Calibrating Steady-State Traffic Stream and Car-Following Models Using Loop Detector Data

2010 ◽  
Vol 44 (2) ◽  
pp. 151-168 ◽  
Author(s):  
Hesham Rakha ◽  
Mazen Arafeh
Keyword(s):  
Steady State ◽  
Car Following ◽  
Loop Detector ◽  
Traffic Stream

Comparison of Greenshields, Pipes, and Van Aerde Car-Following and Traffic Stream Models

2002 ◽  
Vol 1802 (1) ◽  
pp. 248-262 ◽  
Author(s):  
Hesham Rakha ◽  
Brent Crowther
Keyword(s):  
Steady State ◽  
Simulation Models ◽  
Field Measurements ◽  
Calibration Procedure ◽  
Additional Degree ◽  
Car Following ◽  
Planning Models ◽  
Wide Range ◽  
Traffic Stream ◽  
Car Following Model

Three car-following models were compared: the Greenshields single-regime model, the Pipes two-regime model, and a four-parameter single-regime model that amalgamates both the Greenshields and Pipes models. The four-parameter model proposed by Van Aerde and Rakha is less known but is currently implemented in the INTEGRATION 2.30 software. The Greenshields and Pipes models were considered because they represent state-of-the-practice models for several types of microscopic and macroscopic software. The Greenshields model is widely used in macroscopic transportation planning models. In addition, the Pipes model is implemented in a number of microscopic traffic simulation models including CORSIM and VISSIM. Steady-state car-following behavior is also related to macroscopic traffic stream models to develop calibration procedures that can be achieved using macroscopic loop detector data. The study concluded that the additional degree of freedom that results from including a fourth parameter (Van Aerde model) overcomes the shortcomings of the current state-of-the-practice traffic stream models by capturing both macroscopic and microscopic steady-state traffic behavior for a wide range of roadway facilities and traffic conditions. Also developed was a procedure for calibrating the Pipes car-following model using macroscopic field measurements that can be obtained from loop detectors. Although this calibration procedure does not overcome the inherent shortcomings of the Pipes model, it does provide an opportunity to calibrate the CORSIM and VISSIM car-following behavior to existing roadway conditions more efficiently and without the need to collect microscopic traffic data.

Car-following theory of steady-state traffic flow using time-to-collision

2011 ◽  
Vol 12 (8) ◽  
pp. 645-654 ◽  
Author(s):  
Sheng Jin ◽  
Zhi-yi Huang ◽  
Peng-fei Tao ◽  
Dian-hai Wang
Keyword(s):  
Steady State ◽  
Traffic Flow ◽  
Time To Collision ◽  
Car Following

Car-Following Theory of Steady-State Traffic Flow

1959 ◽  
Vol 7 (4) ◽  
pp. 499-505 ◽  
Author(s):  
Denos C. Gazis ◽  
Robert Herman ◽  
Renfrey B. Potts
Keyword(s):  
Steady State ◽  
Traffic Flow ◽  
Car Following

scholarly journals Comparing the Effect of Age, Gender, and Desired Speed on Car-Following Behavior by Using Driving Simulator

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Ehsan Ramezani-Khansari ◽  
Masoud Tabibi ◽  
Fereidoon Moghadas Nejad ◽  
Mahmoud Mesbah
Keyword(s):  
Genetic Algorithm ◽  
Steady State ◽  
Driving Simulator ◽  
General Motors ◽  
Car Following ◽  
Time Headway ◽  
Age Factor ◽  
And Gender ◽  
Gender Factor ◽  
Effect Of Age

In this study, the effect of age, gender, and desired speed (DS) factors on General Motors car-following (CF) behavior was investigated. DS was defined as the speed selected by the driver in free driving situation. A low-level driving simulator was used to collect data. The CF model for each driver was calibrated by genetic algorithm. Gender and DS were effective in CF behavior, while the age factor was not. The drivers’ sensitivity to the variables of speed and distance in the CF model increased with increasing the DS. The gender factor affected only the magnitude of deceleration which was higher in women. For further investigation, the effect of the desired speed on the time headway in the steady-state CF was also examined. DS factor was effective in steady-state CF behavior. As the DS increased, the time headway decreased. Examining CF threshold demonstrated that women maintained larger distance than men. Finally, it can be said that DS and gender would be more important than age to be considered in CF models.

Conformity and stability analysis of a modified spring–mass–damper system dynamics-based car-following model

2019 ◽  
Vol 33 (06) ◽  
pp. 1950025 ◽  
Author(s):  
Caleb Ronald Munigety
Keyword(s):  
Stability Analysis ◽  
System Dynamics ◽  
Traffic System ◽  
Car Following ◽  
Proposed Model ◽  
Mass Damper ◽  
Traffic Stream ◽  
Behavioral Parameters ◽  
Car Following Model ◽  
The Stability

Modeling the dynamics of a traffic system involves using the principles of both physical and social sciences since it is composed of vehicles as well as drivers. A novel car-following model is proposed in this paper by incorporating the socio-psychological aspects of drivers into the dynamics of a purely physics-based spring–mass–damper mechanical system to represent the driver–vehicle longitudinal movements in a traffic stream. The crux of this model is that a traffic system can be viewed as various masses interacting with each other by means of springs and dampers attached between them. While the spring and damping constants represent the driver behavioral parameters, the mass component represents the vehicle characteristics. The proposed model when tested for its ability to capture the traffic system dynamics both at micro, driver, and macro, stream, levels behaved pragmatically. The stability analysis carried out using perturbation method also revealed that the proposed model is both locally and asymptotically stable.

An s-shaped three-parameter (S3) traffic stream model with consistent car following relationship

2021 ◽  
Vol 153 ◽  
pp. 246-271
Author(s):  
Qixiu Cheng ◽  
Zhiyuan Liu ◽  
Yuqian Lin ◽  
Xuesong (Simon) Zhou
Keyword(s):  
Car Following ◽  
Traffic Stream ◽  
Stream Model
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