Analysis%20of%20Patterns%20in%20Traffic%20Congestion

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Analysis of Patterns in Traffic Congestion

• Tom Ioerger, Paul Nelson
• Department of Computer Science
• Texas AM University
• Support provided by a grant from the Southwest
  Region University Transportation Research Center
  and the Texas Transportation Institute

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Motivation

• Fundamental Diagram
• What causes departure from linearity?
• What is flow a function of, besides density?
• Phases of Traffic Flow
• Free flow
• Synchronized flow (Kerner)
• Phantom/emergent traffic jams
• Gazis Herman, Nagel Paczuski, Helbing
  Treiber, etc.
• Phase transitions?

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Videogrammetric Data

• Turner-Fairbanks (TFHRC) web site
• 5 data sets
• basic sections (no on/off ramps, etc.), 2000 ft.
• 1 hour of data captured by camera on plane
• digitized 1 second per frame
• individual velocities and position within section
  
• vehicles labeled for comparing between frames
• compute flow (count 5s), vel (avg), dens (400ft)
• each var. smoothed over windows of 10-60 sec.
• Finer granularity than induction-loop data

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I-405 in L.A. (near Mulholland Dr.)

• Some congestion
• high density regions, and low velocities
• disabled vehicles on right shoulder at 22
  minutes, and left shoulder at 38 minutes

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Correlation of Vel. And Dens.
vel-0.96dens92.8 r20.829
Quadratic Fit flowdensvel
dens(-0.96dens92.8)
-0.96dens292.8dens
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Microanalysis

• Space-time diagram suggests existence of
  constrictions
• very high density
• tend to propagate backwards
• different from platoons (which move forward)
• hypothesis tend to trigger slow-downs
• theory
• front of shock wave
• Lighthill-Whitham model (kinematic wave theory)
• queue formation from events down-stream?

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Convolutions

• How to detect constrictions in data stream?
• Use time-series/signal-analysis techniques
• Template convolution
• let f(t) be signal (discrete samples)
• let g(t) be a pattern to be searched for (e.g.
  pulse)
• C(f,g)(t) ? f(t-u)g(u) du
• gives peaks in spatial domain where tmplt.
  matches
• efficient computation based on Fourier
  transforms
• C(f,g)(t) T-1(F(v)G(v)) where F(v)T(f),
  G(v)T(g)

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• Template 1
• sin wave in gradient of density - anticipation
• subtract waves for forward-moving platoons
• Template 2
• spike up in density (Gaussian)
• coupled with sharp drop in velocity

gradient
time
density
velocity
time
time
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New Observations in Mulh. Data

• Seems qualitatively different...
• Examine plots of flow, velocity, and flow
• Notice difference between lt1300 and gt1300
• 0..1300
• Flow tracks density, velocity unrelated
• 1300..3600
• velocity inverse to density, flow roughly
  constant
• Correlation coefficients

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Velocity
Frames 0..1300 correlation of vel and
dens r20.373
Density
Velocity
Frames 1300..3600 correlation of vel and
dens r20.826
Density
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Frames 0..1300 correlation of flow and
density r20.698
Frames 1300..3600 correlation of flow and
density r20.034
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Phase Separation on Fundamental Diagram
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New Phases?

• Phase 1 free flow - flow coupled to density
• Phase 2
• characteristic of congested traffic
• velocity reacts inversely to density
• contains constrictions (extremes)
• Appearance in other datasets
• free flow US101 (White Oak, Van Nuys), I-495
  (Montgom. Cnty., MD), I-10 (near La Brea Blvd.,
  L.A.)
• mixture? I-395 (near Duke St., in Alexandria VA)

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Conclusions

• Video data is good for fine-grained analysis of
  traffic behavior (greater length desired)
• Can use signal analysis techinques
• Discovery of two unique behaviors (phases)
• Future Work
• relation to other phases in lit. (sync. flow?)
• cluster analysis techniques, adjacent lanes?
• explanation by kinematic wave models
• design detection methods for ind. loop sensors