the UPBI-Sketch index to acquire each object’s most likely position between two continuous sampling instants. Next, it introduces the AMH+-Sketch index to processing the aggregate operation online, making sure each object is counted only once in the result. The experimental results show that the hybrid framework can ensure the query accuracy by adjusting the parameters L and U of AMH+-Sketch index and its space storage advantage becomes more and more obvious when the data scale is very large.

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 Automatic Method for Detection and Update of Additive Changes in Road Network with GPS Trajectory Data

2019 ◽  
Vol 8 (9) ◽  
pp. 411 ◽  
Author(s):  
Tang ◽  
Deng ◽  
Huang ◽  
Liu ◽  
Chen
Keyword(s):  
Road Network ◽  
Sampling Rate ◽  
Quality Data ◽  
Automatic Method ◽  
Trajectory Data ◽  
The Road ◽  
High Sampling Rate ◽  
Map Generation ◽  
Gps Trajectory Data ◽  
Gps Trajectory

Ubiquitous trajectory data provides new opportunities for production and update of the road network. A number of methods have been proposed for road network construction and update based on trajectory data. However, existing methods were mainly focused on reconstruction of the existing road network, and the update of newly added roads was not given much attention. Besides, most of existing methods were designed for high sampling rate trajectory data, while the commonly available GPS trajectory data are usually low-quality data with noise, low sampling rates, and uneven spatial distributions. In this paper, we present an automatic method for detection and update of newly added roads based on the common low-quality trajectory data. First, additive changes (i.e., newly added roads) are detected using a point-to-segment matching algorithm. Then, the geometric structures of new roads are constructed based on a newly developed decomposition-combination map generation algorithm. Finally, the detected new roads are refined and combined with the original road network. Seven trajectory data were used to test the proposed method. Experiments show that the proposed method can successfully detect the additive changes and generate a road network which updates efficiently.

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 RealTE: Real-time Trajectory Estimation Over Large-Scale Road Networks

2022 ◽  
Author(s):  
Qibin Zhou ◽  
Qingang Su ◽  
Dingyu Yang
Keyword(s):  
Travel Time ◽  
Real Time ◽  
High Speed ◽  
Road Network ◽  
Large Scale ◽  
Road Networks ◽  
Trajectory Data ◽  
Real Time Traffic ◽  
Histogram Estimation ◽  
Traffic Estimation

Real-time traffic estimation focuses on predicting the travel time of one travel path, which is capable of helping drivers selecting an appropriate or favor path. Statistical analysis or neural network approaches have been explored to predict the travel time on a massive volume of traffic data. These methods need to be updated when the traffic varies frequently, which incurs tremendous overhead. We build a system RealTER⁢e⁢a⁢l⁢T⁢E, implemented on a popular and open source streaming system StormS⁢t⁢o⁢r⁢m to quickly deal with high speed trajectory data. In RealTER⁢e⁢a⁢l⁢T⁢E, we propose a locality-sensitive partition and deployment algorithm for a large road network. A histogram estimation approach is adopted to predict the traffic. This approach is general and able to be incremental updated in parallel. Extensive experiments are conducted on six real road networks and the results illustrate RealTE achieves higher throughput and lower prediction error than existing methods. The runtime of a traffic estimation is less than 11 seconds over a large road network and it takes only 619619 microseconds for model updates.

scholarly journals Estimation of Groundwater Quality in and Around Dumpsites of Cuddalore District, Tamil Nadu, India

2020 ◽  
Vol 9 (9) ◽  
pp. 229-233
Keyword(s):  
Ground Water ◽  
Tamil Nadu ◽  
World Health ◽  
Ground Water Quality ◽  
Index Method ◽  
Physical Chemical ◽  
Aggregate Index ◽  
Health Organization ◽  
Land Filling

Land filling of urban solid desecrate is a universal dissipate care and one of the economical technique for organizing waste in several parts of the globe. Landfill poses serious threats to the worth of the surroundings if imperfectly safe and indecently managed. The stages of different physicochemical parameters are investigated including Total Dissolved Solids (TDS), alkalinity, pH, Electrical Conductivity (EC), and hardness. The mixture of physical, chemical and microbial practice in the waste shifts the contaminant from the misuse matter to the percolating water. The study asses ground water quality of samples near the dumping area and characterized. The effects are evaluated with the world health organization (WHO) and Bureau of Indian Standard (BIS) index. Also attempt will be made by the aggregate index method to assess the excellence of ground water in Chidambaram and Cuddalore SIPCOT dumpsites.

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.

A trajectory map matching algorithm via exploring the average directional features of continuous windows

2021 ◽  
pp. 1-16
Author(s):  
Xiaohan Wang ◽  
Pei Wang ◽  
Weilong Chen ◽  
Wangwu Hu ◽  
Long Yang
Keyword(s):  
Road Network ◽  
Transition Probability ◽  
Location Based Services ◽  
Original Position ◽  
Map Matching ◽  
Trajectory Data ◽  
Matching Accuracy ◽  
Data Set ◽  
Matching Algorithm ◽  
Low Efficiency

Many location-based services require a pre-processing step of map matching. Due to the error of the original position data and the complexity of the road network, the matching algorithm will have matching errors when the complex road network is implemented, which is therefore challenging. Aiming at the problems of low matching accuracy and low efficiency of existing algorithms at Y-shaped intersections and roundabouts, this paper proposes a space-time-based continuous window average direction feature trajectory map matching algorithm (STDA-matching). Specifically, the algorithm not only adaptively generates road network topology data, but also obtains more accurate road network relationships. Based on this, the transition probability is calculated by using the average direction feature of the continuous window of the track points to improve the matching accuracy of the algorithm. Secondly, the algorithm simplifies the trajectory by clustering the GPS trajectory data aggregation points to improve the matching efficiency of the algorithm. Finally, we use a real and effective data set to compare the algorithm with the two existing algorithms. Experimental results show that our algorithm is effective.

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