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HSM Applications to Suburban/Urban Multilane Intersections

HSM Applications to Suburban/Urban Multilane Intersections. - Session #9. Prediction of Crash Frequency for Suburban/Urban Multilane Intersections. Predicting Crash Frequency for Suburban/Urban Multilane Intersections.

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HSM Applications to Suburban/Urban Multilane Intersections

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  1. HSM Applications to Suburban/Urban Multilane Intersections - Session #9 Prediction of Crash Frequency for Suburban/Urban Multilane Intersections

  2. Predicting Crash Frequency for Suburban/Urban Multilane Intersections • Describe the models to Predict Crash Frequency for Multilane Suburban/Urban Intersections • Describe the Crash Modification Factors for Multilane Suburban/Urban Intersections • Apply Crash Modification Factors to Predicted Crash Frequency for Multilane Suburban/Urban Intersections Learning Outcomes:

  3. Definition of Intersections: “the general area where two or more roadways join or cross, including the roadway and roadside facilities for traffic movements within the area.” Intersections may be: • signalized, • stop controlled, and • roundabouts

  4. Definition of an Intersection 1. Perception-reaction distance; 2. Maneuver distance; and, 3. Queue-storage distance. Three Basic Elements at Approaches:

  5. SPF Models for Prediction of Crash Frequency for Urban/Suburban Multilane Intersections Four types of Collisions are considered: Multiple-vehicle collisions Single-vehicle collisions Vehicle-pedestrian collisions Vehicle-bicycle collisions

  6. SPF Models for Prediction of Crash Frequency for Urban/Suburban Multilane Intersections Nspf int = Nbimv + Nbisv Where: Nspf int = predicted number of total intersection- related crashes for base conditions (excludes pedestrians and bicycle related crashes) Nbimv = Predicted number of multiple-vehicle crashes per year for base conditions Nbisv = Predicted number of single-vehicle crashes per year for base conditions

  7. SPF Models for Prediction of Crash Frequency for Urban/Suburban Multilane Intersections SPF Base Models and Adjustment Factors (CMFs) are organized by four types of Intersection Right of Way Control: Three-leg intersections with STOP control on the minor road approach (3ST) Three-leg signalized intersections (3SG) Four-leg intersections with STOP control on the minor-road approaches (4ST) Four-leg signalized intersection (4SG)

  8. SPF Models for Prediction of Crash Frequency for Urban/Suburban Multilane Intersections Multiple-Vehicle NonDriveway Crashes Where: • Nbimv = expected number of multiple vehicle intersection-related crashes per year for base conditions • AADTmaj = annual average daily traffic volume for the major road (vpd) • AADTmin = annual average daily traffic volume for the minor road (vpd) • a, b, and c = regression coefficients from Table 12-14 Nbimv = exp(a + b ln(AADTmaj) + c ln(AADTmin))

  9. SPF Models for Prediction of Crash Frequency for Urban/Suburban Multilane Intersections Nbimv = exp(a + b ln(AADTmaj) + c ln(AADTmin))

  10. SPF Models for Prediction of Crash Frequency for Urban/Suburban Multilane Intersections Single-Vehicle NonDriveway Crashes Where: • Nbisv = expected number of single vehicle intersection-related crashes per year for base conditions • AADTmaj = annual average daily traffic volume for the major road (vpd) • AADTmin = annual average daily traffic volume for the minor road (vpd) • a, b, and c = regression coefficients from Table 12-16 Nbisv = exp(a + b ln(AADTmaj) + c ln(AADTmin))

  11. SPF Models for Prediction of Crash Frequency for Urban/Suburban Multilane Intersections Nbisv = exp(a + b ln(AADTmaj) + c ln(AADTmin))

  12. Prediction Crash Frequency for an Urban Multilane Intersection: EXAMPLE • Four-Leg Signalized Intersection: • 25,000 AADT and 5,000 AADT Nspf int = Nbimv + Nbisv Where: Nbimv = Predicted number of multiple-vehicle crashes per year for base conditions Nbisv = Predicted number of single-vehicle crashes per year for base conditions

  13. Prediction Crash Frequency for an Urban Multilane Intersection – Example: • Four-Leg Signalized Intersection: • 25,000 AADT and 5,000 AADT Nbimv = exp(a + b ln(AADTmaj) + c ln(AADTmin)) Nbimv = exp(-10.99 + 1.07ln(25,000)) + 0.23ln(5,000) = exp(-10.99 + 10.83 + 1.959) = exp(1.804) = 6.08 crashes per year

  14. Prediction Crash Frequency for an Urban Multilane Intersection – Example: • Four-Leg Signalized Intersection: • 25,000 AADT and 5,000 AADT Nbisv = exp(a + b ln(AADTmaj) + c ln(AADTmin)) Nbisv = exp(-10.21 + 0.68ln(25,000)) + 0.27ln(5,000) = exp(-10.21 + 6.89 + 2.30) = exp(-1.024) = 0.36 crashes per year

  15. Prediction Crash Frequency for an Urban Multilane Intersection - Example: • Four-Leg Signalized Intersection: • 25,000 AADT and 5,000 AADT Nspf int = Nbimv + Nbisv Nspf int = ______ + ________ 6.08 0.36 = 6.44 crashes per year

  16. Applying Severity Index to Urban Suburban Multilane Intersections Example: Four Approach Signalized Intersection with 25,000 AADT on Major and 5,000 AADT on minor; Fatal and Injury crashes are 5 of 12 total crashes • a. Compute the actual Severity Index (SI) • SI4sg = Fatal + Injury Crashes = 5/12 = 0.42 • Total Crashes

  17. Applying Severity Index to Urban Suburban Multilane Intersections b. Compute Predicted Fatal + Injury Crashes Nbimv = exp(-13.14 + 1.18ln(25,000)) + 0.22ln(5,000) = 1.98

  18. Applying Severity Index to Urban Suburban Multilane Intersections Nbisv = exp(-13.14 + 1.18ln(25,000)) + 0.22ln(5,000) = 0.09

  19. Applying Severity Index to Urban Suburban Multilane Intersections Example: Four Approach Signalized Intersection with 25,000 AADT on Major and 5,000 AADT on minor; Fatal and Injury crashes are 5 of 12 total crashes a. Compute the actual Severity Index (SI) SI4sg = Fatal + Injury Crashes = 5/12 = 0.42 Total Crashes b. Compute the Predicted Severity Index (SI) SI4sg = Fatal + Injury Crashes = (1.98+0.09)/6.44 Total Crashes = 0.321 • Actual Severity is greater than Predicted Severity

  20. Predicting Crash Frequency for Urban/Suburban At-Grade Intersections Nbi = Nspf int (CMF1i x CMF2i . . . . CMFni) Where: • Nbi = Predicted number of total roadway segment crashes per year with CMFs applied • Nspf int = Predicted number of total roadway segment crashes per year for base conditions • CMF1i CMF2i, .. CMFni = Crash Modification Factors for intersections

  21. CMF’s for Urban Multilane Intersections • Left-Turn Lanes • Left-Turn Signal phasing • Right Turn Lanes • Right Turn on Red • Lighting • Red Light Camera Photo Enforcement

  22. CMF1i for Presence of Left-Turn Lanes at Urban/Suburban Multilane Intersections: -- -- For STOP-Controlled Intersections: CMF applies only on uncontrolled major-road approaches

  23. CMF1i for Left Turn Lane on one approach for a three-Leg Intersection:

  24. CMF1i for Left Turn Lane on one approach for a four-Leg Intersection:

  25. CMF1i for Left Turn Lane on two approaches for a four-Leg Intersection:

  26. CMF2i for Type of Left-Turn Signal Phasing at Urban/Suburban Multilane Intersections: If several approaches have left-turn phasing: • CMF values for each approach should be multiplied together • CMF 2 approaches protected = 0.94 x 0.94 = 0.88

  27. CMF3i for Presence of Right-Turn Lanes at Urban/Suburban Multilane Intersections: --

  28. CMF4i for Prohibiting Right-Turn On Red at Urban/Suburban Multilane Intersections: CMF4i = (0.98)nprohib Where, CMF4i = CMF for the effect of prohibiting right turns on red on total crashes nprohib = number of signalized intersection approaches for which right turn on red is prohibited - Example: For 2 approaches, n=2 CMF4i = (0.98)n = (0.98)2 = 0.96

  29. CMF5i for Lighting at Urban/Suburban Multilane Intersections: Where: CMF5i = CMF for the effect of lighting on total crashes Pni = proportion of total crashes for unlighted intersections that occur at night CMF5i = 1 - 0.38Pni

  30. Example: For 4 Approach Signalized Intersection Signalized (4SG) with no lighting: CMF5i for Lighting CMF5i = 1- 0.38pnr CMF5i = 1- 0.38 pnr = 1- 0.38 x 0.235 )= 0.9107 = 1.00 as the base condition is unlit Example: For 4 Approach Signalized Intersection Signalized (4SG) with lighting: = 1- 0.38 x 0.235 = 0.9107

  31. CMF6i for Red Light Running Automated Enforcement:

  32. Applying CMF’s to Urban Multilane Intersection: Example • Four-Leg Signalized Intersection: • 25,000 AADT and 5,000 AADT • Lt & Rt Turn Lanes on Major Approaches • Protected Left-Turn Phasing on Major Road • Lighted 0.81 Nspf int = 6.44 crashes/yr CMF1i = _______ CMF2i = _____ 0.88 1.00 1.00 1.00 0.911 CMF3i = _____ CMF4i = _____ CMF5i = _____ CMF6i = _____

  33. Applying CMF’s to Urban Multilane Intersection: Example • Four-Leg Signalized Intersection: • 25,000 AADT and 5,000 AADT • Lt & Rt Turn Lanes on Major Approaches • Protected Left-Turn Phasing on Major Road • Lighted Nbi = Nspf int (CMF1i x CMF2i . . . . CMF6i) = 6.44 (0.81 x 0.884 x 1.00 x 1.00 x 0.911 x 1.00) = 4.2 crashes per year

  34. Consdieration for Pedestrians and Bicyclists Predicting Crash Frequency for Urban/Suburban Intersections Npredictedint = (Nbi + Npedi + Nbikei) Ci Where: Nint = Predicted number of total intersection crashes per year after application of CM F’s Nbi = Predicted number of total intersection crashes per year (excluding ped and bike crashes) Npedi = Predicted number of vehicle-ped crashes per year Nbikei = Predicted number of vehicle-bicycle collisions per year Ci = calibration factor for a particular geographical area

  35. Prediction of Crash Frequency for Vehicle-Pedestrian Collisions at Urban/Suburban Intersections: Two Separate Procedures for pedestrians based on Intersection Type: • Signalized Intersections • Npedi = Npedbase(CMF1p xCMF2p xCMF3p) • Stop – Controlled Intersections • Npedi = Nbi x fpedi

  36. Prediction of Vehicle-Pedestrian Crash Frequency for Signalized Urban/Suburban Intersections: Signalized Intersections Npedi = Npedbase (CMF1p x CMF2p x CMF3p) Where, Npedbase = predicted number of vehicle pedestrian collisions per year for base conditions CMF1p … CMF3p = CMFs for vehicle- pedestrian collisions

  37. Prediction of Vehicle-Pedestrian Crash Frequency for Signalized Urban/Suburban Intersections: Npedi = Npedbase (CMF1p x CMF2p x CMF3p) CMF1p -- accounts for the presence of bus stops (Table 12-28) CMF2p -- accounts for the presence of schools (Table 12-29) CMF3p -- accounts for the number of alcohol establishments (Table 12-30)

  38. Prediction of Vehicle-Pedestrian Crash Frequency for Signalized Urban/Suburban Intersections: Npedbase = exp [a + b ln(ADTtot) + c ln(AADTmin/AADTmaj) + d ln(PedVol) + e (nlanesx)] Where, AADTtot = Sum of AADT for major and minor roads (vpd) PedVol = Sum of daily pedestrian volumes crossing each intersection leg (pedestrians/day) Nlanesx = Maximum # of traffic lanes crossed by peds in one movement a, b, c, d, e = regression coefficients, Table 12-14

  39. Prediction of Vehicle-Pedestrian Crash Frequency for Signalized Urban/Suburban Intersections: Values for a, b, c, d, & e regression Coefficients

  40. Estimates of Pedestrian Crossing Volumes:

  41. Prediction of Vehicle-Pedestrian Crash Frequency for Signalized Urban/Suburban Intersections: • Example: Four-Leg Signalized Intersection: • 25,000 AADT and 5,000 AADT • Pedestrian Volume (crossing all legs) = 235 peds/day; • Four lanes each Approach on Major Road; two on minor • Left Turn Lanes on major road approaches Npedbase = exp [a + b ln(ADTtot) + c ln(AADTmin/AADTmaj) + d ln(PedVol) + e (nlanesx)] = exp [-9.53 + 0.40 ln(30,000) + 0.26 ln(5000/25000) + 0.45 ln(235) + 0.04(5 )] Npedbase = 0.042 crashes per year

  42. Pedestrian CMFs for Signalized Urban/Suburban Intersections:

  43. Prediction of Veh-Ped Crash Frequency for an Urban Multilane Intersection: EXAMPLE • Four-Leg Signalized Intersection: • 25,000 AADT and 5,000 AADT • Pedestrian Volume (crossing all legs) = 235 peds/day • Four lanes each Approach on Major Road; two on minor • Left Turn Lanes on major road approaches • within 1,000 ft ( Two bus stops + a School + 2 Alcohol Sales) Npedi = Npedbase (CMF1p x CMF2p x CMF3p) Npedbase = 0.042 crashes per year Npedi = 0.042 (2.78 x 1.35 x 1.12) = 0.177 crashes per year

  44. Prediction of Vehicle-Bicycle Crash Frequency for Urban/Suburban Intersections: Nbikei = Nbi x fbikei For Bicyclists: • Nbi = Predicted number of total roadway segment crashes per year • fpedi = Bicycle safety adjustment factor, Table 12-34

  45. Prediction of Crash Frequency for Vehicle-Bicycle Collisions at an Urban/Suburban Intersection • Example: Four-Leg Signalized Intersection • 25,000 AADT and 5,000 AADT • Pedestrian Volume = 235 peds/day • Two lanes each direction on Major Road; one lane in each direction on minor • Left turn lanes on major road and minor Nbi = Nspf int x 0.65 = 6.44 x 0.65 = 4.20 crashes/yr Nbikei = Nbi x fbikei = 4.20 x 0.015 = 0.063 crashes per year

  46. Predicting Total Crash Frequency for Urban/Suburban At-Grade Intersections • Example: Four-Leg Signalized Intersection: • 25,000 AADT and 5,000 AADT • Pedestrian Volume = 235 peds/day • Two lanes each direction on Major Road + Left Turn Lane • Two Bus Stops within 1,000 feet; one school • Two Convenient Store in SE Quadrant that sells alcohol Nint = (Nbi + Npedi + Nbikei) Ci = (4.20 + 0.177 + 0.063) x 1.0 = 4.44 crashes per year

  47. Pedestrian Islands Benefits: Separate conflicts & decision points Reduce crossing distance (reduces the # of lanes crossed in CMF for Pedestrians) Improve signal timing Reduce crashes Application of Islands to Improve Pedestrian Safety • Reducing # of lanes crossed • Reduces Ped Crash Frequency

  48. Right-Turn Slip Lane - Details 55° to 70° between vehicular flows. Cut through medians and islands for pedestrians 25’ to 40’ radius depending on design vehicle 2:1 length/width ratio Crosswalk one car length back Long radius followed by short 150 to 275’ radius Bicycle lane

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