A spring-mass-damper system dynamics-based driver-vehicle integrated model for representing heterogeneous traffic

The traditional traffic microscopic simulation models consider driver and vehicle as a single unit to represent the movements of drivers in a traffic stream. Due to this very fact, the traditional car-following models have the driver behavior related parameters, but ignore the vehicle related aspects. This approach is appropriate for homogeneous traffic conditions where car is the major vehicle type. However, in heterogeneous traffic conditions where multiple vehicle types are present, it becomes important to incorporate the vehicle related parameters exclusively to account for the varying dynamic and static characteristics. Thus, this paper presents a driver-vehicle integrated model hinged on the principles involved in physics-based spring-mass-damper mechanical system. While the spring constant represents the driver’s aggressiveness, the damping constant and the mass component take care of the stability and size/weight related aspects, respectively. The proposed model when tested, behaved pragmatically in representing the vehicle-type dependent longitudinal movements of vehicles.

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

International Journal of Modern Physics B ◽

10.1142/s0217979219500255 ◽

2019 ◽

Vol 33 (06) ◽

pp. 1950025 ◽

Cited By ~ 3

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.

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Vehicle-Type Dependent Car-Following Model for Heterogeneous Traffic Conditions

Journal of Transportation Engineering ◽

10.1061/(asce)te.1943-5436.0000273 ◽

2011 ◽

Vol 137 (11) ◽

pp. 775-781 ◽

Cited By ~ 26

Author(s):

K. V. R. Ravishankar ◽

Tom V. Mathew

Keyword(s):

Heterogeneous Traffic ◽

Car Following ◽

Vehicle Type ◽

Traffic Conditions ◽

Car Following Model

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Influence of Autonomous Vehicles on Car-Following Behavior of Human Drivers

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119862628 ◽

2019 ◽

Vol 2673 (12) ◽

pp. 367-379 ◽

Cited By ~ 2

Author(s):

Yalda Rahmati ◽

Mohammadreza Khajeh Hosseini ◽

Alireza Talebpour ◽

Benjamin Swain ◽

Christopher Nelson

Keyword(s):

Autonomous Vehicles ◽

Simulation Models ◽

Driving Behavior ◽

Data Driven ◽

Microscopic Simulation ◽

Car Following ◽

The Road ◽

Human Driver ◽

Significant Difference ◽

On The Road

Despite numerous studies on general human–robot interactions, in the context of transportation, automated vehicle (AV)–human driver interaction is not a well-studied subject. These vehicles have fundamentally different decision-making logic compared with human drivers and the driving interactions between AVs and humans can potentially change traffic flow dynamics. Accordingly, through an experimental study, this paper investigates whether there is a difference between human–human and human–AV interactions on the road. This study focuses on car-following behavior and conducted several car-following experiments utilizing Texas A&M University’s automated Chevy Bolt. Utilizing NGSIM US-101 dataset, two scenarios for a platoon of three vehicles were considered. For both scenarios, the leader of the platoon follows a series of speed profiles extracted from the NGSIM dataset. The second vehicle in the platoon can be either another human-driven vehicle (scenario A) or an AV (scenario B). Data is collected from the third vehicle in the platoon to characterize the changes in driving behavior when following an AV. A data-driven and a model-based approach were used to identify possible changes in driving behavior from scenario A to scenario B. The findings suggested there is a statistically significant difference between human drivers’ behavior in these two scenarios and human drivers felt more comfortable following the AV. Simulation results also revealed the importance of capturing these changes in human behavior in microscopic simulation models of mixed driving environments.

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Capacity Estimation of Multilane Highways under Heterogeneous Traffic Conditions using Micro-Simulation Technique

SAMRIDDHI A Journal of Physical Sciences Engineering and Technology ◽

10.18090/samriddhi.v12i01.4 ◽

2020 ◽

Vol 12 (01) ◽

pp. 20-25

Author(s):

Tanumoy Ghosh ◽

Sudip Kumar Roy ◽

Subhamay Gangopadhyay

Keyword(s):

Simulation Model ◽

Traffic Flow ◽

Flow Characteristics ◽

Simulation Models ◽

Heterogeneous Traffic ◽

Traffic Condition ◽

Traffic Conditions ◽

Geometric Conditions ◽

Micro Simulation ◽

Traffic Flow Characteristics

The behavior of a driver of any vehicle is important in estimating heterogeneous traffic conditions with no strict lane discipline. In the present study, a micro-simulation model is used to analyze the mixed traffic condition with different drivers’ behavior parameters. The field data collected on traffic flow characteristics of multilane highways are used in the calibration and validation of the simulation model. Out of the ten coefficient of correlation (CC) parameters in the simulation model, five are used in the present study to make a model of simulation for heterogeneous traffic; the other five parameters are not considered for testing their influence on simulated capacity values as they represent very typical behavior of a driver, either in car-following, or in free-flow conditions. Two separate simulation models are made by changing the CC (CC0, CC1, CC2, CC7, and CC8) parameters, each for a four-lane divided and a six-lane divided highway as the geometric conditions of the roads and the traffic flow is different for both the cases. These models are then applied on two other sections of a four-lane divided and a six-lane divided highway to validate the parameters of the model developed earlier for other sections.

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Stability Analysis of Train Movement with Uncertain Factors

Mathematical Problems in Engineering ◽

10.1155/2015/230616 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

JingJing Ye ◽

KePing Li ◽

XueDong Jiang

Keyword(s):

Relaxation Time ◽

Stability Analysis ◽

Fuzzy Theory ◽

Traffic Model ◽

Optimal Velocity ◽

Car Following ◽

Proposed Model ◽

Car Following Model ◽

Simulation Results ◽

The Stability

We propose a new traffic model which is based on the traditional OV (optimal velocity) car-following model. Here, some realistic factors are regarded as uncertain quantity, such as the headway distance. Our aim is to analyze and discuss the stability of car-following model under the constraint of uncertain factors. Then, according to the principle of expected value in fuzzy theory, an improved OV traffic model is constructed. Simulation results show that our proposed model can avoid collisions effectively under uncertain environment, and its stability can also be improved. Moreover, we discuss its stability as some parameters change, such as the relaxation time.

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A NEW DELAY PARAMETER DEPENDENT ON VARIABLE ANALYSIS PERIODS AT SIGNALIZED INTERSECTIONS. PART 2: VALIDATION AND APPLICATION

Transport ◽

10.3846/1648-4142.2008.23.91-94 ◽

2008 ◽

Vol 23 (2) ◽

pp. 91-94 ◽

Cited By ~ 8

Author(s):

Ali Payidar Akgüngör

Keyword(s):

Linear Regression Analysis ◽

Signalized Intersections ◽

Delay Model ◽

Microscopic Simulation ◽

Good Relationship ◽

Traffic Conditions ◽

Proposed Model ◽

Calibration And Verification ◽

Parameter Dependent ◽

Variable Analysis

The main objective of the present study is to investigate the performance of the proposed model in Part 1 for variable demand, time and oversaturated conditions. To accomplish this objective and test the proposed model, an experimental study was performed. The proposed delay model for oversaturated traffic conditions was calibrated and verified by the TRAF‐NETSIM microscopic simulation program. In the calibration and verification of the proposed model, the simulation study was performed to produce various traffic and time conditions using 48 different scenarios. The delays obtained from the simulations and the proposed model were statistically compared using linear regression analysis. The results indicated that there was a good relationship R2 = 0.989 at 95 % confidence level between the delays generated by the simulations and the delays estimated by the proposed model.

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AN EXTENDED OV MODEL WITH CONSIDERATION OF DRIVER'S MEMORY

International Journal of Modern Physics B ◽

10.1142/s0217979209051966 ◽

2009 ◽

Vol 23 (05) ◽

pp. 743-752 ◽

Cited By ~ 65

Author(s):

T. Q. TANG ◽

H. J. HUANG ◽

S. G. ZHAO ◽

G. XU

Keyword(s):

Traffic Flow ◽

Mkdv Equation ◽

Linear Stability Theory ◽

Stable Region ◽

Optimal Velocity ◽

Car Following ◽

The Past ◽

Numerical Tests ◽

Proposed Model ◽

The Stability

In this paper, the optimal velocity (OV) model is extended to take account of the effect that the driver's memory has on the car-following behavior. The stability condition of the proposed model is obtained by using linear stability theory. The modified Korteweg-de Vries (mKdV) equation is obtained and solved. Traffic flows in the headway-sensitivity space are classified into three types as stable, metastable and unstable. Both analytical and simulation results show that introduction of driver's memory in the acceleration can improve the stability of traffic flow. It is also found that the stable region will be enlarged with the increase of the past information considered. Finally, numerical tests show that properly considering driver's memory can improve the stability of traffic flow.

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