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Continuous Risk Profile: A Simple Method for Identifying Sites for Safety Investigation. Koohong Chung, Ph.D. California Department of Transportation Highway Operations. Outline. 2. Continuous Risk Profile . 3. Findings. 1. Motivation and Background. 4. Discussion . 5. Concluding Remarks .

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Continuous risk profile a simple method for identifying sites for safety investigation

Continuous Risk Profile: A Simple Method for Identifying Sites for Safety Investigation.

Koohong Chung, Ph.D.

California Department of Transportation

Highway Operations


Outline

Outline Sites for Safety Investigation.

2. Continuous Risk Profile

3. Findings

1. Motivation and Background

4. Discussion

5. Concluding Remarks


1 motivation and background

1. Motivation and Background Sites for Safety Investigation.


1 motivation and background1

Sites for Safety Investigation.Sliding Moving Window” Approach

1. Motivation and Background

0.2 mile

roadway

the number of collision with the window

the reference value


1 motivation and background2

Sites for Safety Investigation.Sliding Moving Window” Approach

1. Motivation and Background

0.2 mile

roadway

the number of collision with the window

<

0.01 mile

the reference value

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


1 motivation and background3

Sites for Safety Investigation.Sliding Moving Window” Approach

1. Motivation and Background

0.2 mile

roadway

the number of collision with the window

>

the reference value

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


1 motivation and background4

1. Motivation and Background Sites for Safety Investigation.

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

Task Force (2002) conducted survey among 44 safety engineers

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


1 motivation and background5

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

1. Motivation and Background

Direction of traffic


1 motivation and background6

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

1. Motivation and Background


1 motivation and background7

The collision data on freeways were often spatially correlated.

1. Motivation and Background

Reference Rate

Direction of traffic


2 continuous risk profile crp

2. Continuous Risk Profile correlated. (CRP)


2 continuous risk profile

2. Continuous Risk Profile correlated.

A(d)

B(d)

Cumulative number of Collisions

Direction of traffic


2 continuous risk profile1

2. Continuous Risk Profile correlated.

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


2 continuous risk profile2

CRP correlated.

M(d) =

2. Continuous Risk Profile

For

and

Where

Dstart < Dend

l = increment

f(d) = A(d) – B(d-do)

d0 = beginning postmile

dend = ending postmile

2L = size of the moving average

and

K,

are integers


2 continuous risk profile3

CRP correlated.

M(d) =

2. Continuous Risk Profile

For

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

and

Where

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

Dstart < Dend

l = increment

f(d) = A(d) – B(d-do)

d0 = beginning postmile

dend = ending postmile

2L = size of the moving average

and

K,

are integers


2 continuous risk profile4

2. Continuous Risk Profile correlated.

postmile

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


3 findings

3. Findings correlated.


Comment from hydraulic division

Comment from hydraulic division correlated.

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 findings1

3. Findings correlated.

Findings 1:

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


DRY correlated.

WET

WET ONLY


DRY correlated.

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

WET

WET ONLY


3 findings2

3. Findings correlated.

Findings 2:

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


3 findings3

3. Findings correlated.

Findings 3:

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


Postmile correlated.


Project Completed in 2001 correlated.

Postmile


Spillover Benefit correlated.

Postmile


3 findings4

3. Findings correlated.

Findings 4:

Using CRP, you can save time in site investigation.


Access correlated.

OFF

ON

2003

2002

2001

2000

1999

Direction of Traffic

PM 18.1


PM 17.887 correlated.

PM 18.141

PM 18.3


Accidents rate accidents mile sr 91w
Accidents Rate (Accidents/Mile) (SR-91W) correlated.

4 Times Higher

4 Times Higher


Accidents data analysis pdo
Accidents Data Analysis (PDO) correlated.

2 Times Higher


Accidents data analysis injury
Accidents Data Analysis (INJURY) correlated.

3 Times Higher


Due to the inclined freeway, correlated.

drivers tend to accelerate


Heavy Vegetations correlated.


1) Inclined On-Ramp correlated.

2) Heavy vegetations


Map of hccl sr 91 w
Map of HCCL (SR-91 W) correlated.

1) Inclined On-Ramp

2) Heavy vegetations


3 findings5

3. Findings correlated.

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 correlated.


4 discussion1

4. Discussion correlated.

Highways

Intersections

YES (SafetyAnalyst)

Ramp


4 discussion2

4. Discussion correlated.

LOSS -IV

+1.5 б

LOSS -III

SPF

Accidents Per Mile Per Year

LOSS -II

-1.5 б

LOSS -I

AADT

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


4 discussion3

4. Discussion correlated.

LOSS -IV

+1.5 б

LOSS -III

SPF

Accidents Per Mile Per Year

LOSS -II

-1.5 б

LOSS -I

AADT

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


4 discussion4

4. Discussion correlated.

“.. 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.”

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


4 discussion5

4. Discussion correlated.

biased SPF

Unbiased SPF

Accidents Per Mile Per Year

biased SPF

AADT



5 concluding remark1

Spatial correlation is not an issue in constructing CRP correlated.

5. Concluding Remark

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

CRP can proactively monitor traffic collision rates.

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

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


5 concluding remark2

In future research, correlated.

5. Concluding Remark

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

II. Friendly interface CALTRANS

III. Expand CRP approach for CALTRANS intersections and ramp.


Thank you

Thank you! correlated.

Q & A


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