Traffic Jam Prediction through Elman Neural Network Based on Monte Carlo Simulation

2014 ◽  
Vol 551 ◽  
pp. 675-678 ◽  
Author(s):  
Ze Yuan Yu
Keyword(s):  
Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Traffic Flow ◽  
Cross Section ◽  
Queue Length ◽  
Elman Neural Network ◽  
Traffic Jam ◽  
Quick Recovery

This paper simulates the traffic flow at upper stream intersection and the actual traffic flow at cross section of accident, and calculates the time for the queue to reach the upper stream intersection through Elman neural network. The result of 500 simulations shows that the probability for the time of the queue length being 140m: in [2.5min~3.5min] is 39.6% and in [3.5min~4min] is 52.0%. The total is 91.6%, which is highly precise. The prediction of queue length of post-accident traffic jam is of great importance to a quick recovery.

A Traffic Capacity Model of Lane Occupation

2014 ◽  
Vol 599-601 ◽  
pp. 2083-2087
Author(s):  
Yi Xuan He
Keyword(s):  
Traffic Flow ◽  
Queue Length ◽  
Production Condition ◽  
Modern Society ◽  
Traffic Jam ◽  
Traffic Flow Theory ◽  
Related Data ◽  
Traffic Capacity ◽  
Capacity Model ◽  
Big City

In modern society, traffic jam has already become a major problem which curbs the development of big city. And lane occupation is an important reason why traffic jam happens. After studying on the production condition, time and queue length of traffic jam after lane occupation happens, we propose a model based on famous traffic flow theory and we use related data to verify the rightness of our model. Result shows that our model can predict the development of traffic jam caused by lane occupation

scholarly journals Investigating the Electronic Coefficients in Ne-Hg Mixtures: Comparing a Monte Carlo and Artificial Neural Network Model

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Rafid Abbas Ali ◽  
Faten Sajet Mater ◽  
Asmaa Satar Jeeiad Al-Ragehey
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Network Architecture ◽  
Vapor Concentration ◽  
Neural Network Architecture ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
New Applications

Electron coefficients such as drift velocity, ionization coefficient, mean electron energy and Townsend energy for different concentrations of Hg 0.1%, 1%, 10% and 50% in the Ne-Hg mixture at a reduced electric field were calculated using two approaches taking into account inelastic collisions: The Monte Carlo simulation, and an artificial neural network. The effect of Hg vapor concentration on the electron coefficients showed that insignificant additions of mercury atom impurities to Neon, starting from fractions of a percent, affect the characteristics of inelastic processes and discharge, respectively. The aim of this paper is to explore the new applications of neural networks. The Levenberg-Marquardt algorithm and artificial neural network architecture employed was presented in this work to calculate the electron coefficients for different concentrations of Hg in Ne-Hg mixtures. The artificial neural network has been trained with four models (M1, M2, M3, M4), and analysis of the regression between the values of an artificial neural network and Monte Carlo simulation indicates that the M2 output provided the best perfect correlation at 100 Epochs, and the output data obtained was closest to the target data required through using the different stages of artificial neural network development starting with design, training and testing.

scholarly journals Predicting and Visualizing the Uncertainty Propagations in Traffic Assignments Model Using Monte Carlo Simulation Method

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Mundher Seger ◽  
Lajos Kisgyörgy
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Traffic Flow ◽  
Travel Demand ◽  
Simulation Method ◽  
Transport Network ◽  
Monte Carlo Simulation Method ◽  
Traffic Flows ◽  
Demand Model ◽  
Travel Demand Model

Uncertainty can be found at all stages of travel demand model, where the error is passing from one stage to another and propagating over the whole model. Therefore, studying the uncertainty in the last stage is more important because it represents the result of uncertainty in the travel demand model. The objective of this paper is to assist transport modellers in perceiving uncertainty in traffic assignment in the transport network, by building a new methodology to predict the traffic flow and compare predicted values to the real values or values calculated in analytical methods. This methodology was built using Monte Carlo simulation method to quantify uncertainty in traffic flows on a transport network. The values of OD matrix were considered as stochastic variables following a specific probability distribution. And, the results of the simulation process represent the predicted traffic flows in each link on the transport network. Consequently, these predicted results are classified into four cases according to variability and bias. Finally, the results are drawn into figures to visualize the uncertainty in traffic assignments. This methodology was applied to a case study using different scenarios. These scenarios are varying according to inputs parameters used in MC simulation. The simulation results for the scenarios gave different bias for each link separately according to the physical feature of the transport network and original OD matrix, but in general, there is a direct relationship between the input parameter of standard deviation with the bias and variability of the predicted traffic flow for all scenarios.

General Regression Neural Network and Monte Carlo Simulation Model for Survival and Growth of Salmonella on Raw Chicken Skin as a Function of Serotype, Temperature, and Time for Use in Risk Assessment†

2009 ◽  
Vol 72 (10) ◽  
pp. 2078-2087 ◽  
Author(s):  
THOMAS P. OSCAR
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
General Regression Neural Network ◽  
Chicken Skin ◽  
Survival And Growth ◽  
Fail Safe ◽  
General Regression ◽  
Monte Carlo Simulation Model

A general regression neural network (GRNN) and Monte Carlo simulation model for predicting survival and growth of Salmonella on raw chicken skin as a function of serotype (Typhimurium, Kentucky, and Hadar), temperature (5 to 50°C), and time (0 to 8 h) was developed. Poultry isolates of Salmonella with natural resistance to antibiotics were used to investigate and model survival and growth from a low initial dose (<1 log) on raw chicken skin. Computer spreadsheet and spreadsheet add-in programs were used to develop and simulate a GRNN model. Model performance was evaluated by determining the percentage of residuals in an acceptable prediction zone from −1 log (fail-safe) to 0.5 log (fail-dangerous). The GRNN model had an acceptable prediction rate of 92% for dependent data (n = 464) and 89% for independent data (n = 116), which exceeded the performance criterion for model validation of 70% acceptable predictions. Relative contributions of independent variables were 16.8% for serotype, 48.3% for temperature, and 34.9% for time. Differences among serotypes were observed, with Kentucky exhibiting less growth than Typhimurium and Hadar, which had similar growth levels. Temperature abuse scenarios were simulated to demonstrate how the model can be integrated with risk assessment, and the most common output distribution obtained was Pearson5. This study demonstrated that it is important to include serotype as an independent variable in predictive models for Salmonella. Had a cocktail of serotypes Typhimurium, Kentucky, and Hadar been used for model development, the GRNN model would have provided overly fail-safe predictions of Salmonella growth on raw chicken skin contaminated with serotype Kentucky. Thus, by developing the GRNN model with individual strains and then modeling growth as a function of serotype prevalence, more accurate predictions were obtained.

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