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Analysis of Patterns in Traffic Congestion

Analysis of Patterns in Traffic Congestion. Tom Ioerger, Paul Nelson Department of Computer Science Texas A&M University Support provided by a grant from the Southwest Region University Transportation Research Center and the Texas Transportation Institute. Motivation. Fundamental Diagram

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Analysis of Patterns in Traffic Congestion

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  1. Analysis of Patterns in Traffic Congestion Tom Ioerger, Paul Nelson Department of Computer Science Texas A&M University Support provided by a grant from the Southwest Region University Transportation Research Center and the Texas Transportation Institute Texas A&M University

  2. 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? Texas A&M University

  3. 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 Texas A&M University

  4. 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 Texas A&M University

  5. Correlation of Vel. And Dens. vel=-0.96*dens+92.8 r2=0.829 Quadratic Fit: flow=dens*vel =dens*(-0.96*dens+92.8) =-0.96*dens2+92.8*dens Texas A&M University

  6. 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? Texas A&M University

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  9. 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) Texas A&M University

  10. 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 Texas A&M University

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  12. New Observations in Mulh. Data • Seems qualitatively different... • Examine plots of flow, velocity, and flow • Notice difference between <1300 and >1300 • [0..1300]: • Flow tracks density, velocity unrelated • [1300..3600]: • velocity inverse to density, flow roughly constant • Correlation coefficients Texas A&M University

  13. Velocity Frames [0..1300] correlation of vel and dens: r2=0.373 Density Velocity Frames [1300..3600] correlation of vel and dens: r2=0.826 Density Texas A&M University

  14. Frames [0..1300] correlation of flow and density: r2=0.698 Frames [1300..3600] correlation of flow and density: r2=0.034 Texas A&M University

  15. Phase Separation on Fundamental Diagram Texas A&M University

  16. 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) Texas A&M University

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  18. 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 Texas A&M University

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