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EVALUATION OF WIDELY USED HYDROPLANING
RISK PREDICTION METHODS USING FLORIDASPAST CRASH DATA
Presented by:
Waruna Jayasooriya, M.Sc.
Ph.D candidate
Department of Civil and Environmental
Engineering
University of South Florida
Manjriker Gunaratne, Ph.D., P.E.
Professor and Chairman
Department of Civil and Environmental
Engineering
University of South Florida
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Introduction to Hydroplaning
When a tire moving at a certain speed, layer of water
builds between the tire and the road surface
loss of traction and preventing the vehicle from
responding to control inputs
i.e. Steering, braking or accelerating
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Introduction cont...
Viscous hydroplaning (Sliding)
Dynamic hydroplaning
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Objectives
Develop a methodology to capturer the dynamic
hydroplaning crashes
Estimate the accuracy and reliability of most profound
hydroplaning prediction models
Estimates the accuracy of all cross combination
Develop an application to estimate all the combination
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Stages of hydroplaning speed prediction
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Stages of hydroplaning speed prediction
cont...
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Literature review
Equatio
n
Number
SourceModel
StructureEquation Form Variables Applicability Limitations
4.1
British - Road
Research
Laboratory (4)
Empirical
Mean texture depth (MTD) was
not considered.
4.2Empirical form of
PAVDRN (8) Empirical None
4.3Gallaway (2)
(TxDOT method)Analystical None
4.4NZ modified
Equation (3)Empirical None
4.5Analytical form of
PAVDRN (8,10)Analytical None
MTDS
nLIt
6.0
5.01.36
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Equation
Number Source
Model
Structure Equation Form Variables Applicability Limitations
5.1 NASA (11) EmpiricalAverage water film thickness is limited to
7.62 mm.
5.2 Ivey, et al. (12) Empirical None
5.3 NASA (11,13) EmpiricalAverage water film thickness is limited to
7.62 mm.
5.4Agrawal and Henry
(14,15)Empirical
Maximum water film thickness of 2.4 mm
5.5 Wambold et al. (16) Empirical
5.6 Horne, et al. (13) EmpiricalApplicable for the truck tires with a
fixed water film thickness.
5.7 PAVDRN (10,15) Analytical
Five pavement sections can be
analyzed by using PAVDRN: (a)
tangent section, (b) horizontal curve,
(c) transition section, (d) vertical crest
curve, and (e) vertical sag curve.
5.8Gallaway (2)
(TxDOT method)Analytical
Limited to vehicle speeds of less than
55 mph, 10% SD used as an indicator
of hydroplaning.
Tread depth of 2/32 inches used in
design.
5.9
USF Gunaratne et al.
(7) simplified form
based on Ong and Fwa
(6)
Empirical/
Finite
Element
Applicable to the light vehicles that
employ tires that are compatible with
locked-wheel tester tires.8
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Methodology
1. Development of hydroplaning crash database
a) Florida Department of Transportation (FDOT)
databases
b) Weather information
c) Field observations
2. Analyze each hydroplaning crash with the
existing models and its combination
3. Evaluate the sensitivity and prediction accuracy
of the model combinations9
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Databases (FDOT)
Crash Analysis and Reporting System (CARS) database:
Pavement Condition Survey (PCS) database:
Geographical Information System (GIS) database:
Vehicle, Passenger and Driver (VPD) Information database:
Police long-form (reports) database:
As-built plans
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11
CARSDatabase
Police
Long-form
Weather Sta.
Database
GISDatabase
As-built
Plans
VPDDatabase
PCSDatabase
1-Interstate
Network
5-Estimation of
WFT, Use of
PAVDRN
equation
2-Screening Parameters
Weather Condition: Rainy
Surface Condition: Wet, Slippery
Max.Posted Speed: >= 40mphLighting Condition
Drug or Alcohol ExcludedSite Loc. Ramps Excluded
6-Compare lagging
distance with SSD
Wider roadway
section & HOV lanedesignation
Identification of Inward
sloped sections
4-Identification of closest
weather stations
3-Location details
Side of the roadPavement distresses Vehicle information
Driver, Passenger details
7-Classification of Hyd.
Crash type
Hydroplaning Crash
Database 2006 - 2011
Rainfall Intensity,Visibility, Wind
Dynamic hyd.
Viscous hyd.
Vehicle damage,Tire condition,
Vehicle movement
damage severity
IRI, Rut number, Ride
rating, Crack rating
WFT
Flow Chart Key
Database
Screening Criteria
Outcome variable
Combined Database
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Separation of Hydroplaning Crash Type
(Manual)
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(a) Dynamic hydroplaning event (b) Viscous hydroplaning event
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Assignment of Rainfall Intensity for Each
Crash Weather data interpolation
Inverse squared distance interpolation
Thiessen polygon Method
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Sample of Hydroplaning Database
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Crash ID CrashRate Traffic Densit rate for 10ACCISEV HYD TYPE Travel LanACCLANE Num of LaTravel SpeMAXSPEE CS Trav la Rainfall InWFT (mm Visibility ( Pav Mat
71094140 0.00442982 225.7428 44 2 1 2 S 2 60 70 2.0% 0 2.5 #DIV/0! OGFC
85275710 0.004016064 249 40 1 1 2 S 2 65 70 2.0% 0.34 2.95 0.34 OGFC
90465020 0.007297752 137.0285 73 1 1 1 S 3 50 70 2.0% 0.07 2.65 0.07 OGFC
92895220 0.002024038 494.06175 20 1 1 3 S 4 30 45 3.0% 1.05 1.6 0.420476 OGFC
95164550 0.001498641 667.271 15 1 1 2 S 2 70 70 2.0% 0.01 2.6 0.01 OGFC
143399430 0.000873675 1144.591 9 3 0 1 1 3 45 55 2.0% 0.4 0.7 0.225595 OGFC
603599490 0.000512509 1951.1865 5 3 0 4 4 5 30 65 3.0% 0.15 0.7 0.083824 OGFC
700168610 0.001268081 788.593 13 1 1 2 S 2 55 70 2.0% 0.21 2.85 0.21 OGFC700625690 0.000735328 1359.936963 7 1 1 1 M 3 30 45 2.0% 0 0 #DIV/0! OGFC
700786450 0.002607905 383.4495 26 1 1 1 M 3 50 55 2.0% 0 0 #DIV/0! PCC
701512880 0.00697715 143.325 70 1 1 3 2 4 40 50 3.0% 0 0 #DIV/0! PCC
704357700 0.000677199 1476.6705 7 1 0 2 2 4 60 65 2.0% 0.18 0.6 0.143381 OGFC
704556370 0.001511001 661.81275 15 1 1 2 2 3 70 70 2.0% 0.04 2.65 0.021543 OGFC
704970550 0.002305248 433.7928 23 3 1 5 S 5 65 65 3.5% 0.08 0.6 0.056359 OGFC
704972150 0.005057694 197.7185595 51 1 0 2 2 4 65 55 2.0% 0.08 0.4 0.037447 OGFC
707872010 0.000436085 2293.13 4 2 0 1 1 3 15 55 2.0% 0 0 #DIV/0! OGFC
707882860 0.000779837 1282.32 8 1 1 2 S 3 65 55 2.0% 0.48 1 0.392594 OGFC
707899220 0.000605179 1652.4025 6 3 0 1 1 3 40 55 2.0% 0.24 2.75 0.24 OGFC
709963360 0.002688129 372.006 27 2 1 2 S 2 70 70 2.0% 0.27 2.9 0.233073 OGFC
710451930 0.001465532 682.346 15 1 1 1 1 3 70 70 2.0% 0.21 2.75 0.168089 OGFC
711178700 0.002385804 419.146 24 1 1 1 S 3 55 70 2.0% 0 2.5 #DIV/0! OGFC
711184800 0.001269218 787.887 13 2 1 2 S 3 70 70 2.0% 0.81 3.15 0.652484 OGFC
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Content of Hydroplaning Database
Hydroplaning type
Posted speed/ Travel speed
Rainfall intensity Number of lanes/Travel lane
Pavement material
Dense graded asphalt concrete (DGAC)
Portland Cement Concrete (PCC) Open graded friction course (OGFC)
Pavement distresses (Roughness, IRI)16
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Sensitivity Analysis of Model combinations
The specific values of the variables closest to their averages in
the entire dynamic hydroplaning crash database were assumed
as the base values listed below.
Rainfall intensity, I = 2 inches/hr.
Pavement surfacing type = DGAC
Travel lane (from median of the road) = 2
Longitudinal slope, s = 0.5 %
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Summary Results of Sensitivity Analysis
Model Combination1 2 3 4 5 6 7 8 9 10 11 12
Stage 1
(WFT prediction)Gallaway (Eqn. 4.3) British-RRL (Eqn. 4.1) NZ modified (Eqn. 4.4) PAVDRN (Eqn. 4.5)
PAVDRN
(Eqn. 5.7)
Gallaway
(Eqn. 5.8)
USF
(Eqn. 5.9)
PAVDRN
(Eqn. 5.7)
Gallaway
(Eqn. 5.8)
USF
(Eqn. 5.9)
PAVDRN
(Eqn. 5.7)
Gallaway
(Eqn. 5.8)
USF
(Eqn. 5.9)
PAVDRN
(Eqn. 5.7)
Gallaway
(Eqn. 5.8)
USF
(Eqn. 5.9)
Rainfall
Intensity(in/h) 0.1 (-95%) 41.9%* 6.6% 16.6% 51.2% 6.2% 13.0% 29.1% 3.8% 7.9% 50.4% 6.2% 12.8%
0.5(-75%) 11.3% 3.0% 8.5% 20.5% 2.7% 5.7% 12.6% 1.7% 3.6% 20.3% 2.7% 5.7%
1(-50%) 0.4% 1.5% 5.2% 9.7% 1.3% 2.8% 6.1% 0.9% 1.8% 9.6% 1.3% 2.8%
3(50%) -1.8% -0.8% -1.1% -5.2% -0.8% -1.6% -3.4% -0.5% -1.0% -5.2% -0.8% -1.6%
4(100%) -3.1% -1.4% -1.8% -8.8% -1.3% -2.7% -5.7% -0.8% -1.8% -8.7% -1.3% -2.7%
Pavement
Type PCC - - - - - - - - - 20.5% 2.7% 5.7%
OGFC 6.9% -0.2% -0.3% 0.5% 0.1% 7.6% 0.0% 0.0% 7.4% 5.0% 0.7% 9.0%
TravelLane 1 8.8% 2.6% 7.7% 20.5% 2.7% 5.7% 19.7% 2.7% 5.5% 18.1% 2.4% 5.1%
3 -2.8% -1.3% -1.7% -9.6% -1.4% -3.0% -9.1% -1.4% -2.8% -8.3% -1.2% -2.6%
4 -4.7% -2.3% -2.9% -12.3% -2.4% -7.8% -15.2% -2.3% -4.8% -11.0% -2.1% -7.3%
5 -5.5% -3.0% -3.7% -13.7% -3.2% -8.7% -16.6% -3.1% -9.1% -12.3% -2.8% -8.1%
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Model prediction accuracy
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Comparison of model combinations with
accuracy and sensitivity model combination 1
shows an average
sensitivity with the highest
prediction accuracy. model combinations 4 and
10 also show high
sensitivity to rainfall
intensity and travel lane
while exhibiting a lowprediction accuracy.
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Descriptive Statistics of Error Distribution
Model Combination 1 2 3 4 5 6 7 8 9 10 11 12
Mean (mph) 4.34 6.99 14.23 16.03 8.97 20.88 12.00 8.54 19.71 12.39 8.63 19.88
Median (mph) 0.00 4.32 13.29 13.48 6.98 19.82 10.63 6.33 18.54 10.69 6.53 18.47
Mode (mph) 0.00 0.00 0.00 0.00 0.00 20.48 0.00 0.00 16.62 0.00 0.00 11.35
Std. Dev. 7.16 7.55 9.33 14.20 8.11 9.29 10.90 7.99 8.85 11.12 8.06 8.79
5% percentile (mph) 0.00 0.00 0.00 0.00 0.00 7.44 0.00 0.00 7.48 0.00 0.00 7.74
95% percentile (mph) 20.56 22.15 31.04 42.99 24.65 37.49 32.80 23.93 35.96 33.71 24.12 36.03
Range (mph) 42.65 32.61 44.30 69.22 35.42 51.79 51.32 34.73 49.22 58.50 34.91 48.30
ICC* 0.733 0.114 0.408 0.427 0.133 0.260 0.518 0.082 0.245 0.420 0.040 0.184
*Intraclass Correlation Coefficient
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Error distributions in each model
combination
Based on the ICC and the
model prediction accuracy
the model combinations canbe ordered as 1,4,10 and 3
in terms of accuracy.
fFrequency distribution of
hydroplaning crashes,Error in speed prediction with
respect to predicted threshold
hydroplaning speed22
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Development an application on analysis of
minimum Hs
Input parameters
Pavement properties
Geometrical properties Vehicle characteristics
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Conclusions
Floridas CARS database was fortified with using (1) PCS (2)
CARS (3) GIS (4) VPD databases and (5) Police long-forms.
Two stage process of prediction generated twelve possible
model combinations.
Sensitivity analysis performed on the threshold hydroplaning
speed on the key parameters.
Reliability of each model combination was evaluated by using
intra-class correlation coefficients of the resulting error
distributions.
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Conclusions cont..
Prediction accuracy of model combinations was alsoevaluated
A computer program (HP) was developed to compute thepossible hydroplaning speed for each model combination
Model combinations were more accurate in predictinghydroplaning risk.
Provide tools to evaluate the hydroplaning risk on a givenhighway under typical adverse weather conditions withimproved reliability.
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Thank you
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Acknowledgement
This research project was funded by Florida department of
transportation (FDOT)
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