2010 Estimated Data Line Counts Individual Income Tax Returns
TRAFFIC LOADING ESTIMATED FROM COUNTS
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M Slavik & J Bosman
description
TRAFFIC LOADING ESTIMATED FROM COUNTS. M Slavik & J Bosman. Data. Expertise. Information. M1 M2 M3. Collect. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20. Collect New Data. Organise. Make Accessible. Analyse. Apply. Retrieve. Knowledge. 2. - PowerPoint PPT Presentation
Transcript of TRAFFIC LOADING ESTIMATED FROM COUNTS
M Slavik & J Bosman
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ExpertiseExpertise
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DataData
Information
KnowledgeKnowledgeM1 M2 M3M1 M2 M3
70 % freight by road 4 % p.a. growth
Information on traffic loadingneeded for:
• pavement design• road maintenance• law enforcement• statistics, patterns, trends
SOURCES OF TRAFFIC LOADINGINFORMATION:
• Inductive-loop counters
• Weigh-In-Motion (WIM)
• Weighbridges
ASPECTS OF TRAFFIC LOADING:
• MagnitudeAverage Daily Traffic (ADT)Average Daily Truck Traffic (ADTT)
• CompositionLV, HV; HV - short, medium, long,buses, vehicle / axle configuration
• Axle loadsAxle-load distributionESAL (E80)
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Axle load, t
E80
E80 = (Axle load / 8.2)4.2
E80 FROM INDUCTIVE LOOPS
• Bosman 1988:4 classes of road, by % of 2-ax HVTypical (default) axle-load distributions
• Bosman 2004: Simplified to 3 classes
• Slavik & Bosman in 2006:Parameters measurable by loopsHV - Short, Medium, Long, vs E80/HVInfluence of law enforcement3 steps
STEP 1 – RELATION WITH E80/HV
Loop measured attributes:
• % heavy vehicles (HV)• % short HV• % mediun HV• % long HV
STEP 2 – 76 WIM STATIONS, 2005
• Data validated
• Reprocessed
• % Long trucks determined
• E80/HV evaluated
STEP 3 – LAW ENFORCEMENT
INTENSITY :
• Strong – permanent presence
• Medium – ad-hoc, blitzes
• Weak – occasional; non-existent
STEPS 1 + 2 + 3 : GRAPH – FIG.1
E80/HV vs %LT and LE - ALL DATA
y = 0.0243x + 1.3303
R2 = 0.1604
y = 0.0414x + 0.235
R2 = 0.6557
y = 0.0263x + 0.6066
R2 = 0.3106
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
20 25 30 35 40 45 50 55 60 65 70 75 80 85
Percentage of long trucks
E8
0 p
er
he
avy
veh
icle
Strong LE Med LE Weak LE Linear (Weak LE) Linear (Med LE) Linear (Strong LE)
1 KLPnb
2 WNKnb
3 KMTeb
5 HDBsb
4 HDCsb
STEP 4 –TRAFFIC LOADING MODELS
Type LT class Law Enf.
1 Below 35 % Any
2 35 % – 55 % Weak
3 35 % – 55 % Strong
4 Over 55 % Weak
5 Over 55 % Strong
STEP 5 – FIVE MODEL STATIONS
Type LT LE Model
1 Below 35 % Any N12 Kliprivier
2 35 % – 55 % Weak N2 Winkelspruit
3 35 % – 55 % Strong N4 Komati
4 Over 55 % Weak N3 Hidcote
5 Over 55 % Strong N3 Heidelberg
TABLE 2. Traffic and Sample Sizes at the Five Traffic-loading Model Stations
Type Abbrev. ADT/dir ADTT/dir HV HV-ax
1 KLP 32 451 1 618 367 147 1 512 495
2 WNK 11 898 951 317 918 1 444 444
3 KMT 1 619 222 46 932 210 335
4 HDC 7 223 1 995 593 819 3 154 415
5 HDB 5 082 1 052 325 177 1 703 140
TABLE 3. Key Figures of the Five Traffic-loading Types
Model %LT class LE WIM % LT t/ax E80/ax ax/HV E80/HV
1 Below 35 Any KLP 29.8 4.845 0.411 4.12 1.69
2 35 - 55 Weak WNK 42.2 5.080 0.574 4.54 2.61
3 35 - 55 Strong KMT 48.9 5.279 0.415 4.48 1.86
4 Over 55 Weak HDC 60.6 5.984 0.583 5.31 3.10
5 Over 55 Strong HDB 58.4 5.783 0.453 5.24 2.37
DISTRIBUTION OF RAW AXLE LOADSLANE 1
E80/HV = 1.69; Model 1; KLPnb; %LT<35 - Any LE; 1512495 HV axles (8.5ms27/2/06)Averages: ton/AL=4.845; E80/AL=0.411; Axles/HV=4.120; E80/day=1855.2; SD=2.625
Axle load, t20191817161514131211109876543210
Cu
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% o
f al
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axl
es
100.0
90.0
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
Frequency, %
20
18
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10
8
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4
2
0
1.69
8.91
19.78
14.89
10.8911.3
9.4
8.08
6.85
4.61
2.24
0.870.31 0.11 0.04 0.01 0 0 0 0
1 - N12 KLIPRIVIER
DISTRIBUTION OF RAW AXLE LOADSLANE 1
E80/HV = 2.61; Model 2: WNKnb; 35<%LT<55 - Weak LE; 1444444 HV axles (8.6ms27/6/06)Averages: ton/AL=5.080; E80/AL=0.574; Axles/HV=4.543; E80/day=2363.9; SD=2.972
Axle load, t20191817161514131211109876543210
Cu
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% o
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axl
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100.0
90.0
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70.0
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50.0
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0.0
Frequency, %
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8
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4
2
0
1.27
11.46
19.32
13.5
10.63
9.48
6.56
5.66
7.457.05
4.61
2.04
0.70.21 0.06 0 0 0 0 0
2 - N2 WINKELSPRUIT
DISTRIBUTION OF RAW AXLE LOADSLANE 1
E80/HV = 1.86; Model 3; KMTeb; 35<%LT<55 - Strong LE; 210335 HV axles (8.5ms27/2/06)Averages: ton/AL=5.279; E80/AL=0.415; Axles/HV=4.482; E80/day=402.2; SD=2.409
Axle load, t20191817161514131211109876543210
Cu
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% o
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axl
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100.0
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0.0
Frequency, %
1716
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1110
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87
6
54
3
21
0
0.65
5.63
14.49
15.99
11.68 11.4211.03
11.86
10.39
5.24
1.3
0.24 0.06 0.01 0.01 0.00 0 0 0 0
3 - N4 KOMATIPOORT
DISTRIBUTION OF RAW AXLE LOADSLANE 4
E80/HV = 3.10; Model 4; HDCsb; %LT>55 - Weak LE; 3154415 HV axles (8.5ms27/2/06)Averages: ton/AL=5.984; E80/AL=0.583; Axles/HV=5.312; E80/day=5151.2; SD=2.438
Axle load, t20191817161514131211109876543210
Cu
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axl
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90.0
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0.0
Frequency, %
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1.28
2.54
8.69
12.18
9.95
14.23 14.46
12.01
12.95
7.41
3.42
0.720.12 0.03 0.01 0.00 0 0 0 0
4 - N3 HIDCOTE
DISTRIBUTION OF RAW AXLE LOADSLANE 1
E80/HV = 2.37; Model 5; HDBsb; %LT>55 - Strong LE; 1703140 HV axles (8.5ms27/2/06)Averages: ton/AL=5.783; E80/AL=0.453; Axles/HV=5.238; E80/day=2141.4; SD=2.112
Axle load, t20191817161514131211109876543210
Cu
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% o
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axl
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100.0
90.0
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Frequency, %
18
16
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8
6
4
2
00.15
1.58
8.85
13.82
12.59
14.23
17.31
14.67
10.64
4.77
1.12
0.22 0.04 0.01 0.00 0.00 0 0 0 0
5 - N3 HEIDELBERG
BASE-YEAR PRESENT WORTH OF PAVEMENT LIFE COST
Model 4: %LT>55, Weak LEAverage = 437.48 R/m2; St.dev. = 38.60 R/m2
Base-year present worth of life cost C, R/m2600580560540520500480460440420400380360340
Pro
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-th
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%100
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1/m
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1614
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8
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(5.19/9/06)
BASE-YEAR PRESENT WORTH OF PAVEMENT LIFE COST
Model 5: %LT>55, Strong LEAverage = 393.90 R/m2; St.dev. = 28.29 R/m2
Base-year present worth of life cost C, R/m2500480460440420400380360340320
Pro
ba
bili
ty o
f 'm
ore
-th
an
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C',
%100
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Ca
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a R
1/m
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35
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15
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5
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(5.19/9/06)
E80/HV vs %LT and LE - ALL DATA
y = 0.0243x + 1.3303
R2 = 0.1604
y = 0.0414x + 0.235
R2 = 0.6557
y = 0.0263x + 0.6066
R2 = 0.3106
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
20 25 30 35 40 45 50 55 60 65 70 75 80 85
Percentage of long trucks
E8
0 p
er
he
avy
veh
icle
Strong LE Med LE Weak LE Linear (Weak LE) Linear (Med LE) Linear (Strong LE)
1 KLPnb
2 WNKnb
3 KMTeb
5 HDBsb
4 HDCsb
WHY?• Weak relationship between %LT and E80/HV• Imprecision of WIM due to - Calibration problems - Deteriorating pavement - Hardware and software defects• No cheap substitute for good WIM measurements
NEXT?• Strict WIM quality control (European Standard) • Uniform data validation procedures• Uniform tender requirements• Re-appraise situation in 2-3 years time
CONCLUSION
The relationship between the percentage of long trucks and E80/HV is not very good. R-square varies from 0,16 to 0,66.
The trend lines, however, indicate that
• the E80/HV is lower with higher law enforcement, and • the E80/HV is higher with a higher percentage of long trucks.
It is thus recommended that, in the absence of better traffic loading data
• the E80/HV values in Table 3 of the paper, and • the axle distributions in Appendix A of the paper
be used by designers and practitioners in the meantime.
ACKNOWLEDGEMENTS
The authors wish to express their gratitude to:
• NTRV (Northern Toll Road Venture, the N1 Toll Road Concessionaire) • N3TC (N3 Toll Concession, the N3 Toll Road Concessionaire),
• TRAC (Trans African Concessions, the N4 Toll Road Concessionaire), • Bakwena (the N4 Platinum Toll Road Concessionaire), and
• SANRAL (South African National Roads Agency Limited)
for the traffic data and information made available.
M Slavik & J Bosman
