Car-following Model of Vehicle Car-following Model of Vehicle TrafficTraffic
Car-following Model of Vehicle Car-following Model of Vehicle TrafficTraffic
In the name of GodIn the name of God
A. Khosravi
Definition: Car-following model is a microscopic simulation model of vehicle traffic which describes one-by-one following process of vehicle on the same lane.
……
-GHR
-Fuzzy
MITSim-ModelMITSIM
a Queuing ModelaQUEUE
--GIPS
Optimal Velocity OV
Cell TransitionCT
Cellular AutomatonCA
Different Models
Car-following: Pipes 1958
Gazis-Herman-Rothery(GHR) model (1958)
][][Re Stimulussponse n
Types of model vary since the definition of stimulus vary.
Stimulus
1) Speed of vehicle
2) Relative Speed
3) Spacing between the n & n-1 vehicle
GHR model specifies the stimulus as the relative velocity of the
vehicle that is:
Every vehicle tends to move as the same speed of its front vehicle.
Formulation:
)()( Ttvctan
acceleration of vehicle
n implemented at time t
Relative speed of the vehicle n to its front vehicle at earlier time of t-T
T: driver reaction timec: sensitivity coefficient
Above equation is very different from real situation. In order to make the model more realistic:
)(
)()(
Ttx
Ttvctan
x Relative spacing to two vehicle
In 1960, Eide modified model again. He thought the velocity of the vehicle itself influences the behavior of driver, too. So the GHR model can be more generally expressed as:
)(
)()(
Ttx
Ttvcvta
lmnn
Vn the speed of the n th vehiclem,l are the constant must to be determined.
The most vital part of the GHR model
Experience
]2,0[m
]2,1[l
Optimal Velocity(OV) model
)]()([)( tvtVcta ndesirednn
The desired velocity of the n th vehicle at time t.
In GHR model:
In the OV model, the desired velocity is considered to be relevant to the relative spacing:
))(()( txVtV noptdesired
n
)()( 1 tvtV ndesiredn
So that:
)]())(([)( tvtxVcta nxopt
n
ModelModel Driver StrategyDriver Strategy
OVOVto maintain a safe velocity according to relative position
GHRGHRKeeps a safe
distance according to relative velocity
There are many specific forms of ))(( txV nopt . A popular choice is:
xxv
xxxxf
xx
xV
B
BA
Aopt
max
)(
0
)(
Traffic under congestion and vehicle should stop.
The vehicle density is low and thus the vehicles could run at their maximum speed.
Fuzzy Logic Model
Behavior of human Vehicle behavior
A correct description of human A effective model
Fuzzy modelFuzzy model
humanhuman Fuzzy controller
Inputs Inputs Statue message of the front car
OutputOutputDecision made through a series of
thinking
Example:
The vehicle should be decelerated when relative distance is too close.
‘too close’ is a fuzzy value and the response of ‘decelerate’ is a fuzzy decision-making.
At first tried to Fuzzify the GHR Model [10].
‘too close’= 5.0| xx‘not close’= 2| xx
….
FUZZY SETS AND SYSTEMS FOR A MOTORWAY MICROSCOPIC SIMULATION MODEL
The two basic models describing driver behavior:
-Car-following (the speed-distance relationship)
- Lane-changing (the interaction between adjacent lanes)
FUZZY SETS AND SYSTEMS FOR A CAR-FOLLOWING MODEL
Car-following model has two principal premise variables:
• Relative speed (DV)
• Distance divergence, DSSD (the ratio of vehicle separation, DS, to the driver’s desired following distance)Fuzzy Sets: Triangular
Membership function
Relative Speed (DV)
Distance Divergence
(DSSD)
Driver Response(Acceleration
Rate)
Opening Fast (V1)
Much Too Far (S1)
Strong Acceleration
Opening (V2) Too Far (S2) Light Acceleration
About Zero (V3)
Satisfied (S3) No Action
Closing (V4) Too Close (S4) Light Deceleration
Closing Fast (V5)
Much Too Close (S5)
Strong Deceleration
Fuzzy Set Terms Used in the Car-following Model
A fuzzy rule for car-following modelIf Distance Divergence is Too Far and relative speed
is Closing then the driver’s response is No Action (keep current speed).
FUZZY SETS AND SYSTEMS FOR A LANE-CHANGING MODEL
Two different models:
1. Lane Change to Offside(LCO):
A driver’s motivation to move to the offside lane is to get some form
of speed benefit.
2. Lane Change to Nearside(LCN)
The motivation to move to the nearside lane is to reduce impedance
to fast moving vehicles approaching from behind.
The LCO ModelThe LCO model has two principal premise
variables:
• Overtaking benefit (speed gain)
• Opportunity (Safety and Comfort of the lane change)
Fuzzy Sets: Triangular Membership function
Low
Medium
High
Intention of LCO
Bad (OP3)
Moderate (OP2)
Good (OP1)
Opportunity
High (OB1)
Low (OB3)
Medium (OB2)
Overtaking Benefit
Fuzzy Set Terms Used in the Car-following Model
The LCN ModelThe LCN model has two premise variables:
• Pressure from rear is measured as the time headway of the following vehicle.
• Gap satisfaction is measured by the period of time for which it would be possible for the vehicle to stay in the gap in the nearside lane, without reducing speed.Fuzzy Sets: Triangular
Membership function
Fuzzy Set Terms for the LCN Model
Pressure from Rear
Gap Satisfaction
Intention of LCN
High (PR1) High (GS1) High
Medium (PR2) Medium (GS2) Medium
Low (PR3) Low (GS3) Low
A fuzzy rule for an LCO modelIf Overtaking Benefit is High and Opportunity is Good then Intention of LCO is High.
Data Collection
Two types of data are required:1- The membership functions
2- Obtain dynamic car-following and lane-changing data in a range of circumstances, against which the model can be calibrated.
Car-following Behavior
Phase 1) Say following distance/relative speed using the verbal terms .
Phase 2) To follow a target vehicle at their ‘minimum safe distance’.
Phase 3) To performed a number of acceleration/ braking Maneuvers.
Phase 4) To pass the target vehicle, find a slower vehicle and approach from over 100m until his desired headway was reached.
Phase 5) In ‘free mode’, the driver was again questioned regarding closing speed.
Lane-changing Behavior
Phase1) About every half minute, the observer asked the subject whether he had the intention to make a lane change to the nearside/offside lane.
Phase 2) With each successful lane change, the subject gave a description about the intention level and the reasons, such as ‘overtaking benefit was high’ and the ‘opportunity was good’ etc..
The recorded subjective verbal assessments from
subjects
Data recorded simultaneously by the instrumented vehicles
Fuzzy Data Base
FUZZY SETS AND SYSTEMS CALIBRATION
The fuzzy set calibration, assigns the detailed numerical values collected in the survey to each verbalized fuzzy set.
The Membership Function for the Fuzzy Set ‘about zero’ of Premise Variable, DV
A Sample Survey Result
The Fuzzy Rule Base for the Car-Following Model (in Matrix Structure)
Validation Test: Comparison of Accelerations (Subject 1)
Validation Test: Comparison of Speeds (Subject 1)
Validation Test: Comparison of Relative Speeds (Subject 1)
Comparison of SE on Acceleration Rates for Different
Car-following Models (Subject 1)
Comparison with other models
Comparison of SE on Speeds for Different Car-following Models (Subject 1)
Comparison of SE on Relative Speeds for Different
Car-following Models (Subject 1)
Lane-Changing Model Validation
Lane-changing Rates Comparison between Data from Survey and Simulation
Lane Occupancy Comparison for Lane 1
Lane Occupancy Comparison for Lane 2
Lane Occupancy Comparison for Lane 3
ConclusionConclusion
Fuzzy Logic is the best approach for car-following & Lane-changing modeling.
Any question?Any question?
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