Traffic Prediction System Utilizing Application and Control of Environmental Information

2017 ◽  
pp. 1043-1050
Author(s):  
Yonghoon Kim ◽  
Mokdong Chung
Keyword(s):  
Environmental Information ◽  
Traffic Prediction ◽  
Prediction System ◽  
And Control

scholarly journals Innovative Design of Intelligent Detection Equipment for Growth Information of Facilityhorticultural Crops

2020 ◽  
Vol 11 ◽  
pp. 79-88
Author(s):  
Yu Qiang Ruan ◽  
Xiao Dong Zhang ◽  
Hanping Mao ◽  
Hong Yan Gao ◽  
Xin Zhang ◽  
...  
Keyword(s):  
Near Infrared ◽  
Detection System ◽  
Scientific Basis ◽  
Crop Growth ◽  
Environmental Information ◽  
Growth Stages ◽  
Information Monitoring ◽  
Intelligent Detection ◽  
And Control ◽  
Sensor Detection

 Intelligent equipment technology for facility horticulture is an urgent need for the development of modern facility agriculture.The intelligent monitoring equipment for greenhouse crop growth information can comprehensively monitor the nutrition, growth and environmental information of crops, and provide a scientific basis for the optimal regulation and control of water, fertilizer and environment in the greenhouse. It is a key equipment for the intelligentization of facility horticulture. This research aims at different growth stages In accordance with the testing needs of different plant-shaped crops and the operational needs of the unstructured environment in the greenhouse, we developed wheeled and tracked crop growth and environmental information monitoring systems that can autonomously cruise in the greenhouse;at the same time, in order to meet the detection needs of large-plant crops, a cantilever type crop information monitoring system has also been developed. This system suspends the multi-sensor detection system through the gimbal and installs it on the orbit track laid on the greenhouse truss. Because the detection position is high, it is realized the cruise monitor of greenhouse plants such as cucumber and tomato. In order to achieve comprehensive detection of crop growth information, a multi-sensor detection system for horticultural crop information has been developed. It uses visible-near-infrared binocular multi-spectral cameras, infrared detection sensors, laser ranging sensors, ambient temperature and humidity and light sensors. through the multiple sensor information fusion, implements the facilities horticulture crops nutrition, growth and the comprehensive monitoring of environmental information. Good application effect has been achieved.

scholarly journals A New Way of Airline Traffic Prediction Based on GCN-LSTM

2021 ◽  
Vol 15 ◽  
Author(s):  
Jiangni Yu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Air Traffic ◽  
Traffic Prediction ◽  
Long Term Memory ◽  
Prediction System ◽  
Short Term ◽  
Prediction Ability ◽  
Memory Network

With the development of society and the improvement of people's material level, more and more people like to travel by airplane. If we can predict the passenger flow of an airline in advance, it can be used as an important decision-making basis for its flight route planning, crew scheduling planning and ticket price formulation in the process of management and operation. However, due to the high complexity of aviation network, the existing traffic prediction methods generally have the problem of low prediction accuracy. In order to overcome this problem, this paper makes full use of graph convolutional neural network and long—short memory network to construct a prediction system with short—term prediction ability. Specifically, this paper uses the graph convolutional neural network as a feature extraction tool to extract the key features of air traffic data, and solves the problem of long term and short term dependence between data through the long term memory network, then we build a high-precision air traffic prediction system based on it. Finally, we design a comparison experiment to compare the algorithm with the traditional algorithms. The results show that the algorithm we proposed in this paper has a higher accuracy in air flow prediction according to the lower loss function value.

Statistical Evaluation of Interstate 4 Traffic Prediction System

2003 ◽  
Vol 1856 (1) ◽  
pp. 16-24 ◽  
Author(s):  
Sherif Ishak ◽  
Haitham Al-Deek
Keyword(s):  
Traffic Management ◽  
Traffic Prediction ◽  
Model Parameters ◽  
Prediction Errors ◽  
Prediction System ◽  
Traveler Information ◽  
Planning Stage ◽  
Traffic Conditions ◽  
Wide Range ◽  
Prediction Systems

Short-term traffic prediction systems have received considerable attention in the past few years as a means to support advanced traveler information and traffic management systems. Predictive information allows transportation system users to make better trip decisions at the pretrip planning stage and en route. A comprehensive statistical analysis of the traffic prediction system performance implemented on the 40-mi corridor of Interstate 4 in Orlando, Florida, is presented. The system was evaluated under a wide range of traffic conditions and various model parameters. The prediction performance in terms of prediction errors was examined with both link-based and path-based approaches.

The Traffic Prediction and Control Based on Rough Set Theory

2013 ◽  
Vol 756-759 ◽  
pp. 632-635 ◽  
Author(s):  
Ai Long Fan
Keyword(s):  
Traffic Flow ◽  
Rough Set ◽  
Traffic Control ◽  
Rough Set Theory ◽  
Fuzzy Theory ◽  
Research Priorities ◽  
Traffic Prediction ◽  
Prediction And Control ◽  
Traffic Guidance ◽  
And Control

Intelligent traffic control and traffic guidance systems have become the core question of ITS research, but real-time and accurate traffic prediction and control are two keys that they may achieve. On this basis, Research priorities are proposed and it introduces general ideas of short-term traffic flow forecasting and control, and uses the rough set and fuzzy theory to predict and control traffic flow. Compared with the actual, forecasting results' error is smaller, and the same time the jam of intersection is effectively alleviated.

A Matlab-Based Traffic Prediction System

2016 ◽  
Author(s):  
Yi-Chung Chen ◽  
Yin-Wei Lin ◽  
Ming-Yang Lu ◽  
Yuan-Tien Wang
Keyword(s):  
Traffic Prediction ◽  
Prediction System
Sign in / Sign up

Export Citation Format

Share Document