a prioritisation scheme for the safety management of curves presented by: neil jamieson research...
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A PRIORITISATION SCHEME FOR THE SAFETY MANAGEMENT OF CURVES
Presented by:
Neil JamiesonResearch Leader, Tyre-Road InteractionsOpus Central Laboratories
3
Loss of control on curves are the largest cause of injury crashes on NZ rural State Highways!
In 2009:• Amounted to 1309 reported injury crashes• Corresponds to:
– 49% of reported injury crashes on rural SH’s– 36% of all reported injury crashes
• 1210 (92%) occurred on moderate or easy curves• 471 (36%) occurred in wet
Answer
4
For a curve defined as:
• Collective risk = Fatal Crashes + Serious & Minor Injury Crashes on Curve
Number of Years of Data
• Personal risk or crash rate is a measure of the likelihood of an individual road user being involved in a crash as they enter a curve i.e.Personal Risk =
Fatal Crashes + Serious & Minor Injury Crashes on Curve (No. of years of data × 365 days × AADT)/108
Collective and personal risk metrics
5
Curve radius & collective risk
0-50 50-100 100-150 150-200 200-250 250-300 300-350 350-400 400-450 450-500
Fatal 2 7.2 8 11.8 10 10 6.2 4.4 2.6 1
Serious Injury 18.8 35.4 29.6 38.8 33 30.6 22 14.2 8.8 2.4
Minor Injury 54.6 90.4 92.8 107.6 97.2 93.2 70.4 48.6 31.2 13.2
0
20
40
60
80
100
120
Ave
rage
Ann
ual C
rash
No.
(200
4-2
008)
Curve Radius (m)
6
Curve radius & personal risk
0-50 50-100 100-150 150-200 200-250 250-300 300-350 350-400 400-450 450-500
Fatal 0.17 0.90 0.33 0.50 0.44 0.36 0.22 0.17 0.21 0.25
Serious Injury 2.65 2.23 2.00 1.88 1.46 1.16 1.06 0.81 0.65 0.22
Minor Injury 6.12 5.89 4.94 4.01 4.06 3.00 2.93 2.49 2.36 2.15
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00P
erso
nal C
rash
Ris
k 20
04-2
008
(Cra
shes
per
100
Mill
ion
Veh
icle
s E
nter
ing
Cur
ve)
Curve Radius (m)
7
• Relied on T10 specification, which aimed to equalise personal risk across SH network through investigatory skid resistance levels (IL’s).
• Prior to October 2010, curves < 250mR were managed to a skid resistance level that was 25% greater than for all other curves on SH network (IL=0.5 c.f. IL=0.4).
• Curves ≥ 250mR (85 km/h curves) treated the same as straights (event free).
• Too simplistic for “safe system approach”!
Previous safety management of curves
8
Potential for reducing SH crash numbers
0
20
40
60
80
100
120
140
160
180
0
2
4
6
8
10
Cras
h N
umbe
r (20
04-20
08)
Cras
h R
ate
(Cra
shes
per
100
mill
ion
vehi
cles
en
teri
ng th
e cu
rve)
Curve Radius (m)
Crash Rate (Personal Risk) Crash Number (Collective Risk)
T10:2002 Site Cat 2 IL=0.5 T10:2002 Site Cat 4 IL=0.4
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1. All curves < 400mR identified
2. Crash rate calculated using a predictive model which has as inputs:– curve speed (derived from geometry)– curve length– approach gradient (averaged over 100 m prior to curve)– difference between approach speed and curve speed
3. Risk ranking of “high”, “medium” or “low” assigned to each curve on basis of predicted crash rate.
Slides which follow expand on the above three steps
Solution for more effective safety management
10
Locating start of curve
Typical Right Hand Curve
StraightStraight
Spiral SpiralCircular Arc
Tangent PointTangent Point
CSCS
Start of curve “Point where radius < 800m”
11
Estimation of curve radius
Typical Right Hand Curve
StraightStraight
Spiral SpiralCircular Arc
Tangent PointTangent Point
CSCS
Superelevation (crossfall) Averaged over tightest 30mR
Curve Radius Averaged over tightest 30m of the curve
Curve included if <400mR
12
Locating end of curve
Typical Right Hand Curve
StraightStraight
Spiral SpiralCircular Arc
Tangent PointTangent Point
CSCS
End of curve Radius > 800m
13
Curve Crash Rate = (108⁄365)×L1×exp(L2)L1 & L2 are linear combinations of transforms of road characteristics as follows: L1: a constant
square root of curve length
L2: OOCC (i.e. difference between approach & curve speeds)curve speedskid resistanceapproach gradientlog 10 (ADT)yearNZTA administration region
Poisson linear/log-linear model
14
Predicted effects on curve crash rates - ADT
0
5
10
15
20
25
100 1000 10000
Pers
onal
Ris
k(C
rash
es p
er 1
0^8
vehi
cles
ent
erin
g cu
rve)
Average Daily Traffic (vehicles/day)
15
Predicted effects on curve crash rates - SCRIM
0
5
10
15
20
25
0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7
Pers
onal
Ris
k(C
rash
es p
er 1
0^8
vehi
cles
ent
erin
g cu
rve)
Skid Resistance (SCRIM SFC)
16
Predicted effects on curve crash rates – curve length
0
5
10
15
20
25
0 200 400 600 800 1000
Pers
onal
Ris
k(C
rash
es p
er 1
0^8
vehi
cles
ent
erin
g c
urve
)
Length of Curve (m)
17
Predicted effects on curve crash rates – approach gradient
0
5
10
15
20
25
-15 -10 -5 0 5 10 15
Pers
onal
Ris
k(C
rash
es p
er 1
0^8
vehi
cles
ent
erin
g cu
rve)
Approach Gradient (%)
18
Predicted effects on curve crash rates – speed difference
0
5
10
15
20
25
0 10 20 30 40 50
Pers
onal
Ris
k(C
rash
es p
er 1
0^8
vehi
cles
ent
erin
g cu
rve)
Difference between approach and curve speeds (km/h)
19
Observed & modelled crash numbers
0 100 200 300 400 500 600 700
length < 80; AS < 60
AS 60 - 80
AS 80 - 100
AS >= 100
length 80 - 200; AS < 60
AS 60 - 80
AS 80 - 100
AS >= 100
length 200 - 300; AS < 60
AS 60 - 80
AS 80 - 100
AS >= 100
length 300 - 400; AS < 60
AS 60 - 80
AS 80 - 100
AS >= 100
length >= 400; AS < 60
AS 60 - 80
AS 80 - 100
AS >= 100
Number of crashes
Actual
Model
20
Predicted crash rate distribution
0
200
400
600
800
1000
1200
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0-0
0-3
3-6
6-9
9-12
12-1
5
15-1
8
18-2
1
21-2
4
24-2
7
27-3
0
30-3
3
33-3
6
36-3
9
39-4
2
42+
Freq
uenc
y (ba
rs)
Cum
ulati
ve p
ropo
rtion
(lin
e)
Personal Risk (No. crashes per 10^8 vehicles entering curve)
75th percentile25th percentile
21
Predicted Crash Rate(crashes per 100 million vehicle entering curve)
Curve Risk Rating SCRIM Investigatory Level
PCR > 14 High 0.55
7≤PCR≤14 Medium 0.50
PCR < 7, R<250m Low (Cat 2) 0.45
PCR < 7, 250m≤R≤400m Low (Cat 4) 0.40
Default risk ratings of curves and IL’s
22
• High risk curves >250mR lowered to low if speed difference less than 15km/h
• High risk curves >250mR lowered to medium if speed difference below 20km/h
• Medium risk curves (<250mR) raised to high if speed difference greater than 35km/h
• High risk curves <250mR lowered to medium risk if speed difference <20km/h
Moderations to default curve risk ratings
23
Actual injury crash rates versus risk rating
11.12
5.39
2.71 2.380
2
4
6
8
10
12
High Medium Low (250<R<500)
Low (R<250)
Act
ual I
njur
y Cr
ash
Rate
(10^
8 ve
hicl
es e
nter
ing
curv
e)
Curve Risk Rating
24
• Superseded T10:2002– 11800 curves (<250mR)– Approximately 1041 km’s (9.3% of network) , IL=0.5
• T10:2010 (curve risk rating incorporated)– ≈ 17000 curves (≤400mR)– Equates to 2620 km’s (23.4% of network)
• 505 km (4.5% of network) low risk (IL=0.40 or 0.45)• 1365 km (12.2% of network) medium risk (IL=0.50)• 750 km (6.7% of network) high risk (IL=0.55)
Implications for NZ’s rural SH network
25
• Extending <250mR curves (T10:2002 site cat 2 curves) to include transition spiral increases length of network managed to an IL=0.5 from 1041 kms (9.3% of network) to 1699 kms (15.6% of network). However, B/C ≈ 10.
• Applying curve risk rating procedure to extended curves gives B/C≈ 26.
• Targeted skid resistance management of curves seen as a very cost-effective safety measure.
• Curve table incorporated in RAMM to assist industry.
Concluding Remarks