AN AUTOMATIC SEMANTIC MAP GENERATION METHOD USING TRAJECTORY DATA

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 63-67
Author(s):  
Y. Miao ◽  
X. Tang ◽  
Z. Wang
Keyword(s):  
Human Behavior ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Group Behavior ◽  
Trajectory Data ◽  
Geometric Information ◽  
Semantic Map ◽  
Map Generation ◽  
Terrain Features ◽  
Unknown Region

Abstract. It’s easily to obtain the geometric information of terrain features in a timely manner using advanced surveying and mapping methods, but it is impossible to obtain their semantic information with low latency due to the rapid development of cities. The popularity of GPS-enabled devices and technologies provide us a large number of personal location information. Moreover, it is possible to extract the personal or group behavior pattern due to the regularity of human behavior. Those conditions make it possible to extract and identify human behavior patterns from their trajectory data. In this paper, we present an automatic semantic map generation method that extract semantic patterns and take advantage of them to tagging spatial objects in an unknown region based on known semantic patterns. We study the regularity of trajectory data and build the semantic pattern based on the regularity of human behavior. Most importantly, we use known semantic patterns to identify the semantics of the stay points in the unknown region, and use this method to realize the semantic recognition of the stay points. Results of the experiments show the effectiveness of our proposed method.

scholarly journals GeoSOT-Based Spatiotemporal Index of Massive Trajectory Data

2019 ◽  
Vol 8 (6) ◽  
pp. 284 ◽  
Author(s):  
Chunyao Qian ◽  
Chao Yi ◽  
Chengqi Cheng ◽  
Guoliang Pu ◽  
Xiaofeng Wei ◽  
...  
Keyword(s):  
Large Scale ◽  
User Behavior ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Range Query ◽  
Space Time ◽  
Trajectory Data ◽  
Time Dimension ◽  
Growth Trend ◽  
Query Efficiency

With the rapid development of global positioning technologies and the pervasiveness of intelligent mobile terminals, trajectory data have shown a sharp growth trend both in terms of data volume and coverage. In recent years, increasing numbers of LBS (location based service) applications have provided us with trajectory data services such as traffic flow statistics and user behavior pattern analyses. However, the storage and query efficiency of massive trajectory data are increasingly creating a bottleneck for these applications, especially for large-scale spatiotemporal query scenarios. To solve this problem, we propose a new spatiotemporal indexing method to improve the query efficiency of massive trajectory data. First, the method extends the GeoSOT spatial partitioning scheme to the time dimension and forms a global space–time subdivision scheme. Second, a novel multilevel spatiotemporal grid index, called the GeoSOT ST-index, was constructed to organize trajectory data hierarchically. Finally, a spatiotemporal range query processing method is proposed based on the index. We implement and evaluate the index in MongoDB. By comparing the range query efficiency and scalability of our index with those of the other two space–time composite indexes, we found that our approach improves query efficiency levels by approximately 40% and has better scalability under different data volumes.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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 Blind Stereo Image Quality Evaluation Based on Convolutional Network and Saliency Weighting

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Wujie Zhou
Keyword(s):  
Image Quality ◽  
Quality Evaluation ◽  
Rapid Development ◽  
Stereo Image ◽  
Convolutional Network ◽  
Image Quality Evaluation ◽  
Map Generation ◽  
Versatile Tool ◽  
Generation Network ◽  
Quality Map

With the rapid development of stereo image applications, there is an increasing demand to develop a versatile tool to evaluate the perceived quality of stereo images. Therefore, in this study, a blind stereo image quality evaluation (SIQE) algorithm based on convolutional network and saliency weighting is proposed. The main network framework used by the algorithm is the quality map generation network, which is used to train the distortion image dataset and quality map label to obtain an optimal network framework. Finally, the left view, right view, and cyclopean view of the stereo image are used as inputs to the network frame, respectively, and then weighted fusion for the final stereo image quality score. The experimental results reveal that the proposed SIQE algorithm can improve the accuracy of the image quality prediction and prediction score to a certain extent and has good generalization ability.

scholarly journals Learning to Generate Maps from Trajectories

2020 ◽  
Vol 34 (01) ◽  
pp. 890-897 ◽  
Author(s):  
Sijie Ruan ◽  
Cheng Long ◽  
Jie Bao ◽  
Chunyang Li ◽  
Zisheng Yu ◽  
...  
Keyword(s):  
Road Network ◽  
Traditional Approach ◽  
Trajectory Data ◽  
Embedded Devices ◽  
The Road ◽  
Mobile Objects ◽  
Map Generation ◽  
Spatial View ◽  
Topological Connectivity ◽  
Wide Usage

Accurate and updated road network data is vital in many urban applications, such as car-sharing, and logistics. The traditional approach to identifying the road network, i.e., field survey, requires a significant amount of time and effort. With the wide usage of GPS embedded devices, a huge amount of trajectory data has been generated by different types of mobile objects, which provides a new opportunity to extract the underlying road network. However, the existing trajectory-based map recovery approaches require many empirical parameters and do not utilize the prior knowledge in existing maps, which over-simplifies or over-complicates the reconstructed road network. To this end, we propose a deep learning-based map generation framework, i.e., DeepMG, which learns the structure of the existing road network to overcome the noisy GPS positions. More specifically, DeepMG extracts features from trajectories in both spatial view and transition view and uses a convolutional deep neural network T2RNet to infer road centerlines. After that, a trajectory-based post-processing algorithm is proposed to refine the topological connectivity of the recovered map. Extensive experiments on two real-world trajectory datasets confirm that DeepMG significantly outperforms the state-of-the-art methods.

scholarly journals Extended Classification Course Improves Road Intersection Detection from Low-Frequency GPS Trajectory Data

2020 ◽  
Vol 9 (3) ◽  
pp. 181
Author(s):  
Banqiao Chen ◽  
Chibiao Ding ◽  
Wenjuan Ren ◽  
Guangluan Xu
Keyword(s):  
Clustering Algorithm ◽  
Recall Rate ◽  
Location Based Services ◽  
Trajectory Data ◽  
Intersection Detection ◽  
Map Generation ◽  
Gps Trajectory Data ◽  
Road Intersections ◽  
Road Maps ◽  
Gps Trajectory

The requirements of location-based services have generated an increasing need for up-to-date digital road maps. However, traditional methods are expensive and time-consuming, requiring many skilled operators. The feasibility of using massive GPS trajectory data provides a cheap and quick means for generating and updating road maps. The detection of road intersections, being the critical component of a road map, is a key problem in map generation. Unfortunately, low sampling rates and high disparities are ubiquitous among floating car data (FCD), making road intersection detection from such GPS trajectories very challenging. In this paper, we extend a point clustering-based road intersection detection framework to include a post-classification course, which utilizes the geometric features of road intersections. First, we propose a novel turn-point position compensation algorithm, in order to improve the concentration of selected turn-points under low sampling rates. The initial detection results given by the clustering algorithm are recall-focused. Then, we rule out false detections in an extended classification course based on an image thinning algorithm. The detection results of the proposed method are quantitatively evaluated by matching with intersections from OpenStreetMap using a variety of distance thresholds. Compared with other methods, our approach can achieve a much higher recall rate and better overall performance, thereby better supporting map generation and other similar applications.

scholarly journals Discovering Urban Functional Polycentricity: A Traffic Flow-Embedded and Topic Modeling-Based Methodology Framework

2020 ◽  
Vol 12 (5) ◽  
pp. 1897
Author(s):  
Shaodong Wang ◽  
Yanbin Liu ◽  
Wei Zhi ◽  
Xihua Wen ◽  
Weihua Zhou
Keyword(s):  
Urban Planning ◽  
Traffic Flow ◽  
Urban Area ◽  
Traffic Control ◽  
Urban Areas ◽  
Topic Modeling ◽  
Spatial Organization ◽  
Rapid Development ◽  
Trajectory Data ◽  
Function Distribution

With the rapid development of communication and transportation technologies, the urban area is increasingly becoming an ever more dynamic, comprehensive, and complex system. Meanwhile, functional polycentricity as a distinctive feature has been characterizing urban areas around the world. However, the spatial structure of the urban area has yet to be fully comprehended from a dynamic perspective, and understanding the spatial organization of polycentric urban regions (PUR) is crucial for issues related to urban planning, traffic control, and urban risk management. The analysis of polycentricity strongly depends on the spatial scale. In order to identify functional polycentricity at the intra-unban scale, this paper presents a traffic flow-embedded and topic modeling-based methodology framework. This framework was evaluated on real-world datasets from the Wujiang district, Suzhou, China, which contains 151,419 records of taxi trajectory data and 86,036 records of points of interest (POI) data. This paper provides a novel approach to examining urban functional polycentricity via combining urban function distribution and spatial interactions. This proposed methodology can help urban authorities better understand urban dynamics in terms of function distribution and internal connectedness and facilitate urban development in terms of urban planning and traffic control.

scholarly journals Ship Spatiotemporal Key Feature Point Online Extraction Based on AIS Multi-Sensor Data Using an Improved Sliding Window Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2706 ◽  
Author(s):  
Miao Gao ◽  
Guo-You Shi
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Behavior Pattern ◽  
Sliding Window ◽  
Feature Point ◽  
Sensor Data ◽  
Identification System ◽  
Trajectory Data ◽  
Ship Navigation ◽  
Online Extraction

Large volumes of automatic identification system (AIS) data provide new ideas and methods for ship data mining and navigation behavior pattern analysis. However, large volumes of big data have low unit values, resulting in the need for large-scale computing, storage, and display. Learning efficiency is low and learning direction is blind and untargeted. Therefore, key feature point (KFP) extraction from the ship trajectory plays an important role in fields such as ship navigation behavior analysis and big data mining. In this paper, we propose a ship spatiotemporal KFP online extraction algorithm that is applied to AIS trajectory data. The sliding window algorithm is modified for application to ship navigation angle deviation, position deviation, and the spatiotemporal characteristics of AIS data. Next, in order to facilitate the subsequent use of the algorithm, a recommended threshold range for the corresponding two parameters is discussed. Finally, the performance of the proposed method is compared with that of the Douglas–Peucker (DP) algorithm to assess its feature extraction accuracy and operational efficiency. The results show that the proposed improved sliding window algorithm can be applied to rapidly and easily extract the KFPs from AIS trajectory data. This ability provides significant benefits for ship traffic flow and navigational behavior learning.

Analysis and Simulation of Group Behavior Using a Dynamic Sphere of Influence

2005 ◽  
Vol 9 (2) ◽  
pp. 159-165 ◽  
Author(s):  
Tsuyoshi Taki ◽  
◽  
Junichi Hasegawa
Keyword(s):  
Human Behavior ◽  
Basic Concept ◽  
Personal Space ◽  
Group Behavior ◽  
Crowd Simulation ◽  
Behavior Model ◽  
Spatial Feature ◽  
Sphere Of Influence

To evaluate or predict human behavior, extraction of characteristic patterns from actual crowd scenes and crowd simulation in different situations based on general human behavior model are needed. We use changes in a spatial feature formed by individual movement called a "dominant region", a type of dynamic personal space. In the paper, a basic concept and calculation of the dominant region and its applications are presented. Experiments show that the proposed feature is useful in evaluating and simulating human behavior.

Context-based similarity measure on human behavior pattern analysis

2018 ◽  
Vol 23 (14) ◽  
pp. 5455-5467 ◽  
Author(s):  
Aria Ghora Prabono ◽  
Seok-Lyong Lee ◽  
Bernardo Nugroho Yahya
Keyword(s):  
Human Behavior ◽  
Similarity Measure ◽  
Pattern Analysis ◽  
Behavior Pattern
Sign in / Sign up

Export Citation Format

Share Document