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