topic 2 – network screening cee 763

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CEE 763 Fall 2011

Topic 2 – Network Screening

CEE 763

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CEE 763 Fall 20112

OBJECTIVES

Identify locations for further study which have both A high risk of crash losses An economically justifiable opportunity for

reducing the risk

Identify countermeasure options and priorities which maximize the economic benefits

It is as much about exclusion of sites from consideration as it is about inclusion

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NETWORK SCREENING

Key tool in a highway safety improvement program

Definition– A process which aims to identify locations

within the road system where correctable crashes are found in order to develop appropriate and cost-effective treatments to reduce the frequency or severity of crashes

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EFFECTIVENESS

It is important to identify sites with the most “promise” for improvement as engineering studies are expensive. Agencies have limited budgets, and if a site with potential is not identified, an opportunity to substantially improve safety is missed.

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SOME TYPICAL NAMES

High crash location

High accident potential

Black spot

High risk location

Top 5%

Crash concentration

5

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Terms: Site and Facility

Site – a basic safety study location, e.g., a segment (homogeneous), an intersection, and a freeway ramp

Facility – a contiguous set of sites Freeway (segments, ramps)

Urban and suburban arterials (segments, intersections): divided, undivided, signalized, TWSC etc.

Rural highway (segments): two-lane, multi-lane

HSM only covers predictive methods for certain facility types

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CEE 763 Fall 2011

NETWORK SCREENING PROCESS

Establish focus Sites with potential to reduce crash frequency

Specific crash types or severity

Identify sites and reference population Type of site: segments, intersections, ramps

Sites of similar characteristics

Select performance measures Frequency, rate, severity, etc.

Select screening method Ranking, sliding window, peak searching etc.

Screen and evaluate results

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ESTABLISH FOCUS

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PERFORMANCE MEASURES

Crash frequency*

Crash rate*

Quality control* Excess predicted crash frequency using method of moments Critical rate

Crash severity* Equivalent property damage only (EPDO) crash frequency Relative severity index

Level of service of safety

Excess predicted average crash frequency using SPFs*

Probability of specific crash types exceeding threshold proportion

Excess proportion of specific crash types

Expected crash frequency with EB adjustment*

Excess expected crash frequency with EB adjustment

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CEE 763 Fall 201110

CRASH FREQUENCY

Method Rank locations with highest count of crashes for

investigation

Benefits Simple Focuses on areas with most crashes

limitations Does not account for exposure Favors high-volume, urban locations Engineering fix may not be present

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CRASH RATE

Method Rank locations by rate of crashes

Benefits Accounts for exposure Relatively simply Efforts focused on potential problem not just high volume

locations

Limitations Favors low volume, low collision sites Cannot compare cross different volumes

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INTERSECTION RATES

Crashes per million entering vehicles (MEV)

MEV

NRi

Ri = intersection crash rate

N = number of crashes in the study periodn = number of years in the study periodTEV = the sum of volumes entering from all approaches,

in Average Daily Traffic

000,000,1

365

nTEVMEV

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EXAMPLE

Observed 46 crashes in two years. The ADT for the minor approach was 3000 and the major approach was 6000. Note - volumes includes both directions. What is the crash rate?

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SEGMENT RATES

Crashes per million vehicle miles of travel (MVMT)

Example Observed 40 crashes on a 17.5 mile segment in one year. The ADT was 5,000.

MVMT

NRs

000,000,1

365

nLVMVMT

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CRASH AND VOLUME

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CEE 763 Fall 201116

FREQUENCY-RATE CRITERIA

Method Rank by combination of frequency and rate based methods Various ways to combine rankings for composite rankings

Benefits Simple Address drawbacks of both the frequency and rate methods

Drawbacks Final ranking dependent of combination

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EXAMPLE

Five intersections have the following crash frequency and crash rate.

If a critical frequency is set at 10, and a critical crash rate is set at 1.5, which intersection(s) should be ranked as high crash locations?

Crash Data

Intersections

1 2 3 4 5

Frequency 7 12 4 14 10

Rate 0.5 1.5 2.1 1.0 1.8

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CEE 763 Fall 201118

QUALITY CONTROLRate or Frequency

Method Rank location if the crash rate or frequency at a site is

statistically significantly higher than a predetermined rate or frequency for locations of similar characteristics

Benefits Based on Poisson distribution Seems to identify locations with possible treatments

Drawbacks More data is required Categorization is key

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CEE 763 Fall 201119

QUALITY CONTROL

Method 1) Select average rate or frequency for similar facility 2) Calculate the critical rate or frequency 3) Compare actual rate or frequency 4) Flag or rank if exceeds

MM

RPRR a

aC 2

1

RC = critical rate or critical frequency

Ra = the average rate or frequency for similar facility

P = probability constant based on desired level of significance (1.645 for 95%)

M = millions of VMT or entering vehicles

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EXAMPLE

There were 40 observed crashes on a 17.5 mile segment in one year. The ADT was 5,000. Given the average rate for similar segments is 1.02 MVMT, does the subject segment exceed the critical rate at 95% confidence?

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CEE 763 Fall 201121

SEVERITY

Method Rank locations by weighting the severity of crashes

Benefits Adds severity to the frequency method Usually relates to benefit/cost selection

Drawbacks Dependent on weighting, may concentrate on fatal collisions Weights are essentially arbitrary since it assigned from global

crash costs

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EQUIVALENT PROPERTY DAMAGE ONLY (EPDO) CRASH FREQUENCY

i

iiNfEPDO

EPDO = Equivalent property damage only crashesfi = weight for crash type INi = number of crashes of type i

Severity Cost Weight

Fatal (K) $4,008,900 542

Injury (A,B,C) $82,600 11

PDO (O) $7,400 1

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CEE 763 Fall 201123

EXAMPLE

A location has experienced 2 fatal, 12 injury A, 30 injury B, 40 injury C, and 140 PDO crashes in 5 years. What is the EPDO crashes?

• Fatal = $3,400,000

• A = $260,000

• B = $56,000

• C = $27,000

• PDO = $4,000

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CEE 763 Fall 201124

RELATIVE SEVERITY INDEX (RSI)

i

n

jj

i N

RSI

RSI

1

= relative severity index cost for intersection i

RSIj = relative severity index cost for crash type j

iRSI

Crash Type Number of Crashes

Cost per Crash

Rear End 19 $13,200

Sideswipe 7 $34,000

Angle 5 $61,100

Fixed Object 3 $94,700

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RSI EXAMPLE

Crash Type Number of Crashes

Cost per Crash

Rear End 19 $13,200

Sideswipe 7 $34,000

Angle 5 $61,100

Fixed Object 3 $94,700

An intersection has the following crashes. Determine the RSI for this intersection

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CEE 763 Fall 201126

SAFETY INDICES

Method Rank locations by creating an index which includes a number of

factors such as rates, frequencies, severities, and possibly site data. A weighted average or scores are then combined to calculate a composite index. The “Relative Severity Index” discussed earlier is one of these types.

Benefits Simple and attempts to combine criteria

Drawbacks Rank is sensitive to weights of scores which are usually

assigned “arbitrarily”

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*ODOT SAFETY PRIORITY INDEX SYSTEM(SPIS)

Composite score assigned for frequency, severity, and rate

3 years data, 0.10 mile sections Maximum index is 100

• 25 points max for frequency

• 25 points max rate

• 50 points max severity

Total score = Sum of Indicator values (IV) of Frequency, Rate, and Severity

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CEE 763 Fall 201128

*SAFETY PRIORITY INDEX SYSTEM

25

1150

1,25min

LOG

esTotalCrashLOGIVFreq

2517

13653

000,000,1

,25minLOG

ADTdaysyresTotalCrash

LOG

IVRate

50

300

10100,50min

PDOINJINJINJFATALIV CBA

Severity

Note: Max SPIS score is 100

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EXAMPLE

0 Fatal, 1 A, 0 B, 3 C, 4 PDO. ADT 14,200.

25

1150

1,25min

LOG

esTotalCrashLOGIVFreq

2517

13653

000,000,1

,25minLOG

ADTdaysyresTotalCrash

LOG

IVRate

50

300

10100,50min

PDOINJINJINJFATALIV CBA

Severity

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EXAMPLE

95.1025

1150

18

LOG

LOGIVFreq

99.42517

1200,143653

000,000,18

LOG

daysyrLOG

IVRate

33.2250300

431010100

SeverityIV

0 Fatal, 1 A, 0 B, 3 C, 4 PDO. ADT 14,200.

Answer: SPIS Score = 38.27

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POTENTIAL ACCIDENT REDUCTION

Method Rank or flag locations where the difference between observed

and expected crash experience will maximize benefits if their crash history can be reduced to the expected value.

Benefits Most uses frequency rather than rates Can account for “regression to the mean”

Drawbacks Data hungry, expected values must be predicted

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EXCESS PREDICTED CRASH FREQUENCY USING METHOD OF MOMENTS

Calculate average crash frequency per reference population Calculate crash frequency variance

Calculate adjusted observed crash frequency per site

Calculate potential for improvement (PI) per site

Rank site according to PI (highest to lowest)

)( obspapa

obsadj NNVAR

NNN

observedN

averagepopulationN

adjustedN

obs

pa

adj

paadji NNPI

1

)(1

2,

n

NNVAR

n

ipaiobs

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CEE 763 Fall 201133

EXAMPLE

An unsignalized intersection has observed 11 crashes in a year. Suppose among all the unsignalized intersections, the average crashes per year is 8, and the standard deviation of crash for all the intersections is 3. Calculate the PI for this intersection.

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CEE 763 Fall 201134

EXCESS PREDICTED CRASH FREQUENCY USING SAFETY PERFORMANCE FUNCTIONS

Calculate expected crash frequency using SPF Calculate excess predicted average crash

frequency

Rank site according to the excess frequency

ectedexpi,obsi,excess NNN

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CEE 763 Fall 201135

EXAMPLE

An unsignalized intersection has observed 11 crashes in a year. According to the SPF developed for all the unsignalized intersections, the predicted crash frequency per year is 8. What is the excess predicted crash frequency?

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EMPIRICAL BAYES METHODS

Volume

Cra

sh F

requ

ence

E() -Modeled # of crashes

SPF

K - Observed # of crashes

is best estimate for the expected # of

crashes

K)()k(E}K/k{E 1

}k{E}k{VAR

Y

1

1

/)(1

1

kYE

E(k) is the predicted value at similar sites, in crash/year

Y is the analysis period in number of years

φ is over-dispersion factor

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SAMPLE DATA

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CEE 763 Fall 2011

SAMPLE DATA

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CEE 763 Fall 2011

CRASH FREQUENCY WITH EB ADJUSTMENT

Step 1 – Calculate the predicted average crash frequency using an SPF

Step 2 – Calculate annual correction factor

1,predicted

n,predictedn N

NC

Year Predicted Average Correction factor

123

2.52.52.7

1.01.0?

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CEE 763 Fall 2011


Step 3 – Calculate EB weighting factor,Note: rely on dispersion factor or variance.

Year Predicted Average

123

2.52.52.7

N

nn,predictedN/

/)k(YE

1

11

1

1

1

4901 ./factorDispersion

?N/

N

nn,predicted

1

11

1

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CEE 763 Fall 2011


Step 4 – Calculate first year EB adjusted average crash frequency.

Year Predicted Average Observed Crashes

123

2.52.52.7

119

14

N

nn

N

nn,observed

,predicted,ectedexp

C

N)(NN

1

111 1

?N ,ectedexp 1

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CEE 763 Fall 2011


Step 5 – Calculate final year EB adjusted average crash frequency.

Step 6 – Calculate the variance (optional)

Step 7 - Rank sites based on the EB adjusted expected average crash frequency for the final year.

n,ectedexpn,ectedexp C*NN 1

n

nn,ectedexp

th

C

C)(*N)yearn(Var 1

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OTHER CRITERIA

Level of service safety (LOSS) Konokov et al. (Colorado DOT)

Method of moments PIARC manual

Proportions testing Exceeding a particular crash type

Rank locations bases on the current annual cost of crashes based on average cost of crash by accident type

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WHICH CRITERIA TO USE?

Little consensus on methods

The key issue is how the criteria adopted direct the analyst to consider sites which contributes to the overall road safety goal, namely the maximization of benefits of road safety treatments

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METHOD USAGE

All of the methods are in use either alone or in combination In US states

• Crash frequency by 15%

• Crash rate or RQC by 15% of agencies

• Crash severities by 50% of agencies

• Indices by 18%

• Other by 16%

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CEE 763 Fall 2011

MORE PRECISE DEFINATION OF SITE

Three alternatives (Hauer et al., TRR 1784 – Screening the road network for sites with promise) Based on “Section” Based on a uniform length of a roadway, e.g.,

0.1 mi Based on a minimum segment that identifies

the highest accident frequency while satisfying the statistical limits (i.e., CV).

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CEE 763 Fall 2011

SEARCHING ALGORITHMS

Expected

Segment average

Expected

Segment average

Segment average does not correspond to the highest

Segments of different length with the highest crash

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CEE 763 Fall 2011

SLIDING WINDOW0.3-mile window with 0.1 increment

0.0 mi 0.1 mi 0.2 mi 0.3 mi 0.4 mi 0.5 mi 0.6 mi

Win # 1

Win # 2

Win # 3

Win # 4

Roadway Segment

*The window that has the highest risk is used to rank the segment.

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CEE 763 Fall 2011

EXAMPLE

A roadway network has ten segments composed of three types of facilities. Using the sliding window method and the crash rate to rank Segments 1 and 2.

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More Data

Segment 1 starts at mile post 1.2 and ends at 2.0. Segment 2 starts at mile post 2.0 and ends at 2.4.

1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4

Segment 1 Segment 2

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CEE 763 Fall 2011

SLIDING WINDOW0.3-mi window with 0.1-mi increment

Site No. 1

MP 1.0 MP 2.6

First Sliding WindowW = 0.3 mi

Second Sliding WindowW = 0.3 mi

0.1 mi 0.2 mi 0.3 mi 0.4 mi 0.5 mi

Sliding window is moved incrementallyby 0.1 mi along the roadway segment.

Site No. 1

MP 1.0 MP 2.6



0.1 mi 0.2 mi 0.3 mi 0.4 mi 0.5 mi

Site No. 1

MP 1.0 MP 2.6



0.1 mi 0.2 mi 0.3 mi 0.4 mi 0.5 mi

Sliding window is moved incrementallyby 0.1 mi along the roadway segment.

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Site No. 11 Site No. 12

MP 21.4 MP 22.2 MP 23.0

Sliding WindowW = 0.3 mi


MP 21.4 MP 22.2 MP 23.0

Sliding WindowW = 0.3 mi

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MP 35.4 MP 36.2 MP 36.7MP 36.37

Site No. 25

0.1 mi 0.17 mi 0.03 mi


MP 35.4 MP 36.2 MP 36.7MP 36.37

Site No. 25

0.1 mi 0.17 mi 0.03 mi

Sliding Window Concepts: Bridging Three Contiguous Roadway Segments

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CEE 763 Fall 2011


0.3 mi


MP 53.5 MP 54.3 MP 54.48

Site No. 33

0.2 mi 0.1 mi


MP 53.5 MP 54.3 MP 54.48

Site No. 33


MP 53.5 MP 54.3 MP 54.48

Site No. 33

0.1 mi 0.18 mi

A

B

C

0.3 mi


MP 53.5 MP 54.3 MP 54.48

Site No. 33

0.2 mi 0.1 mi


MP 53.5 MP 54.3 MP 54.48

Site No. 33


MP 53.5 MP 54.3 MP 54.48

Site No. 33

0.1 mi 0.18 mi

A

B

C

Sliding Window Concepts: Window Positions at the End of Contiguous Roadway Segments When Window is Moved Incrementally by 0.1 Miles

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CEE 763 Fall 2011



MP35.5

MP36.2

MP36.7

Window No. 1

Window No. 2

Window No. 3

Window No. 4

Window No. 5

Window No. 6

Window No. 7

Window No. 8

Window No. 9

Window No. 10

Window No. 11

Window No. 12

Window No. 13


MP35.6


MP35.5

MP36.2

MP36.7

Window No. 1

Window No. 2

Window No. 3

Window No. 4

Window No. 5

Window No. 6

Window No. 7

Window No. 8

Window No. 9

Window No. 10

Window No. 11

Window No. 12

Window No. 13


MP35.6

Sliding Window Concepts: Example of Position and Location of Sliding Windows and Subsegments

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CEE 763 Fall 2011



MP35.5

MP36.2

MP36.7

Window No. 1

Window No. 2

Window No. 3

Window No. 4

Window No. 5

Window No. 6

Window No. 7

Window No. 8

Window No. 9

Window No. 10

Window No. 11

Window No. 12

Window No. 13


MP35.6

74.36)( )( EPDOYXSum













Note: Sum(XY(EPDO)) expressed as acc/mi


MP35.5

MP36.2

MP36.7

Window No. 1

Window No. 2

Window No. 3

Window No. 4

Window No. 5

Window No. 6

Window No. 7

Window No. 8

Window No. 9

Window No. 10

Window No. 11

Window No. 12

Window No. 13


MP35.6


MP35.5

MP36.2

MP36.7

Window No. 1

Window No. 2

Window No. 3

Window No. 4

Window No. 5

Window No. 6

Window No. 7

Window No. 8

Window No. 9

Window No. 10

Window No. 11

Window No. 12

Window No. 13


MP35.6














Note: Sum(XY(EPDO)) expressed as acc/mi

Sliding Window Concepts: Ranking Example

limiting value: 40 acc/mi/yr

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CEE 763 Fall 2011

EXAMPLE

A segment with 2 lanes, rural

ADT= 6000

Limiting frequency: 10

SPF:

Intercept:-3.63

ADT coefficient: 0.53

Over dispersion Parameter: 0.5

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CEE 763 Fall 2011

0.0430.1470.2310.2310.2400.2510.2510.2870.2870.2870.3100.3110.3250.3290.4330.4340.4400.4400.4400.4410.4520.4540.4830.493

0.533

0.598

0.636

0.636

0.658

0.743

0.806

0.806

0.808

0.822

0.823

0.848

0.862

0.862

0.901

0.948

0.983

EXAMPLE

Site A: 0-0.4 mile

Site B: 0.4-0.9 mileContiguous

Site C: 0.9-1 mileNon contiguous

Accident locations(mile)

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CEE 763 Fall 2011

PEAK SEARCHING0.1-mile window

0.0 mi 0.1 mi 0.2 mi 0.3 mi 0.4 mi 0.5 mi 0.6 mi 0.67 mi

Win # 1

Win # 2

Win # 3

Win # 4

Win # 5

Win # 7

0.03 mi

0.07 mi

Roadway Segment

Win # 6

Note:Window length = 0.1 mi

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CEE 763 Fall 2011



Win # 1

Win # 2

Win # 3

Win # 4

Win # 5

0.03 mi

Roadway Segment

Win # 6

0.07 mi


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CEE 763 Fall 2011



Win # 1

Win # 2

Win # 3

Win # 4

Roadway Segment


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EXAMPLE

A roadway segment is 0.47 miles long. Using a window length of 0.1 miles, the following crash data were obtained for each sub-segment. Calculate the CV for each sub-segment, and determine whether the search should continue with longer window sizes (assume the limiting CV is 0.25).

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EXAMPLE-continued

Sub-segment Position Excess Expected Crash Frequency

C.V.

B1 0.00-0.20 6.50

B2 0.10-0.30 4.45

B3 0.20-0.40 3.80

B4 0.27-0.47 7.15

topic 2 – network screening cee 763

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