A Parameter Identification Scheme for Second-Order Highway Traffic Model Based on Differential Algebraic Methodology

2013 ◽  
pp. 85-93
Author(s):  
Nan Li ◽  
Guangzhou Zhao
Keyword(s):  
Parameter Identification ◽  
Second Order ◽  
Traffic Model ◽  
Highway Traffic ◽  
Identification Scheme ◽  
Model Based

Research and Improvement of the Keep-Right-Except-to-Pass Rule

2014 ◽  
Vol 915-916 ◽  
pp. 459-463
Author(s):  
He Quan Zhang
Keyword(s):  
Fluid Mechanics ◽  
Traffic Flow ◽  
Influence Factors ◽  
Traffic Model ◽  
Light Traffic ◽  
The Road ◽  
Model Based ◽  
Light Passing ◽  
Improved Model ◽  
The Impact

In order to deal with the impact on traffic flow of the rule, we compare the influence factors of traffic flow (passing, etc.) into viscous resistance of fluid mechanics, and establish a traffic model based on fluid mechanics. First, in heavy and light traffic, we respectively use this model to simulate the actual segment of the road and find that when the traffic is heavy, the rule hinder the further increase in traffic. For this reason, we make further improvements to the model to obtain a fluid traffic model based on no passing and find that the improved model makes traffic flow increase significantly. Then, the improved model is applied to the light traffic, we find there are no significant changes in traffic flow .In this regard we propose a new rule: when the traffic is light, passing is allowed, but when the traffic is heavy, passing is not allowed.

mparison of model-based identification methods for reserve-capacity assessment of existing bridges

2018 ◽  
Author(s):  
Marco Proverbio ◽  
François-Xavier Favre ◽  
Ian F. C. Smith
Keyword(s):  
Parameter Identification ◽  
Structural Identification ◽  
Domain Model ◽  
Reserve Capacity ◽  
Steel Bridge ◽  
Model Based ◽  
Wrong Identification ◽  
Alternative Methodology ◽  
Existing Bridges ◽  
Residual Minimization

The goal of model-based structural identification is to find suitable values of parameters that affect structure behaviour. To this end, measurements are often compared with predictions of finiteelement models. Although residual minimization (RM) is a prominent methodology for structural identification, it provides wrong parameter identification when flawed model classes are adopted. Error-domain model falsification (EDMF) is an alternative methodology that helps identify candidate models – models that are compatible with behaviour measurements – among an initial model population. This study focuses on the comparison between RM and EDMF for the structural identification of a steel bridge in Exeter (UK). Advantages and limitations of both methodologies are discussed with reference to parameter identification and prognosis tasks such as quantification of reserve capacity. Results show that the employment of RM may lead to wrong identification and unsafe estimations of reserve capacity.

A Highway Traffic Model

1975 ◽  
Vol 12 (S1) ◽  
pp. 303-309
Author(s):  
Herbert Solomon
Keyword(s):  
Poisson Process ◽  
Poisson Distribution ◽  
Random Measure ◽  
Traffic Model ◽  
Highway Traffic ◽  
Intensity Parameter ◽  
Time Space ◽  
Random Line ◽  
Straight Line ◽  
Poisson Fields

The trajectory of a car traveling at a constant speed on an idealized infinite highway can be viewed as a straight line in the time-space plane. Entry times are governed by a Poisson process with intensity parameter A leading to all trajectories as random lines in a plane. The Poisson distribution of number of encounters of cars on the highway is developed through random line models and non-homogeneous Poisson fields, and its parameter, which depends on the specific random measure employed, is obtained explicitly.

scholarly journals ONLINE PARAMETER IDENTIFICATION OF RICE TRANSPLANTER MODEL BASED ON IPSO-EKF ALGORITHM

2020 ◽  
Vol 61 (2) ◽  
pp. 25-34 ◽  
Author(s):  
Yibo Li ◽  
Hang Li ◽  
Xiaonan Guo
Keyword(s):  
Parameter Identification ◽  
Measured Data ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Output Data ◽  
Swarm Optimization ◽  
Model Based ◽  
Simulation Results ◽  
Filtering Effect ◽  
Ipso Algorithm

In order to improve the accuracy of rice transplanter model parameters, an online parameter identification algorithm for the rice transplanter model based on improved particle swarm optimization (IPSO) algorithm and extended Kalman filter (EKF) algorithm was proposed. The dynamic model of the rice transplanter was established to determine the model parameters of the rice transplanter. Aiming at the problem that the noise matrices in EKF algorithm were difficult to select and affected the best filtering effect, the proposed algorithm used the IPSO algorithm to optimize the noise matrices of the EKF algorithm in offline state. According to the actual vehicle tests, the IPSO-EKF was used to identify the cornering stiffness of the front and rear tires online, and the identified cornering stiffness value was substituted into the model to calculate the output data and was compared with the measured data. The simulation results showed that the accuracy of parameter identification for the rice transplanter model based on the IPSO-EKF algorithm was improved, and established an accurate rice transplanter model.

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