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

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.

2019 ◽  
Vol 33 (06) ◽  
pp. 1950025 ◽  
Author(s):  
Caleb Ronald Munigety

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.


Author(s):  
Lin Xiao ◽  
Meng Wang ◽  
Bart van Arem

Adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC) are important technologies for the achievement of vehicle automation, and their effect on traffic systems generally is evaluated with microscopic traffic simulations. A successful simulation requires realistic vehicle behavior and minimal vehicle collisions. However, most existing ACC-CACC simulation studies used simplified models that were not based on real vehicle response. The studies rarely addressed collision avoidance in the simulation. The study presented in this paper developed a realistic and collision-free car-following model for ACC-CACC vehicles. A multiregime model combining a realistic ACC-CACC system with driver intervention for vehicle longitudinal motions is proposed. This model assumes that a human driver resumes vehicle control either according to his or her assessment or after a collision warning asks the driver to take over. The proposed model was tested in a wide range of scenarios to explore model performance and collision possibilities. The testing scenarios included three regular scenarios of stop-and-go, approaching, and cut-out maneuvers, as well as two extreme safety-concerned maneuvers of hard brake and cut-in. The simulation results show that the proposed model is collision free in the full-speed-range operation with leader accelerations within −1 to 1 m/s2 and in approaching and cut-out scenarios. Those results indicate that the proposed ACC-CACC car-following model can produce realistic vehicle response without causing vehicle collisions in regular scenarios for vehicle string operations.


2007 ◽  
Vol 1999 (1) ◽  
pp. 115-127 ◽  
Author(s):  
Hesham Rakha ◽  
Caroline Cavagni Pecker ◽  
Helena Beatriz Bettella Cybis

2021 ◽  
Vol 1 (3) ◽  
pp. 443-465
Author(s):  
Kaveh Bevrani ◽  
Edward Chung ◽  
Pauline Teo

Traffic safety studies need more than what the current micro-simulation models can provide, as they presume that all drivers exhibit safe behaviors. Therefore, existing micro-simulation models are inadequate to evaluate the safety impacts of managed motorway systems such as Variable Speed Limits. All microscopic traffic simulation packages include a core car-following model. This paper highlights the limitations of the existing car-following models to emulate driver behaviour for safety study purposes. It also compares the capabilities of the mainstream car-following models, modelling driver behaviour with precise parameters such as headways and time-to-collisions. The comparison evaluates the robustness of each car-following model for safety metric reproductions. A new car-following model, based on the personal space concept and fish school model is proposed to simulate more accurate traffic metrics. This new model is capable of reflecting changes in the headway distribution after imposing the speed limit from variable speed limit (VSL) systems. This model can also emulate different traffic states and can be easily calibrated. These research findings facilitate assessing and predicting intelligent transportation systems effects on motorways, using microscopic simulation.


2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Mudasser Seraj ◽  
Jiangchen Li ◽  
Zhijun Qiu

Microscopic detail of complex vehicle interactions in mixed traffic, involving manual driving system (MDS) and automated driving system (ADS), is imperative in determining the extent of response by ADS vehicles in the connected automated vehicle (CAV) environment. In this context, this paper proposes a naïve microscopic car-following strategy for a mixed traffic stream in CAV settings and specified shifts in traffic mobility, safety, and environmental features. Additionally, this study explores the influences of platoon properties (i.e., intra-platoon headway, inter-platoon headway, and maximum platoon length) on traffic stream characteristics. Different combinations of MDS and ADS vehicles are simulated in order to understand the variations of improvements induced by ADS vehicles in a traffic stream. Simulation results reveal that grouping ADS vehicles at the front of traffic stream to apply Cooperative Adaptive Cruise Control (CACC) based car-following model will generate maximum mobility benefits for upstream vehicles. Both mobility and environmental improvements can be realized by forming long, closely spaced ADS vehicles at the cost of reduced safety. To achieve balanced mobility, safety, and environmental advantages from mixed traffic environment, dynamically optimized platoon configurations should be determined at varying traffic conditions and ADS market penetrations.


2018 ◽  
Vol 47 (2) ◽  
pp. 146-156 ◽  
Author(s):  
Ioulia Markou ◽  
Vasileia Papathanasopoulou ◽  
Constantinos Antoniou

Calibration plays a fundamental role in successful applications of traffic simulation and Intelligent Transportation Systems. In this research, the calibration of car–following models is seen as a dynamic problem, which is solved at each individual time–step. The optimization of model parameters is fulfilled using the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. The output of the optimization is a distribution of parameter values, capturing a wide range of various traffic conditions. The methodology is demonstrated via a case study, where the proposed framework is implemented for the dynamic calibration of the car–following model used in the TransModeler traffic simulation model and Gipps′ model. This method results to model parameter distributions, which are superior to simply using point parameter values, as they are more realistic, capturing the heterogeneity of driver behavior. Flexibility is thus introduced into the calibration process and restrictions generated by conventional calibration methods are relaxed.


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