traffic flow simulation car-following model by: ittinop(pun) dumnernchanvanit
TRANSCRIPT
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Traffic Flow SimulationCar-Following Model
By: Ittinop(Pun) Dumnernchanvanit
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Introduction:
What is done in this project?◦Simulate traffic following each individual
car.◦Use AI to simulate driver’s behavior on road◦Observe and analyze traffic phenomena
Why car-following model?◦Traffic is extremely complex and most
phenomena are non-linear and cannot be solved easily and accurately through equations.
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Example uses for simulation:
◦Can help maximize traffic flow. For instance, determine the most efficient automated red light, green light pattern.
◦How far to put a warning sign for road block.
◦Help in choosing between stop sign/red light green light at specific intersection
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Phenomena Simulated:ShockwaveRoad blockCutting in frontPlatoonSystem of four way and three
way intersections
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Mechanisms behind the simulationSpeed
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Density Approximation
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Mechanisms behind the simulationAcceleration:
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Mechanisms behind the simulationAngular movement calculated
using turn radius.◦This way we can turn without
worrying about speed
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Mechanisms behind the simulation: Angular movement
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Mechanism behind the simulation: Angular movementAngular movement:
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Mechanisms behind the simulation: Turn radiusTurn radius calculation: (for future
improvement)◦ 1. Find intersection using y = mx+b etc.◦ 2. Find distance from lane end to
intersection◦ 3. Find angle 3◦ 4. Find turn radius
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Mechanisms behind the simulationhow to show vehicle with its
directiontake car to center, then use
rotational matrix, then take it back
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Project code composition:
CarLaneCreatormain
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CarVariables:
◦(x,y) ◦Direction◦Max speed◦Max acceleration◦Max brake◦Lane◦waypoints
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CarMethods:
◦react() –determines acceleration and angular movement. Basically that is how real world driver control car, pedal/brake for acceleration and wheel turning for angular movement.
◦move() – move the car according to acceleration and angular movement.
◦getFrontCar(), getBackCar(), getBackMostCar()
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LaneVariables
◦Position◦Width◦Direction◦Leftlane, rightlane◦Start, end
Methods◦getDirection()◦ insertAdjacentLane(Lane* leftlane_raw,
Lane* rightlane_raw)◦ isEqual(Lane* a)
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CreatorWorks like car factory that spit out car
on to lane from some specific point.Spit out if no car with in a specific
distancestarting_speed:
◦= max_speed*(1-min_d/d);Adjust to different density
automatically and will not over produce.
Can adjust density using this.
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CreatorVariables:
◦Waypoints, endland transitions◦Distance between cars◦Starting speed◦Chance to produce etc.
Methods◦closeByCar() test if there is car near
by the creator object (Can adapt do different density)
◦createCar()
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MainSet-up the system
◦Build lanes, and creators/or carsloop through time steps
◦Run the car◦Record the results
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Assumptions• All units in meters and seconds• chose 0.1 sec for time step because
human reaction is 0.2 secMax speed: 65 kmphMax acceleration: 3.79 m/s
◦(~7.1s 0-60mph) Minimum distance between car:
◦ 7m from center to center, or around 2-3m between car.
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Max Flux DerivationWhat should max flux be?
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Steady State Movie
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Max Flux Data
Distance between cars (m)
flux (n/s)
24 1.0619 1.1814 1.27
(calc. = 1.285)9 14 0.66
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ShockwaveTraveling disturbance in
distribution of cars on road.Usually backward motion
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ShockwaveVideo:
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PlatoonThis is an idea to group vehicle in
to platoons to increase the capacity of road.◦This allow cars to be closer to each
other.This will need smart car that can
be driven by artificial intelligence
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PlatoonVideo:
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PlatoonData comparison: (Assume that
on one of the lane, there is 50%/50% chance that creator will produce platoon or car.)
average final time (s)
flux (n/s)
Platoon 24.1601 1.73No Platoon 27.174 1.27
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Road Block:How it is done:
◦Car object contain pointer to object targetlane and lane
◦Why targetlane? vehicles will also check other vehicle’s
targetlane in their loop so they can recognize incoming car from another lane and yield for it.
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Road Block:Algorithm
◦At road block, vehicle slow down and tries to cut in front of another vehicle.
◦distance to back car and to front car in target lane vehicle need to cut is set
◦when vehicle is set to change lane, it turn and run toward the lane and then turn the wheel back when it is in the middle and
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Something to keep in mind when looking at dataTime is counted from entering
system to exiting system. If flux is low, it might means
traffic jam might propagate much longer than system which means the car would have waited much longer outside the system than the case with less flux
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Road Block MovieNo sign, see block at around 100
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Road Block MovieSign at 100, see actual block
around 200
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Road Block Data:
No Warning Sign Warning SignProduction distance
average final time (s)
flux (n/s) average final time (s)
flux (n/s)
24 70.7099 0.37 51.7759 0.3634 41.8327 0.366667 28.6344 0.42666744 36.5018 0.37 23.2858 0.423333
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Cut in Front: How it is doneDriver looks to another lane to
decide whether it is worth to cut in front.
Then look to the back to determine if it is possible to do so
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Cut in Front: How it is done Judgment criteria driver use for front car:
◦ coefficient*( (speed of front car in our lane)*time +distance to front car in our lane ))
◦ (speed of front car target lane)*time +distance to front car in target lane
◦ where time is any set time, depending on driver’s experience.
◦ coefficient allow us to set how much we want the driver to cut
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Cut in Front: How it is doneThen look at back car
◦driver look at speed of back car and distance to back car.
◦driver knows the amount of time he will use to cut in front.
◦simple algorithm used is just, (coefficient*distance) >( (back car speed)*(cut time))
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Cut in Front: Movie
Show outside
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Cut in Front: Data (5 lanes), del_t = 1.0s
Distance = 14 m Distance = 54 m
Coefficient average final time (s)
flux (n/s) Number of cuts per car
average final time (s)
flux (n/s) Number of cuts per car
1.1 48.9568 1.45 1.69195 38.8109 0.95 0.22807
1.5 48.3438 1.49 1.05593 38.7677 0.95 0.157895
2.0 46.417 1.48667 0.44843 38.7014 0.95 0.101754
3.0 45.8578 1.49333 0.176339 38.7028 0.95 0.0982456
5.0 45.7897 1.49 0.152125 38.7021 0.95 0.0982456
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Keep in mind:Note that there are so much
more variables such as car density we can manipulate and these behaviors might change totally.
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Complex Road System:System of four lane with three
lane attached to it on the eastCases:
◦Red/green light◦all-way stop sign
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Complex Road System: How it is doneWaypoints:
◦the way point build and given to car creator
◦makes sense because driver usually knows where he is going to go from the start. (most of the time)
◦In this project, the waypoints are different lanes the car will go through before exiting
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Complex Road System: How it is doneRed light/Green light set up
◦Red light are built into lane class. Basically, car on the lane check if it is turned on, if so stop at the light if front car is farther than the lane end.
◦Set red light in main class. Set repeating pattern using fmod()
◦Assume no left turn
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Complex Road System: Red light/Green light VideoShow outside
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Complex Road System: How it is doneAll way Stop signs in this project
was extended from red light code.
Check area in the middle and open green light to let some car in temporarily.
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Complex Road System: All-way Stop Signs Video
◦Show outside
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Complex Road System: Data
Red light/green light Stop signsProduction Distance
average final time (s)
flux (n/s) average final time (s)
flux (n/s)
7 102.126 1.87143 110.135 0.48571415 97.4733 1.87143 104.56 0.61428630 90.122 1.81429 92.4511 0.64285750 83.9205 1.67143 74.2911 0.642857
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Complex Road System: Analysis
So traffic lights are better than stop signs in traffic flow at high flux.
They have around the same efficiency at low flux. This is the reason why why we see all-way stop signs in areas with less traffic.
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The End:Thank you for Listening!