topic 2 – network screening cee 763
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1
CEE 763 Fall 2011
Topic 2 – Network Screening
CEE 763
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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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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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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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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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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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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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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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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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*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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CEE 763 Fall 201132
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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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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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
CRASH FREQUENCY WITH EB ADJUSTMENT
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
CRASH FREQUENCY WITH EB ADJUSTMENT
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
CRASH FREQUENCY WITH EB ADJUSTMENT
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
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
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.
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CEE 763 Fall 2011
SLIDING WINDOW0.3-mi window with 0.1-mi increment
Site No. 11 Site No. 12
MP 21.4 MP 22.2 MP 23.0
Sliding WindowW = 0.3 mi
Site No. 11 Site No. 12
MP 21.4 MP 22.2 MP 23.0
Sliding WindowW = 0.3 mi
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CEE 763 Fall 2011
SLIDING WINDOW0.3-mi window with 0.1-mi increment
Site No. 23 Site No. 24
MP 35.4 MP 36.2 MP 36.7MP 36.37
Site No. 25
0.1 mi 0.17 mi 0.03 mi
Site No. 23 Site No. 24
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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SLIDING WINDOW0.3-mi window with 0.1-mi increment
0.3 mi
Site No. 31 Site No. 32
MP 53.5 MP 54.3 MP 54.48
Site No. 33
0.2 mi 0.1 mi
Site No. 31 Site No. 32
MP 53.5 MP 54.3 MP 54.48
Site No. 33
Site No. 31 Site No. 32
MP 53.5 MP 54.3 MP 54.48
Site No. 33
0.1 mi 0.18 mi
A
B
C
0.3 mi
Site No. 31 Site No. 32
MP 53.5 MP 54.3 MP 54.48
Site No. 33
0.2 mi 0.1 mi
Site No. 31 Site No. 32
MP 53.5 MP 54.3 MP 54.48
Site No. 33
Site No. 31 Site No. 32
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
SLIDING WINDOW0.3-mi window with 0.1-mi increment
Site No. 23 Site No. 24
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
Site No. 22 Site No. 25
MP35.6
Site No. 23 Site No. 24
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
Site No. 22 Site No. 25
MP35.6
Sliding Window Concepts: Example of Position and Location of Sliding Windows and Subsegments
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SLIDING WINDOW0.3-mi window with 0.1-mi increment
Site No. 23 Site No. 24
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
Site No. 22 Site No. 25
MP35.6
74.36)( )( EPDOYXSum
50.37)( )( EPDOYXSum
43.50)( )( EPDOYXSum
96.45)( )( EPDOYXSum
51.34)( )( EPDOYXSum
28.39)( )( EPDOYXSum
25.36)( )( EPDOYXSum
85.46)( )( EPDOYXSum
85.44)( )( EPDOYXSum
28.39)( )( EPDOYXSum
11.33)( )( EPDOYXSum
11.24)( )( EPDOYXSum
51.34)( )( EPDOYXSum
Note: Sum(XY(EPDO)) expressed as acc/mi
Site No. 23 Site No. 24
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
Site No. 22 Site No. 25
MP35.6
Site No. 23 Site No. 24
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
Site No. 22 Site No. 25
MP35.6
74.36)( )( EPDOYXSum
50.37)( )( EPDOYXSum
43.50)( )( EPDOYXSum
96.45)( )( EPDOYXSum
51.34)( )( EPDOYXSum
28.39)( )( EPDOYXSum
25.36)( )( EPDOYXSum
85.46)( )( EPDOYXSum
85.44)( )( EPDOYXSum
28.39)( )( EPDOYXSum
11.33)( )( EPDOYXSum
11.24)( )( EPDOYXSum
51.34)( )( EPDOYXSum
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
PEAK SEARCHING0.2-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
0.03 mi
Roadway Segment
Win # 6
0.07 mi
Note:Window length = 0.2 mi
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CEE 763 Fall 2011
PEAK SEARCHING0.4-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
Roadway Segment
Note:Window length = 0.4 mi
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CEE 763 Fall 2011
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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CEE 763 Fall 2011
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
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