Highway Traffic - Universität zu Kölnas/Mypage/NonEq/traffic.pdfSynchronized Flow New phase of...
Transcript of Highway Traffic - Universität zu Kölnas/Mypage/NonEq/traffic.pdfSynchronized Flow New phase of...
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Highway Traffic
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Introduction
• Traffic = macroscopic system of interacting particles (driven or self-driven)
• Nonequilibrium physics: Driven systems far from equilibrium
• Collective phenomena physics!
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Empirical Results
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Spontaneous Jam Formation
Phantom jams start-stop-waves interesting collective phenomena
space
time
jam velocity: -15 km/h (universal!)
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Experiment
(Tokai TV)
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Experiment
Experiment for WDR television, 2006
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Floating cars: Data taken from special cars inside the flow
All measured quantities are time and spatial averages
Density is calculated from mean distance
Aerial photography All measured quantities
are spatial averages, especially velocity and density
Inductive loops integrated in the lane
All measured quantities are typically time averages
Some inductive loops provide single vehicle data
Frame Template
– 7
Empirical data
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Frame Template
Single Vehicle Measurements Single vehicle measurement: detect each vehicle, no aggregation
– 8
Empirical data
• Time (in hundredth seconds) at which the vehicle reaches the detector • Lane of the vehicle • Type of the vehicle (truck, car) • Velocity in km/h (lower bound = 10 km/h) • Length in cm with an accuracy of 1 cm
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Detectors
2-loop detectors
velocity:
temporal headway:
spatial headway:
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Density
• from time of occupation tB,n: (occupancy)
density:
• from hydrodynamic relation J=ρv:
Problem: determination of density from local measurements
generically: density is underestimated!
(N cars passing in time T)
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Fundamental diagram
Universität zu Köln
free flow
congested flow (jams)
Flow
(ve
h/h)
Fl
ow (
veh/
h)
occupancy
Relation: current (flow) vs. density
more detailed features?
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Metastable States
metastable high-flow states
hysteresis
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3-Phase Theory free flow (wide) jams synchronized traffic
3 phases
non-unique flow-density relation
(metastable) high-flow states
(Kerner 1997)
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Synchronized Flow
New phase of traffic flow (Kerner – Rehborn 1996)
States of • high density and relatively large flow • velocity smaller than in free flow • small variance of velocity (bunching) • similar velocities on different lanes (synchronization) • time series of flow looks „irregular“ • no functional relation between flow and density • typically observed close to ramps
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free flow
jam
synchro
free flow, jam:
Cross-correlation function:
Objective criterion for classification of traffic phases
Synchronized traffic
synchronized traffic:
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Time Headway
many short headways!!! density-dependent
free flow synchronized traffic
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Traffic Models
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Classification of models: • description: microscopic ↔ macroscopic • dynamics: stochastic ↔ deterministic • variables: discrete ↔ continuous • interactions: rule-based ↔ force-based • fidelity: high ↔ low • concept: heuristic ↔ first principles
Universität zu Köln
Modelling approaches
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Modelling approaches
Various modelling approaches:
• hydrodynamic • gas-kinetic • force-based • cellular automata
macroscopic
microscopic
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Using queuing or renewal theory, cluster or aggregation theory and gas kinetic theory
quantity of interest: f (x, v, t), where f (x, v, t)dxdv is the probability to find a car between x and x + dx with velocity between v and v + dv at time t
Examples: Gas-kinetic models
Model equations for the macroscopic flow, similar to hydrodynamics
Basis is continuity equation (= car conservation)
Additional dynamic equation for mean velocity v(x, t)
Requires empirical flow density relation as input
Examples: Lighthill-Whitham model
Kerner-Konhäuser model
Model equations for each vehicle i at time t
Variables are position xi(t), velocity vi(t) and acceleration ai(t)
Flow quantities are calculated building mean values
Examples: Optimal velocity models
Car-following models Cellular automata
Microscopic
Frame Template
microscopic: macroscopic: mesoscopic:
– 2
ZWICKAU_V4.PPTX
Modelling approaches
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Cellular Automata
Cellular automata (CA) are discrete in • space • time • state variable (e.g. occupancy, velocity)
• often: stochastic dynamics
Advantages: • efficient implementation for large-scale computer simulations • intuitive rule-based definition of dynamics
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Universität zu Köln
Modelling of Traffic Flow
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Cellular Automata Models
Discrete in • Space: 7.5m • Time: 1 sec • State variable (velocity) velocity
dynamics: Nagel – Schreckenberg (1992)
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Update Rules
Rules (Nagel, Schreckenberg 1992)
1) Acceleration: vj min (vj + 1, vmax)
2) Braking: vj min ( vj , dj)
3) Randomization: vj vj – 1 (with probability p)
4) Motion: xj xj + vj
(dj = # empty cells in front of car j)
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Example Configuration at time t:
Acceleration (vmax = 2):
Braking:
Randomization (p = 1/3):
Motion (state at time t+1):
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Interpretation of the Rules 1) Acceleration: Drivers want to move as fast as possible (or allowed)
2) Braking: no accidents
3) Randomization: a) overreactions at braking b) delayed acceleration c) psychological effects (fluctuations in driving) d) road conditions
4) Driving: Motion of cars
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Realistic Parameter Values
• Standard choice: vmax=5, p=0.5
• Free velocity: 120 km/h ≅ 4.5 cells/timestep
• Space discretization: 1 cell ≅ 7.5 m 1 timestep ≅ 1 sec
• Reasonable: order of reaction time (smallest relevant timescale)
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Spontaneous jam formation in NaSch model
space-time plot
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Simulation of NaSch Model
• Reproduces structure of traffic on highways - Fundamental diagram - Spontaneous jam formation
• Minimal model: all 4 rules are needed
• Order of rules important
• Simple as traffic model, but rather complex as stochastic model
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Fundamental Diagram (p=0.25)
no particle-hole symmetry for vmax>1
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Fundamental Diagram (vmax=5)
No particle-hole symmetry
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VDR model
Modified NaSch model: VDR model (velocity-dependent randomization)
• Step 0: determine randomization p=p(v(t))
p0 if v = 0 p(v) = with p0 > p p if v > 0
Slow-to-start rule
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NaSch model
VDR-model: phase separation
Jam stabilized by Jout < Jmax
VDR model
Jam Structure
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Brake-light model • Nagel-Schreckenberg model
1. acceleration 2. braking 3. randomization 4. motion
• plus: – slow-to-start rule – velocity anticipation – brake lights – interaction horizon – smaller cells – …
brake-light model a.k.a.
comfortable driving model
(Knospe-Santen-Schadschneider-Schreckenberg 2000)
good agreement with single-vehicle data
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More realistic CA models NaSch model
slow-to-start rule
• metastable high-flow states
• hysteresis
• capacity drop
VDR model
• synchronized flow
• short headways
• OV function
• …
• anticipation
• comfortable driving
• mechanical restrictions
• free-flow + jammed regime
• spontaneous jamming
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2-Lane Traffic
• Rules for lane changes (symmetrical or asymmetrical) • Incentive Criterion: Situation on other lane is better • Safety Criterion: Avoid accidents due to lane changes
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• “interpolation” based on online data: online simulation • 30’ and 60’ forecast
classification into 4 states
(www.autobahn.nrw.de) Traffic Forecasting
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Finally!
Sometimes „spontaneous jam formation“ has a rather simple explanation!
Bernd Pfarr, Die ZEIT