scholarly journals Reasoning-predicting Model based on Strong Relevant Logic in Road Traffic Forecasting

2012 ◽  
Vol 33 ◽  
pp. 1105-1110 ◽  
Author(s):  
Dancheng Li ◽  
Zhiliang Liu ◽  
Cheng Liu ◽  
Binsheng Liu ◽  
Wei Zhang
Keyword(s):  
Road Traffic ◽  
Relevant Logic ◽  
Predicting Model ◽  
Traffic Forecasting ◽  
Model Based

Machine Learning and statistic predictive modeling for road traffic flow

2021 ◽  
Vol 03 (01) ◽  
pp. 17-24
Author(s):  
Nadia Slimani ◽  
Ilham Slimani ◽  
Nawal Sbiti ◽  
Mustapha Amghar
Keyword(s):  
Road Traffic ◽  
Smart Cities ◽  
Moving Average ◽  
Ann Model ◽  
Important Place ◽  
Traffic Forecasting ◽  
Absolute Relative Error ◽  
History Of ◽  
The One ◽  
Motorized Vehicles

Traffic forecasting is a research topic debated by several researchers affiliated to a range of disciplines. It is becoming increasingly important given the growth of motorized vehicles on the one hand, and the scarcity of lands for new transportation infrastructure on the other. Indeed, in the context of smart cities and with the uninterrupted increase of the number of vehicles, road congestion is taking up an important place in research. In this context, the ability to provide highly accurate traffic forecasts is of fundamental importance to manage traffic, especially in the context of smart cities. This work is in line with this perspective and aims to solve this problem. The proposed methodology plans to forecast day-by-day traffic stream using three different models: the Multilayer Perceptron of Artificial Neural Networks (ANN), the Seasonal Autoregressive Integrated Moving Average (SARIMA) and the Support Machine Regression (SMOreg). Using those three models, the forecast is realized based on a history of real traffic data recorded on a road section over 42 months. Besides, a recognized traffic manager in Morocco provides this dataset; the performance is then tested based on predefined criteria. From the experiment results, it is clear that the proposed ANN model achieves highest prediction accuracy with the lowest absolute relative error of 0.57%.

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

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.

Road Traffic Forecasting: Recent Advances and New Challenges

2018 ◽  
Vol 10 (2) ◽  
pp. 93-109 ◽  
Author(s):  
Ibai Lana ◽  
Javier Del Ser ◽  
Manuel Velez ◽  
Eleni I. Vlahogianni
Keyword(s):  
Road Traffic ◽  
Traffic Forecasting ◽  
Recent Advances ◽  
New Challenges
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