continuous risk profile: a simple method for identifying sites for safety investigation. koohong...

Continuous Risk Profile: A Simple Method for Identifying Sites for

Safety Investigation.

Koohong Chung, Ph.D.

California Department of Transportation

Highway Operations

Outline

1. Motivation and Background

2. Continuous Risk Profile

4. Discussion

5. Concluding Remarks

3. Findings

1. Motivation and Background

1. Motivation and Background

“Sliding Moving Window” Approach

0.2 mile

roadway

the reference valuethe number of collision with the window

1. Motivation and Background

“Sliding Moving Window” Approach

0.2 mile

roadway

the reference value

slide the window by small increment of 0.1 mile and repeat the same analysis

0.01 mile the number of collision with the window

<

1. Motivation and Background

“Sliding Moving Window” Approach

0.2 mile

roadway

The site will be reported it to Table-C or Wet Table-C and move the window to the next 0.2 mile segment

the reference valuethe number of collision with the window

>

Task Force (2002) conducted survey among 44 safety engineers

A. Identify sites that are adjacent to each other as one site

B. High false positive rate for both Table-C and Wet Table-C

1. Motivation and Background

1. Motivation and Background

Direction of traffic

Pattern I: Collision causative factor can reside outside of 0.2 mile window.

1. Motivation and Background

Pattern II: Collisions can accompany secondary collisions in the vicinity.

1. Motivation and Background

The collision data on freeways were often spatially correlated.

Direction of traffic

Reference Rate

2. Continuous Risk Profile (CRP)

2. Continuous Risk Profile

Direction of traffic

Cumulative number of Collisions

B(d)A(d)

2. Continuous Risk Profile

Rescaled Cumulative Collision Count Curve (I-880 Northbound, Alameda County, California, 2003)

2.27 7.27 12.27 17.27 22.27 27.27 32.27

A(d

) –

B(d

-d0)

, B(d

-d0)

= 4

0 co

llisi

ons/

mile

0

100

Postmile

2. Continuous Risk Profile

M(d) = 1)/)(,/min()/)(,/min(

)(

0

)/)(,/min(

)/)(,/min( 0

lddlLlddlL

lidf

end

lddlL

lddlLi

end

lkdd 0

Where

l

ddk end 0,...2,1

d0 = beginning postmile

dend = ending postmile

l

LK,

l

dd end 0are integers

f(d) = A(d) – B(d-do)Dstart < Dend

2L = size of the moving average

l = increment

For

and

and

0,)()(

l

dMldMMaxCRP

2. Continuous Risk Profile

M(d) = 1)/)(,/min()/)(,/min(

)(

0

)/)(,/min(

)/)(,/min( 0

lddlLlddlL

lidf

end

lddlL

lddlLi

end

lkdd 0

Where

l

ddk end 0,...2,1

d0 = beginning postmile

dend = ending postmile

l

LK,

l

dd end 0are integers

f(d) = A(d) – B(d-do)Dstart < Dend

2L = size of the moving average

l = increment

For

and

and

0,)()(

l

dMldMMaxCRP

A Method for Generating a Continuous Risk Profile for Highway Collisions (2007) Chung and Ragland

To be Determined , (working paper) Chung, Ragland and Madanat

2. Continuous Risk Profile

0

4 . 5

5

5 . 5

6

6 . 5

7

7 . 5

8

0 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5

1.9

D1

2.0

0 15105 454035302520

Kdr

y(d)

Bd(

d-d 0

) =

34.

1 co

llis

ions

/mil

e

0

3.0

1.0

postmile

By dividing the above CRP by AADT, the unit can be converted to number of collisions per vehicle miles.

3. Findings

Comment from hydraulic division

We were thinking that a plot like these presented to Hydraulics prior to a major rehabilitation project would be ideal in assisting us evaluate and upgrade drainage at the high accident locations as necessary.

…Could I encourage you to have a discussion at the end of your report recommending that Caltrans generate such plots?

It (CRP plot) would help us out immeasurably during design.

-Joseph Peterson, Office Chief ,District 4 Hydraulic-

3. Findings

CRP can be used to identify freeway sites that display high collision rate only under wet pavement condition.

Findings 1:

0

4 . 5

5

5 . 5

6

6 . 5

7

7 . 5

8

0 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5

1.9

D1

2.0

0 15105 454035302520

Kdr

y(d)

Bd(

d-d 0

) =

34.

1 co

llis

ions

/mil

e

0

3.0

1.0

0

2 . 9

3

3 . 1

3 . 2

3 . 3

3 . 4

3 . 5

3 . 6

3 . 7

3 . 8

0 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5

0.32 W1

W2

0.4

0 15105 454035302520

Kw

et(d

)

Bw(d

-d0)

= 4

.5 c

olli

sion

s/m

ile

0

0.8

0.6

0.2

1

1 . 1

1 . 2

1 . 3

1 . 4

1 . 5

1 . 6

1 . 7

1 . 8

0 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5

0 15105 454035302520

Postmile

0.75

0.19

W2*

Kw

et-o

nly(

d)

Bw(d

-d0)

= 4

.5 c

olli

sion

s/m

ile

0

0.4

0.8

0.6

0.2

DRY

WET

WET ONLY

0

4 . 5

5

5 . 5

6

6 . 5

7

7 . 5

8

0 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5

1.9

D1

2.0

0 15105 454035302520

Kdr

y(d)

Bd(

d-d 0

) =

34.

1 co

llis

ions

/mil

e

0

3.0

1.0

0

2 . 9

3

3 . 1

3 . 2

3 . 3

3 . 4

3 . 5

3 . 6

3 . 7

3 . 8

0 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5

0.32 W1

W2

0.4

0 15105 454035302520

Kw

et(d

)

Bw(d

-d0)

= 4

.5 c

olli

sion

s/m

ile

0

0.8

0.6

0.2

1

1 . 1

1 . 2

1 . 3

1 . 4

1 . 5

1 . 6

1 . 7

1 . 8

0 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5

0 15105 454035302520

Postmile

0.75

0.19

W2*

Kw

et-o

nly(

d)

Bw(d

-d0)

= 4

.5 c

olli

sion

s/m

ile

0

0.4

0.8

0.6

0.2

DRY

WET

WET ONLY

“Identification of High Collision Concentration Locations Under Wet Weather Conditions”, Hwang, Chung, Ragland, and Chan

3. Findings

Findings 2:

CRP are reproducible over the years and can proactively monitor traffic collisions.

2.27 7.27 12.27 17.27 22.27 27.27 32.27

2002

2001

2000

1999

1998

1997

1996

1995

1994

2003

Postmile

Year

2.27 7.27 12.27 17.27 22.27 27.27 32.27

2002

2001

2000

1999

1998

1997

1996

1995

1994

2003

Postmile

Year

2.27 7.27 12.27 17.27 22.27 27.27 32.27

2002

2001

2000

1999

1998

1997

1996

1995

1994

2003

Postmile

Year C1C2

2.27 7.27 12.27 17.27 22.27 27.27 32.27

2002

2001

2000

1999

1998

1997

1996

1995

1994

2003

Postmile

Year C1C2

-30 -10-20 10 20 300

0

0.8

Distance Shifted (0.01 miles)

Cor

rela

tion

2000 & 1999

2002 & 2001

2003 & 2002

1999 & 1998

2001 & 2000

2.27 7.27 12.27 17.27 22.27 27.27 32.27

2002

2001

2000

1999

1998

1997

1996

1995

1994

2003

Postmile

Year C1C2

2.27 7.27 12.27 17.27 22.27 27.27 32.27

2002

2001

2000

1999

1998

1997

1996

1995

1994

2003

Postmile

Year C1C2

3. Findings

Findings 3:

CRP plots can be used to capture the “spill over benefit”.

1999

2000

2001

2002

2003

7 8 9 10 11 12 13 14 15 16 17 18

Postmile

1999

2000

2001

2002

2003

7 8 9 10 11 12 13 14 15 16 17 18

Project Completed in 2001

Postmile

1999

2000

2001

2002

2003

7 8 9 10 11 12 13 14 15 16 17 18

Spillover Benefit

Postmile

3. Findings

Findings 4:

Using CRP, you can save time in site investigation.

Direction of Traffic

2003

2002

2001

2000

1999

ON

OFFAccess

7 9 11 13 15 17 19

PM 18.1

PM 17.887 PM 18.141 PM 18.3

Accidents Rate (Accidents/Mile) (SR-91W)

0

200

400

600

800

1000

PDO (per mile) Injury (per mile)

Whole Route

HCCL

4 Times Higher

4 Times Higher

Accidents Data Analysis (PDO)

Frequency Percentage Frequency PercentageNot Stated 1 0% 0 0%

Does Not Apply 413 16% 98 21%Beyond Median or Stripe 5 0% 0 0%

Beyond Shoulder Drivers Left 103 4% 5 1%Left Shoulder Area 1 0% 0 0%

Left Lane 389 15% 38 8%Interior Lanes 797 31% 103 22%

Right Lane 620 24% 203 44%Right Shoulder Area 9 0% 1 0%

Beyond Shoulder Drivers Right 91 4% 8 2%Gore Area 2 0% 1 0%

Other 8 0% 0 0%HOV Lane(s) 134 5% 7 2%

HOV Buffer Area 2 0% 0 0%Total 2575 100% 464 100%

PDO PDO_HCCLCollision Location

2 Times Higher

Accidents Data Analysis (INJURY)

Frequency Percentage Frequency PercentageNot Stated 1 0% 0 0%

Does Not Apply 60 11% 7 9%Beyond Shoulder Drivers Left 90 16% 2 3%

Left Lane 64 12% 9 12%Interior Lanes 122 22% 14 18%

Right Lane 98 18% 38 49%Right Shoulder Area 3 1% 1 1%

Beyond Shoulder Drivers Right 62 11% 2 3%Gore Area 1 0% 0 0%

Other 1 0% 0 0%HOV Lane(s) 46 8% 4 5%

Total 548 100% 77 100%

Collision LocationINJURY INJURY_HCCL

3 Times Higher

Due to the inclined freeway, drivers tend to accelerate

Heavy Vegetations

1) Inclined On-Ramp2) Heavy vegetations

Map of HCCL (SR-91 W)

1) Inclined On-Ramp2) Heavy vegetations

3. Findings

More Findings:

“Comparison of Collisions on HOV facilities with Limited and Continuous Access during Peak Hours”, Jang, Chung, Ragland, and Chan

“Identification of High Collision Concentration Locations Under Wet Weather Conditions”, Hwang, Chung, Ragland, and Chan

4. Discussion

4. Discussion

Highways

Intersections

Ramp

YES (SafetyAnalyst)

Acc

iden

ts P

er M

ile

Per

Yea

r

AADT

+1.5 б

-1.5 бLOSS -I

LOSS -II

LOSS -III

LOSS -IV

SPF

(“Level of Service of Safety”, Kononov and Allery)

4. Discussion

Acc

iden

ts P

er M

ile

Per

Yea

r

AADT

+1.5 б

-1.5 бLOSS -I

LOSS -II

LOSS -III

LOSS -IV

SPF

(“Level of Service of Safety”, Kononov and Allery)

4. Discussion

“The Analysis of Count data: overdispersion and autocorrelation”, Barron

“.. ML estimation of both Poisson and negative binomial regression typically requires independent observations. This assumption will often not be true in time-series data, and Poisson and negative binomial regression are then problematic.”

4. Discussion

Acc

iden

ts P

er M

ile

Per

Yea

r

AADT

Unbiased SPF

biased SPF

biased SPF

4. Discussion

5. Concluding Remark

5. Concluding Remark

CRP is simple to use and provides overview of collision rates of extended segment of freeways over the years.

CRP can identify sites that display high collision rates only under certain condition. (ex: wet hot spots)

CRP can proactively monitor traffic collision rates.

CRP can be used to capture “spill over benefit” of countermeasure.

Spatial correlation is not an issue in constructing CRP

5. Concluding Remark

In future research,

III. Expand CRP approach for CALTRANS intersections and ramp.

I. Continue exploring different areas where CRP can be used.

II. Friendly interface CALTRANS

Thank you!

Q & A