Vehicle anomalous trajectory detection algorithm based on road network partition

2021 ◽  
Author(s):  
Xujun Zhao ◽  
Jianhua Su ◽  
Jianghui Cai ◽  
Haifeng Yang ◽  
Tingting Xi
Keyword(s):  
Road Network ◽  
Detection Algorithm ◽  
Network Partition

Spatial Analysis Based Urban Road Network Partition Method for Macroscopic Evaluation

2011 ◽  
Vol 97-98 ◽  
pp. 512-517
Author(s):  
Wen Jie Zou ◽  
Jian Cheng Weng ◽  
Jian Rong ◽  
Wei Zhou
Keyword(s):  
Spatial Analysis ◽  
Road Network ◽  
Urban Road Network ◽  
Network Partition ◽  
Urban Road ◽  
The Road ◽  
Network Operation ◽  
Partition Method ◽  
Operation Status

In order to improve the reliability of urban road network operation evaluation, the road network regional Partition methods were launched in this paper. The geographic grid was introduced first, and a 4-level road network model was defined. Then, the spatial analysis based urban road network division method was proposed by analyzing the characteristics of road network operation. This method can reflect the influence between adjacent regional units, and improve the reliability of urban road network division. Finally, this research took a certain area in Beijing as a case study, and divided the road network as several regional units. Macroscopic evaluation result shows that it is effective for scientifically describing the road network operation status.

scholarly journals Intelligent Highway Traffic Detection Algorithm based on Deep Learning

2021 ◽  
Author(s):  
ming ji ◽  
Chuanxia Sun ◽  
Yinglei Hu
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Traffic Congestion ◽  
Road Network ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Traffic Data ◽  
Vehicle Recognition ◽  
Environment Recognition

Abstract In order to solve the increasingly serious traffic congestion problem, an intelligent transportation system is widely used in dynamic traffic management, which effectively alleviates traffic congestion and improves road traffic efficiency. With the continuous development of traffic data acquisition technology, it is possible to obtain real-time traffic data in the road network in time. A large amount of traffic information provides a data guarantee for the analysis and prediction of road network traffic state. Based on the deep learning framework, this paper studies the vehicle recognition algorithm and road environment discrimination algorithm, which greatly improves the accuracy of highway vehicle recognition. Collect highway video surveillance images in different environments, establish a complete original database, build a deep learning model of environment discrimination, and train the classification model to realize real-time environment recognition of highway, as the basic condition of vehicle recognition and traffic event discrimination, and provide basic information for vehicle detection model selection. To improve the accuracy of road vehicle detection, the vehicle target labeling and sample preprocessing of different environment samples are carried out. On this basis, the vehicle recognition algorithm is studied, and the vehicle detection algorithm based on weather environment recognition and fast RCNN model is proposed. Then, the performance of the vehicle detection algorithm described in this paper is verified by comparing the detection accuracy differences between different environment dataset models and overall dataset models, different network structures and deep learning methods, and other methods.

Taxi Global Positioning System Data in Urban Road Network: A Methodology to Identify Key Road Clusters Based on Travel Speed–Traffic Volume Correlation

2021 ◽  
pp. 036119812110366
Author(s):  
Yi Li ◽  
Weifeng Li ◽  
Qing Yu ◽  
Han Yang
Keyword(s):  
Traffic Congestion ◽  
Road Network ◽  
Travel Speed ◽  
Detection Algorithm ◽  
Traffic Volume ◽  
Urban Traffic ◽  
Mitigation Strategies ◽  
Operating Characteristics ◽  
The Road ◽  
Road Segments

Urban traffic congestion is one of the urban diseases that needs to be solved urgently. Research has already found that a few road segments can significantly influence the overall operation of the road network. Traditional congestion mitigation strategies mainly focus on the topological structure and the transport performance of each single key road segment. However, the propagation characteristics of congestion indicate that the interaction between road segments and the correlation between travel speed and traffic volume should also be considered. The definition is proposed for “key road cluster” as a group of road segments with strong correlation and spatial compactness. A methodology is proposed to identify key road clusters in the network and understand the operating characteristics of key road clusters. Considering the correlation between travel speed and traffic volume, a unidirectional-weighted correlation network is constructed. The community detection algorithm is applied to partition road segments into key road clusters. Three indexes are used to evaluate and describe the characteristic of these road clusters, including sensitivity, importance, and IS. A case study is carried out using taxi GPS data of Shanghai, China, from May 1 to 17, 2019. A total of 44 key road clusters are identified in the road network. According to their spatial distribution patterns, these key road clusters can be classified into three types—along with network skeletons, around transportation hubs, and near bridges. The methodology unveils the mechanism of congestion formation and propagation, which can offer significant support for traffic management.

A novel road network change detection algorithm based on floating car tracking data

2016 ◽  
Vol 75 (2) ◽  
pp. 161-167 ◽  
Author(s):  
Weidong Fang ◽  
Rong Hu ◽  
Xiang Xu ◽  
Ye Xia ◽  
Mao-Hsiung Hung
Keyword(s):  
Change Detection ◽  
Road Network ◽  
Detection Algorithm ◽  
Tracking Data ◽  
Network Change

scholarly journals ANALYSIS OF THE ROAD NETWORK STRUCTURES BASED ON STREET CONNECTIVITY

Contexto ◽  
2020 ◽  
Vol 14 (20) ◽  
Author(s):  
María Erándi Flores Romero ◽  
Irving Omar Morales Agiss ◽  
Liliana Beatriz Sosa Compean
Keyword(s):  
Community Detection ◽  
Road Network ◽  
Public Policies ◽  
Detection Algorithm ◽  
Street Connectivity ◽  
Urban Mobility ◽  
Road Infrastructure ◽  
The Road ◽  
Community Detection Algorithm ◽  
The City

The following article proposes a method to identify structures inside a road network with a flow-base community detection algorithm implemented on a graph representing the city road network. According to the results obtained in the cities of Mexico and Monterrey, the method effectively divides road infrastructure into several communities and preserves geographical neighboring. The frontiers of communities match administrative divisions along with other frontiers inside the city. The identification of communities could be useful to study the heterogeneity of street connectivity inside the city which could lead to improvements in urban mobility or even the application of public policies.

Network Partition and Pilot Nodes Selection for Reactive Power / Voltage Control Based on the Complex Network Theory

2013 ◽  
Vol 291-294 ◽  
pp. 2204-2211
Author(s):  
Xing Hua Wang ◽  
Ze Xiang Cai
Keyword(s):  
Complex Network ◽  
Network Theory ◽  
Reactive Power ◽  
Selection Index ◽  
Voltage Control ◽  
Detection Algorithm ◽  
Voltage Sensitivity ◽  
Network Partition ◽  
Complex Network Theory ◽  
Community Detection Algorithm

Conventional network partition and pilot nodes selection methods for reactive power / voltage control are mainly based on the reactive power - voltage sensitivity, however, it is hard to regulate the balance of the reactive power in partitions and pilot nodes may over-concentrate in some regions. According to the community detection algorithm in complex network theory, an improved community modular index is proposed with the consideration of the reactive power balance degree in partitions, while the power grid is modeled as the weighted network with similarity weight. By introducing the concept of vertex degree and betweenness, a novel pilot nodes selection index is presented , which is based on the ranking of observability and controllability sensitivity and can evaluate the centrality and connection density of node. Applying the proposed index and method to IEEE 39-bus system, simulation results show the effectiveness.

scholarly journals Road network partitioning method based on Canopy-Kmeans clustering algorithm

2020 ◽  
Vol 54 (2) ◽  
pp. 95-106 ◽  
Author(s):  
Xiaohui Lin ◽  
Jianmin Xu
Keyword(s):  
Real Time ◽  
Road Network ◽  
Clustering Algorithm ◽  
Average Density ◽  
Road Segment ◽  
Network Partitioning ◽  
Network Partition ◽  
Average Speed ◽  
The Road ◽  
Road Segments

With the increasing scope of traffic signal control, in order to improve the stability and flexibility of the traffic control system, it is necessary to rationally divide the road network according to the structure of the road network and the characteristics of traffic flow. However, road network partition can be regarded as a clustering process of the division of road segments with similar attributes, and thus, the clustering algorithm can be used to divide the sub-areas of road network, but when Kmeans clustering algorithm is used in road network partitioning, it is easy to fall into the local optimal solution. Therefore, we proposed a road network partitioning method based on the Canopy-Kmeans clustering algorithm based on the real-time data collected from the central longitude and latitude of a road segment, average speed of a road segment, and average density of a road segment. Moreover, a vehicle network simulation platform based on Vissim simulation software is constructed by taking the real-time collected data of central longitude and latitude, average speed and average density of road segments as sample data. Kmeans and Canopy-Kmeans algorithms are used to partition the platform road network. Finally, the quantitative evaluation method of road network partition based on macroscopic fundamental diagram is used to evaluate the results of road network partition, so as to determine the optimal road network partition algorithm. Results show that these two algorithms have divided the road network into four sub-areas, but the sections contained in each sub-area are slightly different. Determining the optimal algorithm on the surface is impossible. However, Canopy-Kmeans clustering algorithm is superior to Kmeans clustering algorithm based on the quantitative evaluation index (e.g. the sum of squares for error and the R-Square) of the results of the subareas. Canopy-Kmeans clustering algorithm can effectively partition the road network, thereby laying a foundation for the subsequent road network boundary control.

Read, Understand, Learn, & Excel: Development and Testing of an Automated Reading Strategy Detection Algorithm for Postsecondary Students

2019 ◽  
Vol 28 (3) ◽  
pp. 1257-1267 ◽  
Author(s):  
Priya Kucheria ◽  
McKay Moore Sohlberg ◽  
Jason Prideaux ◽  
Stephen Fickas
Keyword(s):  
Academic Success ◽  
Undergraduate Students ◽  
Expository Text ◽  
Reading Strategy ◽  
Detection Algorithm ◽  
Expository Texts ◽  
Human Observer ◽  
Behavioral Strategies ◽  
Periodic Review ◽  
Wide Range

PurposeAn important predictor of postsecondary academic success is an individual's reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader's use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.MethodAn iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).ResultsAgreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.ConclusionRead, Understand, Learn, & Excel provides proof of concept that a reader's approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.Supplemental Materialhttps://doi.org/10.23641/asha.8204786

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