Prediction Simulation Study of Road Traffic Carbon Emission Based on Chaos Theory and Neural Network

A full methodology of short-term traffic prediction is proposed for urban road traffic network via Artificial Neural Network (ANN). The goal of the forecasting is to provide speed estimation forward by 5, 15 and 30 min. Unlike similar research results in this field, the investigated method aims to predict traffic speed for signalized urban road links and not for highway or arterial roads. The methodology contains an efficient feature selection algorithm in order to determine the appropriate input parameters required for neural network training. As another contribution of the paper, a built-in incomplete data handling is provided as input data (originating from traffic sensors or Floating Car Data (FCD)) might be absent or biased in practice. Therefore, input data handling can assure a robust operation of speed forecasting also in case of missing data. The proposed algorithm is trained, tested and analysed in a test network built-up in a microscopic traffic simulator by using daily course of real-world traffic.

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Intelligent Method for Identifying Driving Risk Based on V2V Multisource Big Data

Complexity ◽

10.1155/2018/1801273 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Jinshuan Peng ◽

Yiming Shao

Keyword(s):

Neural Network ◽

Traffic Accidents ◽

Road Traffic ◽

Time Window ◽

Driving Behavior ◽

Practical Significance ◽

Accurate Identification ◽

Risky Driving ◽

Bayesian Filter ◽

Identification Rate

Risky driving behavior is a major cause of traffic conflicts, which can develop into road traffic accidents, making the timely and accurate identification of such behavior essential to road safety. A platform was therefore established for analyzing the driving behavior of 20 professional drivers in field tests, in which overclose car following and lane departure were used as typical risky driving behaviors. Characterization parameters for identification were screened and used to determine threshold values and an appropriate time window for identification. A neural network-Bayesian filter identification model was established and data samples were selected to identify risky driving behavior and evaluate the identification efficiency of the model. The results obtained indicated a successful identification rate of 83.6% when the neural network model was solely used to identify risky driving behavior, but this could be increased to 92.46% once corrected by the Bayesian filter. This has important theoretical and practical significance in relation to evaluating the efficiency of existing driver assist systems, as well as the development of future intelligent driving systems.

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A Study on Sustainable Consumption of Fuel—An Estimation Method of Aircraft

Energies ◽

10.3390/en14227559 ◽

2021 ◽

Vol 14 (22) ◽

pp. 7559

Author(s):

Lisha Li ◽

Shuming Yuan ◽

Yue Teng ◽

Jing Shao

Keyword(s):

Neural Network ◽

Fuel Consumption ◽

Influencing Factors ◽

Carbon Emission ◽

Estimation Method ◽

Civil Aviation ◽

Accurate Estimation ◽

Low Carbon ◽

Fuel Price ◽

Aircraft Fuel

Though the development of China’s civil aviation and the improvement of control ability have strengthened the safety operation and support ability effectively, the airlines are under the pressure of operation costs due to the increase of aircraft fuel price. With the development of optimization controlling methods in flight management systems, it becomes increasingly challenging to cut down flight fuel consumption by control the flight status of the aircraft. Therefore, the airlines both at home and abroad mainly rely on the accurate estimation of aircraft fuel to reduce fuel consumption, and further reduce its carbon emission. The airlines have to take various potential factors into consideration and load more fuel to cope with possible negative situation during the flight. Therefore, the fuel for emergency use is called PBCF (Performance-Based Contingency Fuel). The existing PBCF forecasting method used by China Airlines is not accurate, which fails to take into account various influencing factors. This paper aims to find a method that could predict PBCF more accurately than the existing methods for China Airlines.This paper takes China Eastern Airlines as an example. The experimental data of flight fuel of China Eastern Airlines Co, Ltd. were collected to find out the relevant parameters affecting the fuel consumption, which is followed by the establishment of the LSTM neural network through the parameters and collected data. Finally, through the established neural network model, the PBCF addition required by the airline with different influencing factors is output. It can be seen from the results that the all the four models are available for the accurate prediction of fuel consumption. The amount of data of A319 is much larger than that of A320 and A330, which leads to higher accuracy of the model trained by A319. The study contributes to the calculation methods in the fuel-saving project, and helps the practitioners to learn about a particular fuel calculation method. The study brought insights for practitioners to achieve the goal of low carbon emission and further contributed to their progress towards circular economy.

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Recognition Method of Drinking-driving Behaviors Based on PCA and RBF Neural Network

PROMET - Traffic&Transportation ◽

10.7307/ptt.v30i4.2657 ◽

2018 ◽

Vol 30 (4) ◽

pp. 407-417

Author(s):

Yifan Sun ◽

Jinglei Zhang ◽

Xiaoyuan Wang ◽

Zhangu Wang ◽

Jie Yu

Keyword(s):

Neural Network ◽

Traffic Safety ◽

Road Traffic ◽

Rbf Neural Network ◽

Principal Component ◽

Physiological Data ◽

Recognition Model ◽

Driving Behaviors ◽

Model Based ◽

Safety Research

Drinking-driving behaviors are important causes of road traffic injuries, which are serious threats to the lives and property of traffic participants. Therefore, reducing the occurrences of drinking-driving behaviors has become an important problem of traffic safety research. Forty-eight male drivers and six female drivers who could drink moderate alcohol were chosen as participants. The drivers’ physiological data, operation behavior data, car running data, and driving environment data were collected by designing various virtual traffic scenes and organizing drivers to conduct driving simulation experiments. The original variables were analyzed by the Principal Component Analysis (PCA), and seven principal components were extracted as the input vector of the Radial Basis Function (RBF) neural network. The principal component data was used to train and verify the RBF neural network. The Levenberg-Marquardt (LM) algorithm was chosen to train the parameters of the neural network and build a drinking-driving recognition model based on PCA and RBF neural network to realize an accurate recognition of drinking-driving behaviors. The test results showed that the drinking-driving recognition model based on PCA and RBF neural network could identify drinking drivers accurately during driving process with a recognition accuracy of 92.01%, and the operation efficiency of the model was high. The research can provide useful reference for prevention and treatment of drinking and driving and traffic safety maintenance.

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