Research on Road Traffic Situation Awareness System Based on Image Big Data

2020 ◽  
Vol 35 (1) ◽  
pp. 18-26 ◽  
Author(s):  
Qing Zhu
2020 ◽  
Vol 1650 ◽  
pp. 032170
Author(s):  
Yinghao Zhu ◽  
Yangyang Peng ◽  
Shangzheng Wang ◽  
Shuimeng Shi ◽  
Nan Ji

2013 ◽  
Vol 2 (2) ◽  
pp. 45-51
Author(s):  
Goran Kos ◽  
Predrag Brlek ◽  
Kristijan Meic ◽  
Kresimir Vidovic

Abstract In terms of continual increase of number of traffic accidents and alarming trend of increasing number of traffic accidents with catastrophic consequences for human life and health, it is necessary to actively research and develop methods to combat these trends. One of the measures is the implementation of advanced information systems in existing traffic environment. Accidents clusters, as databases of traffic accidents, introduce a new dimension in traffic systems in the form of experience, providing information on current accidents and the ones that have previously occurred in a given period. This paper proposes a new approach to predictive management of traffic processes, based on the collection of data in real time and is based on accidents clusters. The modern traffic information services collects road traffic status data from a wide variety of traffic sensing systems using modern ICT technologies, creating the most accurate road traffic situation awareness achieved so far. Road traffic situation awareness enhanced by accident clusters' data can be visualized and distributed in various ways (including the forms of dynamic heat maps) and on various information platforms, suiting the requirements of the end-users. Accent is placed on their significant features that are based on additional knowledge about existing traffic processes and distribution of important traffic information in order to prevent and reduce traffic accidents.


2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yi Guan ◽  
Qian Chen

Since the 21st century, the development of network technology has entered a new stage, and mankind has gradually entered the era of big data with information explosion. Computer big data technology has changed people’s lifestyles. People can obtain information and materials without going out to provide convenience for people’s lives. But it is affected in many ways, but we will also encounter some difficulties in the process of using it. Informatization has had a profound impact on many areas of human social life. Especially in the context of global informationization, the information security (IS) problems encountered by China at this stage are more prominent and obvious than ever before. IS issues have also become a hot topic for many scholars to pay attention to and study. Informatization has gradually penetrated into every aspect of daily production work. However, the flow of enterprise informatization has made the public suffer from IS problems while improving office efficiency. This paper analyzes the shortcomings of current enterprise IS situation awareness; studies the construction of enterprise IS situation awareness system through big data technology, artificial intelligence algorithm, and threat intelligence technology; and puts forward the enterprise level IS situation awareness system model, situation awareness system, architecture, and specific implementation method. After analyzing the system design and deployment, the threats to corporate IS can be discovered in a more timely manner, and based on risk judgment and threat tracking, the company’s detection capabilities against security threats and security attacks can be improved, and effective security incident handling can be provided. Supported by technical means, the security situational awareness system will bring different protection and prevention to our enterprise.


Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Yun Li ◽  
Yanping Chen ◽  
Miaoxi Zhao ◽  
Xinxin Zhai

There is a huge amount of data in the opportunity of “turning waste into treasure” with the arrival of the big data age. Urban layout is very important for the development of urban transportation and building system. Once the layout of the city is finalized, it will be difficult to start again. Therefore, the urban architectural layout planning and design have a very important impact. This paper uses the urban architecture layout big data for building layout optimization using advanced computation techniques. Firstly, a big data collection and storage system based on the Hadoop platform is established. Then, the evaluation model of urban building planning based on improved logit and PSO algorithm is established. The PSO algorithm is used to find the suitable area for this kind of building layout, and then through five impact indicators: land prices, rail transit, historical protection, road traffic capacity, and commercial potential have been established by using the following logit linear regression model. Then, the bridge between logit and PSO algorithm is established by the fitness value of particle. The particle in the particle swarm is assigned to the index parameter of logit model, and then the logit model in the evaluation system is run. The performance index corresponding to the set of parameters is obtained. The performance index is passed to the PSO as the fitness value of the particle to search for the best adaptive position. The reasonable degree of regional architectural planning is obtained, and the rationality of urban architectural planning layout is determined.


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