A new car following model: comprehensive optimal velocity model Jun fang Tian, Bin jia, Xin gang Li Jun fang Tian, Bin jia, Xin gang Li MOE Key Laboratory.

Slides:



Advertisements
Similar presentations
M. Tech. Project Presentation Automatic Cruise Control System By: Rupesh Sonu Kakade Under the guidance of Prof. Kannan Moudgalya and Prof. Krithi.
Advertisements

Kinematics in One Dimension
Free Fall and Projectile Motion
Noadswood Science,  To understand acceleration and velocity-time graphs Wednesday, April 29, 2015.
Kinematics in One Dimension. Distance and Displacement.
Motion in One Dimension
Motion in Two and Three Dimensions
Simulation-based stability analysis of car-following models under heterogeneous traffic Hao Wang School of Transportation Southeast University Aug 13,
Introduction to VISSIM
Effect of Electronically Enhanced Driver Behavior on Freeway Traffic Flow Alain L. Kornhauser Professor, Operations Research & Financial Engineering Director,
Descriptions of Motion
The INTEGRATION Modeling Framework for Estimating Mobile Source Energy Consumption and Emission Levels Hesham Rakha and Kyoungho Ahn Virginia Tech Transportation.
Kinematics Goals: understand graphs of a) position versus time, b) velocity versus time.
PHYS 2010 Nathalie Hoffmann University of Utah
Computational Modelling of Road Traffic SS Computational Project by David Clarke Supervisor Mauro Ferreira - Merging Two Roads into One As economies grow.
LIAL HORNSBY SCHNEIDER
Graphing Motion.
What about this??? Which one is false?. Aim & Throw where????
C H A P T E R 2 Kinematics in One Dimension. 2.6 Freely Falling Bodies.
Chapter 2 Motion in One Dimension. Kinematics Describes motion while ignoring the agents that caused the motion For now, will consider motion in one dimension.
Motion in One Dimension
Chapter 6 Momentum and Collisions. Chapter Objectives Define linear momentum Compare the momentum of different objects Describe impulse Conservation of.
Motion Graphs Distance vs. Time Graphs. Motion Graphs Show the motion of an object in a graph. Graphs can help make motion easier to picture and understand.
Motion in one dimension
A. Khosravi. Definition: Car-following model is a microscopic simulation model of vehicle traffic which describes one-by-one following process of vehicle.
Galileo Galilei was the first to make an analysis of the motion of freely falling objects. Based on his experimentations and reasoned deductions, Galileo.
C H A P T E R 2 Kinematics in One Dimension Kinematics in One Dimension.
Contents: 2-1E, 2-5E, 2-9P, 2-13P, 2-33P, 2-36P*
Time Dilation We can illustrate the fact that observers in different inertial frames may measure different time intervals between a pair of events by considering.
1 Challenge the future Longitudinal Driving Behavior in case of Emergency situations: An Empirically Underpinned Theoretical Framework Dr. R.(Raymond)
Describing the motion of an object is occasionally hard to do with words. Sometimes graphs help make the motion easier to picture, and therefore understand.
Do Now Describe your position in the classroom using a reference point and a set of reference directions. Record your response in your science journal.
Page 81 (Ch ) 6. The bowling ball moves without acceleration because there is no net force on the ball (neglecting friction) 7.Neglecting air resistance,
A constantly changing velocity. Accelerated Motion.
DLR-Institute of Transport Research Testing and benchmarking of microscopic traffic flow simulation models Elmar Brockfeld, Peter Wagner
Kinematic Equation Examples
Kinematics AP Physics 1. Defining the important variables Kinematics is a way of describing the motion of objects without describing the causes. You can.
Defining the important variables Kinematics is a way of describing the motion of objects without describing the causes. You can describe an object’s motion:
MOTION - A CHANGE IN POSITION MEASURED BY DISTANCE AND TIME. SPEED - THE RATE AT WHICH AN OBJECT MOVES. VELOCITY - SPEED AND DIRECTION OF A MOVING OBJECT.
A Microscopic Simulation Study of Automated Headway Control of Buses on the Exclusive Bus Lane on the Lincoln Tunnel Corridor Vehicle-Following Algorithm.
Effect of Electronically Enhanced Driver Behavior on Freeway Traffic Flow Alain L. Kornhauser Professor, Operations Research & Financial Engineering Director,
Protective Braking for ACSF Informal Document: ACSF
Motion Notes 3 Forces : Terminal velocity. Terminal Velocity Consider a skydiver: 1)At the start of his jump the air resistance is _______ so he ____.
Warm Up – February 3, Speed-Time Graphs Motion and Acceleration.
Modeling Road Traffic Greg Pinkel Brad Ross Math 341 – Differential Equations December 1, 2008.
Enhancing the capacity of on-ramp system by controlling the entrance gap Bin Jia, Xingang Li a, Rui Jiang b, Ziyou Gao a Bin Jia a, Xingang Li a, Rui Jiang.
Dynamics and Space Velocity-time graphs. Learning Outcomes Velocity-time graphs for objects from recorded or experimental data. Interpretation of velocity.
Objectives: Evaluate the difference between velocity and acceleration. Solve simple acceleration problems in one dimension.
Vehicular Mobility Modeling for Flow Models Yaniv Zilberfeld Shai Malul Students: Date: May 7 th, 2012 VANET Course: Algorithms in.
Physics Section 2.2 Apply acceleration to motion
MASTERS CLASS ASSIGNMENT(HIGHWAY AND TRANSPORT OPTION, 2016/2017) DEPARTMENT OF CIVIL ENGINEERING, BAYERO UNIVERSITY KANO. NIGERIA Ibrahim Tanko Abe SPS/16/MCE/00028.
Kinematics.
Dynamics and Space Learning Intention You will be able to:
Chapter 3: LINEAR MOTION Page 7.
Describing Motion Some More Equations….
1-1-4 Kinematics Equations
Speed can be calculated by Speed = Distance/Time
Lecture 2 Chapter ( 2 ).
Dilemma Zone Protection at An Isolated Signalized Intersection Using Dynamic Speed Guidance Wenqing Chen.
Motion.
A car is decelerated to 20 m/s in 6 seconds
Car-Following Theory Glossary Car-following theories
Acceleration (a) Non Uniform Motion
The Kinematics Equations
Unit 6 (2) Acceleration Physical Science.
One Dimensional Kinematics Constant Acceleration:
Motion Graphs 2 x v a.
Presentation transcript:

A new car following model: comprehensive optimal velocity model Jun fang Tian, Bin jia, Xin gang Li Jun fang Tian, Bin jia, Xin gang Li MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing , P.R.China Contributions We present a new car-following model, i.e. comprehensive optimal velocity model (COVM), whose optimal velocity function not only depends on the following distance of the preceding vehicle, but also depends on the velocity difference with the preceding vehicle. Simulation results show that COVM is an improvement over the previous ones theoretically. The unrealistically high deceleration, which appears in OVM and FVMD, will not appear in COVM. Furthermore, the accident in the urgent braking case, which can not be avoid in OVM and FVDM, can be avoid in COVM. During the simulation, the value of L and c 3 is selected as L = 6, c 3 =0.35, other parameters are selected in Table 1. Firstly, we simulate the vehicles’ behaviors under the decelerating case that a freely moving car from a large distance reaches a standing car. The decelerating case is carried out as follows: The leading car stops all the time, the follower moves with the speed 14.5m/s, the headway between them is 150m at the initial time t = 0s. Simulation results are shown in Fig.1. One can see that the maximum deceleration rate are 6.51, 7.30, 2.98m/s 2 in OVM, FVDM and COVM respectively. Since empirical deceleration should not be larger than 3m/s 2 [2], the deceleration in OVM and FVDM is unrealistic high. One also could note that the space headway is lower than 5m in OVM, so crash occurs. As to the COVM, neither high deceleration nor crash occurs. Secondly, we simulate the vehicles' behaviors under the urgent braking case referred to[4]. The urgent braking case is carried out in the following. Two successive cars move with the same speed 14.5m/s at the initial time t=0s and the space headway is 20m. The leading car decelerates suddenly with the deceleration -8m/s 2 until it stops completely. The leading car remains standing for several seconds before accelerating back to its original speed. The simulation results are shown in Fig.2(a), which exhibits the variation of space headway of the following car. One can see that the space headways are lower than 5m in OVM and FVDM, this indicates that the leader and the follower collides in OVM and FVDM. But because the follower could adjust his speed timely, so the space headway is always larger than 5 m in the COVM, i.e. the COVM avoids the accident successfully. Results 1. M. Bando, K. Hasebe, A. Nakayama, A. Shibata, and Y. Sugiyama, Phys. Rev. E. 51: , D. Helbing and B. Tilch, Phys.Rev. E. 58: , R. Jiang, Q. S. Wu, and Z. J. Zhu, Phys. Rev. E. 64:017101, X. M. Zhao and Z. Y. Gao, Eur. Phys. J. B 47: , M. Bando, K. Hasebe, K. Nakanishi, and A. Nakayama, Phys. Rev. E 58: , J.M. Del Castillo and F.G. Benitez, Transp. Res., Part B: Methodol. 29: , 1995 References In the real traffic, when the driver adjusts his speed, he will consider the following conditions: the space headway with his leader, the velocity difference with his leader and so on. So, we believe the optimal velocity function is not only just a function of the following distance, but also should be determined by the speed difference, i.e. V op =V(Δx n (t),Δv n (t)). So, the dynamic behavior of vehicles could be modeled by the following equation: For simplicity, we take: is the reaction coefficient to the relative velocity, 0< <1, so we get: Taking, we could get the new model: Both and are sensitivity. Because the optimal velocity function in the new model is more comprehensive than those in the existing models, so we call our new model as comprehensive optimal velocity model (COVM). is selected as follows: Lc is the length of the vehicle, which can be taken as 5m in simulations, v 1 = 6.75m/s, v 2 = 7.91m/s, c 1 = 0.13m/s, c 2 = 1.57m/s. is selected as below: Where, L and c 3 are constants. The simulation results will show that this form is reasonable and realistic. Model Fig. 1 The simulations in the OVM, FVDM and COVM under the case that a freely moving car approaching a standing car, (a) represents the acceleration of the follower, and (b) represents the headway distance of the follower. In this paper, we present a new car-following model, i.e. comprehensive optimal velocity model (COVM), in which the optimal velocity function not only depends on the following distance of the preceding vehicle, but also depends on the velocity difference with the preceding vehicle. The simulation results show that the unrealistically high deceleration will not appear in COVM, and the accident in the urgent braking case can be avoid in COVM. Conclusions Thirdly, the delay time of car motion and kinematic wave speed c j at jam density are examined in COVM. We carried out the simulation as that in the reference[3]. First a traffic signal is yellow and all vehicles are waiting with headway 7.4m, at which the optimal velocity is zero. Then at time t=0s, the signal changes to green and cars begin to move. The simulation results are shown in Fig.3(a) and Table 1. From Table 1, one can see that the values of and c j are 1.28s and 20.81m/s in COVM. As Bando et al. pointed out, the observed is of the order of 1s[5], and Del Castillo and Benitez indicated that c j ranges between 17 and 23 km/h[6]. So, the COVM is successful in anticipating the two parameters. Fig.3(b) shows the variation of acceleration under the case that two successive car initially at rest, and the leading car is unobstructed. At t=0s, they begin to start up according to the OVM, FVDM and COVM. One can see that the maximum value of the leading car's acceleration in COVM is not greater than that in the other two models. As for the following car, the car in COVM accelerates more quickly than others, so the delay time of COVM is shorter than others. From above simulations, one can see that the COVM describes the traffic dynamics most exactly, which verifies that the improvement in COVM is reasonable and realistic. Fig. 3. (a) exhibits the variation of velocity of all vehicles starting from a traffic signal in COVM. (b) exhibits the variation of acceleration of unobstructed leading car and its following car both initially at rest in OVM, FVDM, and COVM. This work is financially supported by 973 Program (2006CB705500), the National Natural Science Foundation of China ( and ), Program for New Century Excellent Talents in University (NCET ), and the Natural Science Foundation of Beijing ( ). Funding δ c j Table 1 the values of δt and c j Modelδt (s)c j (km/h) OVM(κ=0.85 s - 1, λ=0 s - 1 ) GFM(κ=0.41 s - 1, λ=0.5 s - 1 ) FVDM(κ=0.41 s - 1, λ=0.5 s - 1 ) CVOM(κ=0.41 s - 1, λ=0.5 s - 1 ) Fig. 2. The simulations in the OVM, FVDM and COVM under an urgent case, (a) represents the headway distance of the follower. Fig. 2. The simulations in the OVM, FVDM and COVM under an urgent case, (b) represents the velocity of the leader.