A User Monitoring Road Traffic Information Collection Using SUMO and Scheme for Road Surveillance with Deep Mind Analytics and Human Behavior Tracking

Author(s):  
B.R. Vachan ◽  
Shakti Mishra
Author(s):  
Daniele Alessandrelli ◽  
Andrea Azzarà ◽  
Matteo Petracca ◽  
Christian Nastasi ◽  
Paolo Pagano

2003 ◽  
Vol 36 (18) ◽  
pp. 443-448
Author(s):  
Luciano Spinello ◽  
Luca Spaghetti ◽  
Francesco Martinelli ◽  
Valentino Di Salvo

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Musong Gu ◽  
Lei You ◽  
Jun Hu ◽  
Lintao Duan ◽  
Zhen Zuo

Wireless sensor networks (WSN) are applied in Intelligent Transport System for data collection. For the low redundancy rate of the wireless sensor networks nodes of traffic information collection, the senor nodes should be deployed reasonably for the WSN nodes to work effectively, and, thus, the base network structure and the density optimization of the sensor network are one of the main problems of WSN application. This paper establishes the wireless sensor networks design optimization model oriented to the traffic information collection, solving the design optimization model with the chemical reaction optimization (CRO) algorithm. The experimental results show that CRO algorithm outperforms the traditional particle swarm optimization (PSO) in solving the wireless sensor network design optimization oriented to the traffic information collection, capable of optimizing the wireless sensor network deployment of traffic information collection to contribute to the great improvement of the comprehensive value of the network performance. The reasonable design of the wireless sensor network nodes has great significance for the information collection, post-maintenance-and-extension, and cost saving of a monitoring system.


Author(s):  
Guohua Xiong

In order to solve the problem of traffic jams, intelligent traffic technology and car networking technology were applied. In the context of big data, data acquisition and mining algorithms for vehicular network were studied. First, the overall architecture of the system was introduced. Then, the data acquisition technology based on the car network and the data mining technology based on the cloud plat-form were introduced. Finally, simulation experiments of real-time traffic information collection and recognition algorithms were performed. The results showed that the proposed mining algorithm had better data repair effect and better clustering effect, and the probability of misjudgment was smaller. Therefore, the algorithm can obtain accurate road traffic conditions.


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