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

Learning Outcomes: ► 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 Predicting Crash Frequency for Suburban/Urban Multilane Intersections

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

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

SPF Models for Prediction of Crash Frequency for Urban/Suburban Multilane Intersections 1)Multiple-vehicle collisions 2)Single-vehicle collisions 3)Vehicle-pedestrian collisions 4)Vehicle-bicycle collisions Four types of Collisions are considered:

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

1)Three-leg intersections with STOP control on the minor road approach (3ST) 2)Three-leg signalized intersections (3SG) 3)Four-leg intersections with STOP control on the minor-road approaches (4ST) 4)Four-leg signalized intersection (4SG) SPF Base Models and Adjustment Factors (CMFs) are organized by four types of Intersection Right of Way Control: SPF Models for Prediction of Crash Frequency for Urban/Suburban Multilane Intersections

N bimv = exp(a + b ln(AADT maj ) + c ln(AADT min )) 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 SPF Models for Prediction of Crash Frequency for Urban/Suburban Multilane Intersections

N bimv = exp(a + b ln(AADT maj ) + c ln(AADT min )) SPF Models for Prediction of Crash Frequency for Urban/Suburban Multilane Intersections

N bisv = exp(a + b ln(AADT maj ) + c ln(AADT min )) 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 SPF Models for Prediction of Crash Frequency for Urban/Suburban Multilane Intersections

N bisv = exp(a + b ln(AADT maj ) + c ln(AADT min )) SPF Models for Prediction of Crash Frequency for Urban/Suburban Multilane Intersections

Prediction Crash Frequency for an Urban Multilane Intersection: EXAMPLE Four-Leg Signalized Intersection:  25,000 AADT and 5,000 AADT N spf int = N bimv + N bisv Where: N bimv = Predicted number of multiple-vehicle crashes per year for base conditions N bisv = Predicted number of single-vehicle crashes per year for base conditions

Four-Leg Signalized Intersection:  25,000 AADT and 5,000 AADT = 6.08 crashes per year N bimv = exp( ln(25,000)) ln(5,000) = exp( ) = exp(1.804) N bimv = exp(a + b ln(AADT maj ) + c ln(AADT min )) Prediction Crash Frequency for an Urban Multilane Intersection – Example:

Four-Leg Signalized Intersection:  25,000 AADT and 5,000 AADT = 0.36 crashes per year N bisv = exp( ln(25,000)) ln(5,000) = exp( ) = exp(-1.024) N bisv = exp(a + b ln(AADT maj ) + c ln(AADT min )) Prediction Crash Frequency for an Urban Multilane Intersection – Example:

Four-Leg Signalized Intersection:  25,000 AADT and 5,000 AADT N spf int = N bimv + N bisv N spf int = ______ + ________ = 6.44 crashes per year Prediction Crash Frequency for an Urban Multilane Intersection - Example:

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 Applying Severity Index to Urban Suburban Multilane Intersections a. Compute the actual Severity Index (SI) SI 4sg = Fatal + Injury Crashes = 5/12 = 0.42 Total Crashes

Applying Severity Index to Urban Suburban Multilane Intersections b. Compute Predicted Fatal + Injury Crashes N bimv = exp( ln(25,000)) ln(5,000) = 1.98

Applying Severity Index to Urban Suburban Multilane Intersections N bisv = exp( ln(25,000)) ln(5,000) = 0.09

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 Applying Severity Index to Urban Suburban Multilane Intersections a. Compute the actual Severity Index (SI) SI 4sg = Fatal + Injury Crashes = 5/12 = 0.42 Total Crashes b. Compute the Predicted Severity Index (SI) SI 4sg = Fatal + Injury Crashes = ( )/6.44 Total Crashes = ►Actual Severity is greater than Predicted Severity

N bi = N spf int (CMF 1i x CMF 2i.... CMF ni ) ► N bi = Predicted number of total roadway segment crashes per year with CMFs applied ► N spf int = Predicted number of total roadway segment crashes per year for base conditions ► CMF 1i CMF 2i,.. CMF ni = Crash Modification Factors for intersections Where: Predicting Crash Frequency for Urban/Suburban At-Grade Intersections

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

CMF 1i for Presence of Left-Turn Lanes at Urban/Suburban Multilane Intersections: For STOP-Controlled Intersections: CMF applies only on uncontrolled major-road approaches --

CMF 1i for Left Turn Lane on one approach for a three-Leg Intersection:

CMF 1i for Left Turn Lane on one approach for a four-Leg Intersection:

CMF 1i for Left Turn Lane on two approaches for a four-Leg Intersection:

CMF 2i 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

CMF 3i for Presence of Right-Turn Lanes at Urban/Suburban Multilane Intersections: --

CMF 4i for Prohibiting Right-Turn On Red at Urban/Suburban Multilane Intersections: CMF 4i = (0.98) nprohib Where, CMF 4i = CMF for the effect of prohibiting right turns on red on total crashes n prohib = number of signalized intersection approaches for which right turn on red is prohibited - Example: For 2 approaches, n=2 CMF 4i = (0.98) n = (0.98) 2 = 0.96

Where: CMF 5i = CMF for the effect of lighting on total crashes P ni = proportion of total crashes for unlighted intersections that occur at night CMF 5i for Lighting at Urban/Suburban Multilane Intersections: CMF 5i = P ni

CMF 5i for Lighting Example: For 4 Approach Signalized Intersection Signalized (4SG) with no lighting: = CMF 5i = p nr = x CMF 5i = p nr = x ) = = 1.00 as the base condition is unlit Example: For 4 Approach Signalized Intersection Signalized (4SG) with lighting:

CMF 6i for Red Light Running Automated Enforcement:

N spf int = 6.44 crashes/yr Applying CMF’s to Urban Multilane Intersection: Example CMF 1i = _______ CMF 3i = _____CMF 4i = _____ CMF 2i = _____ 0.81 CMF 5i = _____ CMF 6i = _____ 1.00 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

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 N bi = N spf int (CMF 1i x CMF 2i.... CMF 6i ) = 6.44 (0.81 x x 1.00 x 1.00 x x 1.00) = 4.2 crashes per year Applying CMF’s to Urban Multilane Intersection: Example

N predicted int = (N bi + N pedi + N bikei ) C i Where: N int = Predicted number of total intersection crashes per year after application of CM F’s N bi = Predicted number of total intersection crashes per year (excluding ped and bike crashes) N pedi = Predicted number of vehicle-ped crashes per year N bikei = Predicted number of vehicle-bicycle collisions per year Ci = calibration factor for a particular geographical area Consdieration for Pedestrians and Bicyclists Predicting Crash Frequency for Urban/Suburban Intersections

Prediction of Crash Frequency for Vehicle- Pedestrian Collisions at Urban/Suburban Intersections: Two Separate Procedures for pedestrians based on Intersection Type:  Signalized Intersections N pedi = N pedbase (CMF 1p x CMF 2p x CMF 3p )  Stop – Controlled Intersections N pedi = N bi x f pedi

Prediction of Vehicle-Pedestrian Crash Frequency for Signalized Urban/Suburban Intersections: N pedi = N pedbase (CMF 1p x CMF 2p x CMF 3p ) Where, N pedbase = predicted number of vehicle pedestrian collisions per year for base conditions CMF 1p … CMF 3p = CMFs for vehicle- pedestrian collisions Signalized Intersections

N pedi = N pedbase (CMF 1p x CMF 2p x CMF 3p ) CMF 1p -- accounts for the presence of bus stops (Table 12-28) CMF 2p -- accounts for the presence of schools (Table 12-29) CMF 3p -- accounts for the number of alcohol establishments (Table 12-30) Prediction of Vehicle-Pedestrian Crash Frequency for Signalized Urban/Suburban Intersections:

N pedbase = exp [a + b ln(ADT tot ) + c ln(AADT min /AADT maj ) + d ln(PedVol) + e (n lanesx )] Where, AADT tot = Sum of AADT for major and minor roads (vpd) PedVol = Sum of daily pedestrian volumes crossing each intersection leg (pedestrians/day) N lanesx = Maximum # of traffic lanes crossed by peds in one movement a, b, c, d, e = regression coefficients, Table Prediction of Vehicle-Pedestrian Crash Frequency for Signalized Urban/Suburban Intersections:

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

Estimates of Pedestrian Crossing Volumes:

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 N pedbase = crashes per year = exp [ ln(30,000) ln(5000/25000) ln(235) (5 )] N pedbase = exp [a + b ln(ADT tot ) + c ln(AADT min /AADT maj ) + d ln(PedVol) + e (n lanesx )] Prediction of Vehicle-Pedestrian Crash Frequency for Signalized Urban/Suburban Intersections:

Pedestrian CMFs for Signalized Urban/Suburban Intersections:

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) Prediction of Veh-Ped Crash Frequency for an Urban Multilane Intersection: EXAMPLE N pedbase = crashes per year N pedi = (2.78 x 1.35 x 1.12) = crashes per year N pedi = N pedbase (CMF 1p x CMF 2p x CMF 3p )

Prediction of Vehicle-Bicycle Crash Frequency for Urban/Suburban Intersections: N bikei = N bi x f bikei  N bi = Predicted number of total roadway segment crashes per year  f pedi = Bicycle safety adjustment factor, Table For Bicyclists:

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 Prediction of Crash Frequency for Vehicle-Bicycle Collisions at an Urban/Suburban Intersection = 4.20 x = crashes per year N bikei = N bi x f bikei N bi = N spf int x 0.65 = 6.44 x 0.65 = 4.20 crashes/yr

Predicting Total Crash Frequency for Urban/Suburban At-Grade Intersections N int = (N bi + N pedi + N bikei ) C i 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 = ( ) x 1.0 = 4.44 crashes per year

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

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

Prediction of Vehicle-Pedestrian Crash Frequency for Stop–Controlled Urban/Suburban Intersections: N pedi = N bi x f pedi ► N bi = Predicted number of total roadway segment crashes per year ► f pedi = Pedestrian safety adjustment factor, Table 12-16

Four-Leg Stop Controlled Intersection:  25,000 AADT and 5,000 AADT N bimv = exp( ln(25,000)) ln(5,000) = 4.63 crashes per year N bimv & bisv = exp(a + b ln(AADT maj ) + c ln(AADT min )) Prediction Crash Frequency for an Urban Multilane Intersection – EXAMPLE: N bisv = exp( ln(25,000)) ln(5,000) = 0.38 crashes per year N spf int = = 5.01 crashes per year

N pedi = N bi x f pedi = 5.01 x For a 4-approach stop controlled suburban intersection with N bi = 5.01 = Prediction of Vehicle-Pedestrian Crash Frequency for Stop–Controlled Urban/Suburban Intersections:

Learning Outcomes: ► Described the models to Predict Crash Frequency for Multilane Suburban/Urban Intersections ►Described the Crash Modification Factors for Multilane Suburban/Urban Intersections ► Applied Crash Modification Factors to Predicted Crash Frequency for Multilane Suburban/Urban Intersections Predicting Crash Frequency for Suburban/Urban Multilane Intersections

Questions and Discussion: Predicting Crash Frequency for Suburban/Urban Multilane Intersections