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

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.

scholarly journals A Novel Traffic Stream Model Deriving from a Bimodal Kinetic Equilibrium 1

1997 ◽  
Vol 30 (8) ◽  
pp. 771-776 ◽  
Author(s):  
Paul Nelson ◽  
Dat Duc Bui ◽  
Alexandros Sopasakis
Keyword(s):  
Traffic Stream ◽  
Stream Model ◽  
Kinetic Equilibrium

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.

scholarly journals Stochastic Model of Ships Traffic Capacity and Congestion — Validation by Real Ships Traffic Data on Świnoujście — Szczecin Waterway

2017 ◽  
Vol 24 (1) ◽  
pp. 177-191 ◽  
Author(s):  
Lucjan Gucma ◽  
Andrzej Bąk ◽  
Sylwia Sokołowska
Keyword(s):  
Monte Carlo ◽  
Stochastic Model ◽  
Main Parameter ◽  
Real Data ◽  
Model Output ◽  
Traffic Data ◽  
Traffic Capacity ◽  
Traffic Stream ◽  
Separate Object ◽  
Stream Model

AbstractPaper presents validation of previously created stochastic ships traffic stream model by the real data of ships delays on Świnoujście — Szczecin waterway. The model is mostly based on Monte Carlo methodology. The model is microscopic which means that each ship’s model is treated as separate object possessing given attributes. As the main parameter of presented validation total waiting (delay) time of ships have been applied. The time of ships delays was possessed from Szczecin VTS centre and compared with the model output.

scholarly journals Calibration of the empirical fundamental relationship using very large databases

TRANSPORTES ◽  
2021 ◽  
Vol 29 (1) ◽  
pp. 212-228
Author(s):  
Juliana Mitsuyama Cardoso ◽  
Lucas Assirati ◽  
José Reynaldo Setti
Keyword(s):  
Model Fitting ◽  
Traffic Monitoring ◽  
Metropolitan Region ◽  
Model Parameters ◽  
Inaccurate Data ◽  
Fundamental Relationship ◽  
Large Databases ◽  
Traffic Stream ◽  
Very Large Databases ◽  
Stream Model

This paper describes a procedure for fitting traffic stream models using very large traffic databases. The proposed approach consists of four steps: (1) an initial treatment to eliminate noisy, inaccurate data and to homogenize the information over the density range; (2) a first fitting of the model, based on the sum of squared orthogonal errors; (3) a second filter, to eliminate outliers that survived the initial data treatment; and (4) a second fitting of the model. The proposed approach was tested by fitting the Van Aerde traffic stream model to 104 thousand observations collected by a permanent traffic monitoring station on a freeway in the metropolitan region of São Paulo, Brazil. The model fitting used a genetic algorithm to search for the best values of the model parameters. The results demonstrate the effectiveness of the proposed approach.

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