A PRIORITISATION SCHEME FOR THE SAFETY MANAGEMENT OF CURVES

Presented by:

Neil JamiesonResearch Leader, Tyre-Road InteractionsOpus Central Laboratories

2

Why focus on curves?

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

9

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

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• 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

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• 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

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• 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