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Transport and Population Da

Commercial Transport SClassified Vehicle Count

October 2003


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THE TRANSPORT AND POPULATION DATA CENTRE

The Transport and Population Data Centre (TPDC) of the NSW Department of Infrastru

and Natural Resources provides data on current and forecast travel patterns for the peopl

Sydney Metropolitan Region as inputs to transport and land use planning and polic

TPDC’s main data sets include:

• Household Travel Survey 1997/98, 1998/99, 1999/2000, 2000/01 and 2001/02

• Commercial Transport Study

• Journey to Work (JTW) data derived from the Australian Bureau of Statistics (APopulation and Housing, for 1981, 1991, 1996 and 2001.

• Land use (population and employment) projections 1996-2026

Transport and Population Data Centre

Level 18

227 Elizabeth St

Sydney NSW 2000.

GPO Box 1620

SYDNEY NSW 2001

Telephone: (02) 9268 2858

Facsimile: (02) 9268 2853

Email: [email protected]

www.planning.nsw.gov.au/tdc

© 2003 Crown Copyright

Subject to copyright. All rights are reserved. Except as permitted under the Copyright Act 1968 by copyright may be reproduced or copied by any process or any means, electronically or otwritten permission of the copyright owner.

Disclaimer

While all care is taken in producing and publishing this work, no responsibility is taken or warrespect to the accuracy of any information, data or representation. The authors (including copyr publishers expressly disclaim all liability in respect of anything done or omitted to be done and tupon reliance of the contents of this publication.

Prepared for the Transport and Population Data Centre byCFE I f ti T h l i (Fi l R t S t b 2003)


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Table of Contents

FOREWORD ..................... ...................... ...................... ...................... ...................... ................... ..........

EXECUTIVE SUMMARY........... ...................... ..................... ...................... ..................... ....................

BACKGROUND ................... ...................... ...................... ...................... ...................... .................... ......

PILOT STUDY #1......................................................................................................................

Pilot Study #1 – Objectives................................................... .............................................

Pilot Study #1 – Method ............................................................ ........................................

Pilot Study #1 – Audit Results ................................................................ ........................... Pilot Study #1 – Analysis ............................................................... ....................................

Pilot Study #1 – Conclusions................... ................................................................ ..........

PILOT STUDY #2......................................................................................................................

Pilot Study #2 - Objectives .............................................................. ..................................

Pilot Study #2 - Method............................................................ .........................................

Pilot Study #2 – Audit Results ................................................................ ...........................

Pilot Study #2 – Analysis ............................................................... ....................................

Pilot Study #2 – Conclusions................... ................................................................ ..........

MAIN PROJECT ....................................................................................................................... Main Project – Method.................................... ................................................................ ..

Main Project – Audit Results........................................... ..................................................

Main Project – Analysis ................................................................ ....................................

Main Project – Conclusions ....................................................... .......................................

APPENDIX 1 : TRAFFIC COUNTING METHODS.................... ...................... ...................... ..........

P NEUMATIC TUBE COUNTING.................................................................................................

Nesting.................................................................... ...........................................................

Tube Counters and Processing Software..........................................................................ISSUES AFFECTING COUNTER ACCURACY..............................................................................

Reflections / Phantom Events / Ghost Events............................. .......................................

Hidden Events............................................................ ........................................................

Missing Events....................................... ................................................................ ............

Congested Traffic / Stopped Vehicles...............................................................................

Transit Lanes. The effect of Motorbikes on Classification. ...............................................

Shielding Devices and Cost Effectiveness .................................................................... .....

APPENDIX 2: GLOSSARY........ ...................... ...................... ...................... ...................... ..................

APPENDIX 3: VEHICLE TYPE CLASSIFICATION................. ...................... ...................... ..........

APPENDIX 4: VEHICLE COUNT LOCATIONS ...................... ....................... ...................... .........


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Foreword

In May 2002, the Transport Data Centre, now called the Transport Population and Data Cen

NSW Department of Infrastructure, Planning and Natural Resources, undertook the Com

Study (CTS) Classified Vehicle Count Study.

The aim of the study was to collect the most accurate counts of heavy vehicles on major an

in the Sydney Greater Metropolitan Region (GMR). This data is an important input to the

vehicle small-area trip table estimation process. In the estimation process, the counts of co

are used during the matrix estimation process, along with other inputs such as prior matrices

The Centre for Excellence Pty Ltd (CFE) was engaged to undertake this study for TPDC

tubes were used to collect the classification counts in the study. Prior to the study, TPD

aware that collecting these classification data for multi-lane roads using pneumatic

inaccuracies, particularly during congested periods. TPDC, therefore, included in the study

validation component to gain an understanding of the level and the reasons for these in

determine ways to address them so that more accurate data could be obtained.

As a result of the video validation, CFE was able to:

(1) enhance their classification software to incorporate new programs that would addreissues noted, and

(2) devise a new tube configuration using a four-tube, four-metre spacing instead of the two-tube, one-metre spacing used in the industry, also to ensure more accurate counts.

The following report, produced by CFE, provides a comprehensive discussion of the methofrom the study, including the results of the validation studies undertaken before and afte

count study.

The actual data obtained from this study consist of hourly, lane by lane, directional, 7-day

the 41 roads counted in the study. The actual counts are not included in this report, but can

TPDC Client Services.


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Executive Summary

The Centre for Excellence Pty Ltd (CFE) has undertaken a project for the Transport and

Centre (TPDC) of the Department of Infrastructure, Planning and Natural Resources, in as

NSW Roads and Traffic Authority (RTA).

The Project, known as the Commercial Transport Study Classified Vehicle Count Study, en

of commercial vehicle traffic data at 44 locations on main roads using pneumatic tube

accurate data in a lane by lane, 7 day, 1 hour, speed and classification format.

During this project, The Centre for Excellence undertook investigation, analysis and

component of the pneumatic count process to enable The Centre for Excellence to improv

and explain inaccuracies in the resultant data. The Centre for Excellence agreed to includ

research in this paper for the benefit of the TDC, RTA and the wider community.

During the study, auditing of videotaped traffic flows indicated situations where exist

configurations were inadequate in providing accurate classification on multi-lane roads in

unidirectional flows under all circumstances. Improvements to the tube configuration we

implemented to increase, in conjunction with changes to the analysis software, the accur

under a wider range of traffic conditions.

During the study, auditing of videotaped traffic flows indicated situations under specific tr

which the then current classification software was deficient in its analysis of the vehic

producing inaccuracies in vehicle classifications. In particular, the issues of Slow and High

hidden events, missed events and motorcycles in transit lanes were investigated and

addressed in the classification software to produce more accurate classifications und

conditions. These issues, their characteristics, general probability and common effect ondiscussed in this report.

The resultant physical tube configuration and classification software produced high

classification results under a wide range of conditions including single lane counts and m

unidirectional and bi-directional situations covering high volume, high and low speed, co

commercial vehicle environments.

The results of various audits and the results of the classification counts undertaken are tabled


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Objectives

The objectives of this report are to:

1) provide the data collected and summarised from the pneumatic tube counts

2) provide the results of videotape audits indicating the accuracy of the pneumatic tube co

3) provide a description of the project as a whole and to detail the key activities undertake

4) recommend whether the use of Classification Adjustment Factors is possible and practi

5) detail the pneumatic tube counting process with the view of enabling the TPDC, RTA a

a) to more fully understand the issues of counter accuracy

b) to better interpret classified data from pneumatic tube counts

6) determine if pneumatic tube traffic counts on a middle lane (that is, a non kerb or m

undertaken with sufficient accuracy using pneumatic tube counters.

Background

At the beginning of this project there was a general belief in the traffic industry that pneu

counts (hereafter referred to as PT Counts) had an acceptable level of accuracy at low antraffic volumes and yet had significant and unacceptable levels of inaccuracies at highe

particularly when congestion occurred.

Historically, these inaccuracies were believed to be primarily dependent on volume, that is,

increased from relatively low volumes outside of the peak periods to relatively higher volum

periods, counter accuracy would reduce. It is more correct to say however, that thes

exacerbated when the flow of traffic is near to, or in, a congested condition. Since congestio

occur within the peak periods when the volume is relatively high, counter inaccuracies were

to be greatest within the peak periods. The importance of this distinction however, is thconditions can occur outside of the peak periods, perhaps due, for example, to traffic signal

volume may be relatively low and congestion-like queuing results from the regulation

signalisation.

The likelihood and frequency of inaccuracies was also believed to be dependent on

configuration or “layout”. Single lane classification counts were thought to be the most acc

multilane bidirectional and multilane unidirectional classification counts.

Where the volume of traffic is such that vehicles travelling over the tubes do not frequently the case of bidirectional), or travel together (in the case of multilane unidirectional), then t

should not be significantly affected. However, it was a common assumption that the volume

vehicles travelling over the tubes frequently pass each other (in the case of bidirectional), or

the case of multilane) would be lower than the point at which congestion occurs and that err

frequent than in single lane counts even in peak periods. In other words, inaccuracies i

multilane tube counts would start at significantly lower volumes than those volumes causi


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1) misclassification of vehicles resulting in the correct volume but otherwise incorrect cl

2) misclassification of vehicles resulting in decreased volume and otherwise incorrect cl3) misclassification of vehicles resulting in increased volume and otherwise incorrect cla

4) undercounting of vehicles resulting in a decreased volume but otherwise correct class

5) overcounting of vehicles resulting in an increased volume but otherwise correct class

Note : “otherwise incorrect classification totals” means that classified totals in classes othe

vehicle in error would also be in error. “otherwise correct classification totals” means that t

in classes other than the class of vehicle in error would not be in error.

Examples of Errors

Type 1) Vehicle misclassification resulting in the correct volume but otherwise incorrect cla

If this error occurred, say when a Class 9 (o…o.o…o.o.o) vehicle is incorrectly classi

(o…o.o…o.o), Total Volume would be correct and otherwise the classification totals would

is, the Class 9 total would be reduced by one and the Class 8 total increased by one.

Type 2) Vehicle misclassification resulting in a decreased volume and otherwise incorrect cl

If this error occurred, say when a Class 4 (o…..o.o) followed by a Class 1 (o...o) is incorreClass 9 vehicle (o…..o.o….o…..o), the Total Volume would be decreased by one a

classification totals would be incorrect. That is, the Class 9 total would be increased by on

and Class 1 totals would each be decreased by one. A type 2) error is the reverse of the type

Type 3) Vehicle misclassification resulting in increased volume and otherwise incorrect clas

If this error occurred, say when a Class 9 vehicle (o…..o.o….o…..o) is incorrectly class

(o…..o.o) followed by a Class 1 (o...o), the Total Volume would be increased by one

classification totals would be incorrect. That is, the Class 9 total would be decreased by o

and Class 1 totals would each be increased by one. A type 3) error is the reverse of the type 2

Type 4) Vehicle undercounting resulting in a decreased volume but otherwise correct classif

If this error occurred, say when a car avoids the tubes, or the Datalogger does not record the

Volume would be decreased by one but otherwise the classification totals would be corre

class total would be decreased by one and all other classes would be correct.

Type 5) Vehicle overcounting resulting in an increased volume but otherwise correct classifi

Type 5 errors are uncommon, although it is possible to overcount vehicles by misclassifyinto smaller vehicles, the generation of additional vehicles from “thin air” is not a frequent o

There are two important points that can be drawn from the above examples, which

comprehensive. Firstly that Types 1), 2) and 3) Errors have the effect of “confusi

classification totals because they affect more than one class of vehicle. Secondly, that becaus

3) E ff t th l f hi l th t (b i M


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1) The sophistication of the classification softwareWhen the classification software attempts to firstly associate events together, then to associ

it must do so using certain limits on such things as speed, interaxle acceleration, interaxle sp

spacing and vehicle lengths. The software procedures used to classify a vehicle and the so

procedures also affect the certainty of correctly associating events and subsequent axles. I

software is incorrect in using these limits or the process of associating events is defici

incorrect classification may result (in say a too fast vehicle of longer than actual length, a to

shorter than actual length, a misclassification of a vehicle at it’s actual speed, or the mi

entirely). Generally, however, over a length of time the software developer, upon investigati

through research and experience, should be able to reduce the number of errors resultishortfalls to a minimum.

2) Hidden Events

In multilane counts, Hidden Events result from the hiding of a vehicle’s axle event in the lan

counter due to the interference by another vehicle that is crossing the tube at the same time

to the counter. Hidden events result in the either the misclassification of the vehicle, usually

missing of the vehicle entirely.

3) High Speed Reflections / Ghost Events

High Speed Reflections are additional events recorded by the counter that are produced by t

air pulse within the tube due to high speed vehicles. High Speed Reflections cause m

vehicles, usually upward, and the rarely result in a missed vehicle is the pattern matching pro

4) Slow Speed Reflections

Slow Speed Reflections are additional events recorded by the counter that are produced by

trapped by the vehicle’s tyre. Slow Speed Reflections cause misclassification of vehicles, usthe rarely result in a missed vehicle is the pattern matching process is sufficient.

5) Motorbikes

Motorbikes can cause misclassification, particularly resulting in Class 7 vehicles, when they

relative speed and within close proximity to the preceding vehicle. Since motorbikes ten

distance between themselves and the preceding vehicle as their speed increases, they theref

the distance between themselves and the preceding vehicle at slower speeds. This mean

misclassification tends to occur only within high volume slow moving traffic with no cong

at constant speed. Since transit lanes, and in particular, bus lanes, contain a high propor

motorbikes, this type of misclassification is more frequent in bus and transit lanes when t

relatively low.

6) Tube Layout and Counter Configuration

The configuration of the tube layout on the road and the setting of the counters “dbounce”


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Quantification of Inaccuracies

Historically, there has been a feeling in the industry that these inaccuracies existed but they

From time to time in an ad-hoc, irregular and inconsistent manner, PT classification counts

be compared with other data, not by CFE but by customers of CFE, using methods such as

1) the comparison with other PT counts done in the recent past2) the comparison with manual counts done concurrently3) the comparison with data from permanent stations4) anecdotal evidence, “gut” feelings, and/or professional knowledge.

Since these situations were infrequent and were for different traffic volumes, traffic chlocation, tube configuration, counter brand and model, the results did not lend in any way

and definitive understanding of the inaccuracy of any particular traffic counter under any

condition.

CFE, as likely did all traffic survey companies, installed the counters in accordance with t

instructions, in the best possible location and with the best work practices gained from exper

the customer of the reasonable estimation of the resultant count accuracy. Given at the tim

and counter classification software constituted a “black box” from which the data was down

traffic survey companies were not in a position to go much further in determining the absocount or the number and types of errors that were present in a count, without additional cost

customer. Since traffic engineers and consultants use representative sampling and are

considerable professional expertise in interpreting, analysing and forecasting the informatio

a characteristic of the industry that consultants who accepted data collected from PT cla

understood that in some situations inaccuracies would be significant and would accomm

professional work. The Centre for Excellence was not aware if any traffic consultants th

supplied by CFE in the past had a methodology of interpreting PT count data for inaccurac

advise in this report of any such methods.

One thing, however, clearly indicates that traffic has exceeded a counter’s ability to rea

traffic stream. This is the appearance of Austroads Classes 11, 12 and 13 vehicles in the pe

reports. The absence of these classes in the non-peak periods was the general basis of the ind

that the counts were acceptably accurate at low volumes and inaccurate during high volume

Prior to the Project and as part of CFE’s tender preparation, CFE undertook a Manual Cou

Road Beverly Hills and compared the results to three concurrent Single Lane PT counts usin

a) Golden River Archer 6400, b) Microcom MetroCount 5600Plus,c) QTC 4 Tube 4 Million Event Counter

The results (not provided in this document) indicated that all three counters performed acc

in classifying vehicles in a free flowing Single Lane at around 250 vehicles per hour non

QTC counter was somewhat more accurate than both the Archer and the MetroCount The


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3) During the development of the QTC 4 Tube 4 Million Event Counter, CFE had prov

advice to QTC, part of which was that the counter should have four air switches, not twQTC 4 Tube 4 Million Event Counter has four air switches while the MetroCount

Golden River Archer 6400 counters have only two air switches. With four air switches

is better able to analyse multilane bidirectional and unidirectional counts using a tec

CFE called Nesting. The majority of the TPDC count sites were two lane roads with n

lane roads, that is, the majority of sites required bidirectional and unidirectional multilan

Horses for Courses

Having stated the reasons behind CFE’s proposal to undertake this project using QTC countmake it clear that certain counters are particularly suited to doing certain types of counts

time being unsuitable for other types of counts. This does not mean in any way that those co

as the commercial reality of providing a counting service means that the value of any p

counter must incorporate issues of cost, durability, longevity, reliability and maintenanc

reality is then that older technologies may come to the forefront of performance in a partic

For example, the Queensland Department of Main Roads cannot speak more highly of th

VDAS counters in their environment. The NSW Roads and Traffic Authority for man

Trafficorders and by far and away the Archer6400 has the highest return on investme

operated by CFE (primarily because the majority of traffic counting is undertaken on munici

Adjustment Factors

In the tender analysis stage of the project, CFE was asked how it was going to address the in

RTA and TPDC were expecting to occur during the peak periods. CFE suggested that a com

count data with manual count data be undertaken on a class by class basis for each fifteen

four hour peak period at a selection of count sites. It was suggested that the adjustmen

derived from the comparison of each class of vehicles in each fifteen minute period at each s

the adjustment factors were applied to the PT count during the time of the manual count, th be adjusted to meet the manual count.

CFE suggested that the adjustment factors may be able to be subsequently used at the sam

peak period on other days on which the manual count was not undertaken and when the

exceed the level at which the inaccuracies begin Following that it was then reasoned th

Inaccuracy

Tube CountVolume

Time


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Pilot Study #1

At the Project kick-off meeting held on the 8th May 2002 it was decided to undertake a Pilo

methodology proposed by CFE. Through consultation between the parties five morning peak

afternoon peak periods were selected. The locations were determined to each repr

characteristic of traffic from which the subsequent adjustment factors may be derived.

It was mutually agreed that four continuous hours would encompass both non peak and

flows. The four-hour periods were determined as 6am to10am for morning peak periods an

afternoon peak periods. It was also mutually agreed that the day on which the manual counwould not be a public holiday or weekend and that the peak period, whether morning or af

the higher of the two peak periods for each location.

One of the six locations was selected by the RTA and CFE was required to undertake a con

lane by lane pneumatic tube event count upstream from the first selected site. No m

undertaken at this count site, the purpose of which was to determine the consistency of th

counting process in a closed system. The location selected by the RTA was Victoria Road

which has four lanes of traffic at both count sites.

Before the commencement of the Pilot study the TPDC required that CFE and the manual co

accomplish a pre-pilot field test to ascertain whether the method of manual counting and com

could be undertaken successfully in the Pilot Study.

On July 19th 2002 The Centre for Excellence submitted the Pilot Study #1 Report, p

reproduced in this document.

Pilot Study #1 – Objectives

1) to test the methodology proposed by CFE in the collection of on-site manual counts and2) to ascertain the availability of CFE’s application software and computer systems to pr

reporting between on-site manual counts, PT counts and the subsequent derivation of ad

3) to test the consistency of the proposed QTC 4 Tube traffic counter in a high volume, clo4) to determine the initial level of inaccuracy of the QTC 4 Tube traffic counter in varied tr

Pilot Study #1 – Method

Early in the development of the QTC counter, CFE proposed the idea of Nesting, as a wa

lanes of traffic concurrently using one traffic counter. By using more than two tubes it wa

third tube could be used to segregate the vehicles into lanes thereby increasing the accurac

count and providing the benefit of determining lane usage, that is, the determination of clas

lane. Although CFE had initially suggested a different configuration to that shown (one w

tubes were much closer together), QTC had developed their classification software to analys


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peak period on one weekday. One TIS staff member visually classified each vehicle in one

and reported the classified vehicles as totals during each fifteen minute period over the fou

responsible to transpose the written results into electronic form and provide the data t

delimited format. To load the TIS spreadsheet into CFE’s existing traffic database, C

interface program and database schema to hold to manual counts.

After the end of each seven-day counting period, CFE removed the counters and download

CFE used QTC’s then current analysis software to classify the vehicles in each traffic lane

the PT count data into CFE’s traffic database in 15-minute format (wherein indivi

summarised into 15-minute periods). CFE then developed a new report format to compare

counts and the 15-minute manual counts by site and lane.

Pilot Study #1 – Audit Results

Table 1. Pilot 1 Audit Results

Wentworth Avenue 20-May-02

Lane 1 - KerbCls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10

Tube Count (T) 308 Nested 1472 16 129 41 4 3 0 10 15 1

Manual Count (M) 2 AW 1540 29 138 44 9 4 1 9 20 0Difference (T - M) -68 -13 -9 -3 -5 -1 -1 1 -5 1

Percentage Difference -4.42 -44.83 -6.52 -6.82 -55.56 -25.00 -100 10.00 -25.00 100

Percentage Error -3.79 -0.72 -0.50 -0.17 -0.28 -0.06 -0.06 0.06 -0.28 0.06

Lane 2 - Middle Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10


Manual Count (M) 1 DW 504 0 41 7 4 0 0 0 1 0

Difference (T - M) -34 0 -5 -1 -4 0 0 1 -1 0

Percentage Difference -6.75 0.00 -12.20 -14.29 -100 0.00 0.00 100 -100 0.00 Percentage Error -6.10 0.00 -0.90 -0.18 -0.72 0.00 0.00 0.18 -0.18 0.00

Great Western Hwy 20-May-02Lane 1 - Kerb Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10


Manual Count (M) 3 LM 3436 39 164 60 11 2 3 9 53 0

Difference (T - M) -1115 51 876 150 -8 43 6 9 -22 3

Percentage Difference -32.45 56.67 84.23 71.43 -72.73 95.56 66.67 50.00 -41.51 100

Percentage Error -29.49 1.35 23.17 3.97 -0.21 1.14 0.16 0.24 -0.58 0.08

Lane 2 - Median Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10


Manual Count (M) 4 PC 3510 21 82 35 3 2 3 2 23 0

Difference (T - M) -846 45 46 -14 0 34 -2 2 -15 0

Percentage Difference -24.10 68.18 35.94 -40.00 0.00 94.44 -66.67 50.00 -65.22 0.00


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Lane 3 – Median Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10

Tube Count (T) 16 Single 4286 46 70 17 1 3 13 1 16 2Manual Count (M) 7 DW 5269 26 48 2 0 0 0 0 2 0

Difference (T - M) -983 20 22 15 1 3 13 1 14 2

Percentage Difference -18.66 43.48 31.43 88.24 100 100 100 100 87.50 100

Percentage Error -18.38 0.37 0.41 0.28 0.02 0.06 0.24 0.02 0.26 0.04

Newbridge Road 5-Jun-02Lane 1 - Kerb Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10


Manual Count (M) 8 AW 3553 22 172 34 4 0 2 1 20 2Difference (T - M) -292 2 -51 -5 -1 6 -2 0 -2 -2

Percentage Difference -8.22 8.33 -29.65 -14.71 -25.00 100 -100 0.00 -10.00 -100

Percentage Error -7.66 0.05 -1.34 -0.13 -0.03 0.16 -0.05 0.00 -0.05 -0.05

Lane 2 – Middle Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10


Manual Count (M) 9 DW 3752 24 241 65 16 2 6 8 107 6

Difference (T - M) 5 128 215 52 -8 70 8 12 -50 -3

Percentage Difference 0.13 84.21 47.15 44.44 -50.00 97.22 57.14 60.00 -46.73 -50.00Percentage Error 0.11 2.75 4.62 1.12 -0.17 1.50 0.17 0.26 -1.07 -0.06

Lane 3 - Median Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10

Tube Count (T) 12 Single 3445 16 155 33 1 4 0 7 36 2

Manual Count (M) 10 PC 3338 11 96 31 1 0 7 6 35 0

Difference (T - M) 107 5 59 2 0 4 -7 1 1 2

Percentage Difference 3.11 31.25 38.06 6.06 0.00 100 -100 14.29 2.78 100

Percentage Error 2.89 0.14 1.60 0.05 0.00 0.11 -0.19 0.03 0.03 0.05

Pennant Hills Road 13-Jun-02Lane 1 - Kerb Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10


Manual Count (M) 11 PC 2210 23 70 28 2 0 3 1 13 2

Difference (T - M) -226 -3 37 -20 -2 2 -3 -1 -13 -2

Percentage Difference -10.23 -13.04 34.58 -71.43 -100 100 -100 -100 -100 -100

Percentage Error -9.61 -0.13 1.57 -0.85 -0.09 0.09 -0.13 -0.04 -0.55 -0.09

Lane 2 - Middle Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10


Manual Count (M) 12 DW 2723 46 174 42 18 18 5 16 283 56

Difference (T - M) -194 84 91 78 -8 28 13 24 -83 -23

Percentage Difference -7.12 64.62 34.34 65.00 -44.44 60.87 72.22 60.00 -29.33 -41.07

Percentage Error -5.72 2.48 2.68 2.30 -0.24 0.83 0.38 0.71 -2.45 -0.68

L 3 M di Cl 1 Cl 2 Cl 3 Cl 4 Cl 5 Cl 6 Cl 7 Cl 8 Cl 9 Cl 10


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Manual Count (M) 22 DW 3509 16 147 15 7 4 0 4 22 0

Difference (T - M) -61 59 219 76 -6 6 0 13 4 1

Percentage Difference -1.74 78.67 59.84 83.52 0.00 0.00 0.00 0.00 0.00 0.00

Percentage Error -1.51 1.46 5.43 1.88 -0.15 0.15 0.00 0.32 0.10 0.02



Manual Count (M) 23 LM 3574 7 13 3 0 0 2 0 1 1

Difference (T - M) -418 60 339 70 0 12 -2 8 13 -1


Percentage Error -11.35 1.63 9.21 1.90 0.00 0.33 -0.05 0.22 0.35 -0.03

6-Jun-02Lane 1 – Kerb Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10


Manual Count (M) 18 AW 1661 4 141 13 0 0 0 0 0 0

Difference (T - M) -79 -2 -18 -8 0 0 0 0 0 0

Percentage Difference -4.76 -50.00 -12.77 -61.54 0.00 0.00 0.00 0.00 0.00 0.00Percentage Error -4.34 -0.11 -0.99 -0.44 0.00 0.00 0.00 0.00 0.00 0.00



Manual Count (M) 19 LM 3812 22 90 31 14 0 5 5 14 0

Difference (T - M) -179 48 136 20 -13 11 -5 0 0 1


Percentage Error -4.46 1.20 3.39 0.50 -0.32 0.27 -0.12 0.00 0.00 0.02

Lane 3 – Middle Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10Tube Count (T) 9 Nested 3228 65 202 38 0 11 0 6 2 0

Manual Count (M) 16 PC 3551 12 45 8 4 1 2 1 0 0

Difference (T - M) -323 53 157 30 -4 10 -2 5 2 0



7-Jun-02Lane 1 – Kerb Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10


Manual Count (M) 14 DW 1244 10 142 13 0 0 0 0 0 0

Difference (T - M) 74 -9 -16 -10 0 0 0 0 0 0

Percentage Difference 5.61 -90.00 -11.27 -76.92 0.00 0.00 0.00 0.00 0.00 0.00

Percentage Error 5.11 -0.62 -1.10 -0.69 0.00 0.00 0.00 0.00 0.00 0.00


T b C t (T) 5 N t d 3867 80 244 37 2 10 0 5 12 0


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Difference (T - M) -148 -3 15 -3 0 0 0 0 -1 0

Percentage Difference -5.28 -42.86 29.41 -30.00 0.00 0.00 0.00 0.00 0.00 0.00

Percentage Error -5.18 -0.11 0.53 -0.11 0.00 0.00 0.00 0.00 -0.04 0.00

11-Jun-02Lane 1 – Kerb* Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10


Manual Count (M) 24 AW 1491 13 158 21 0 0 0 0 1 0

Difference (T - M) -206 -13 -34 -20 0 0 0 0 0 0

Percentage Difference -13.82 -100 -21.52 -95.24 0.00 0.00 0.00 0.00 0.00 0.00

Percentage Error -12.23 -0.77 -2.02 -1.19 0.00 0.00 0.00 0.00 0.00 0.00



Manual Count (M) 25 DW 3833 13 164 15 11 0 2 4 13 0

Difference (T - M) -93 62 58 24 -8 9 -2 4 -7 2


Percentage Error -2.27 1.51 1.41 0.58 -0.19 0.22 -0.05 0.10 -0.17 0.05



Manual Count (M) 26 LM 3738 14 47 0 2 1 4 0 4 0

Difference (T - M) -458 46 127 35 -2 9 -4 0 2 0

Percentage Difference -12.25 76.67 72.99 100 0.00 0.00 0.00 0.00 0.00 0.00


Lane 4 – Median Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10


Manual Count (M) 27 LM 2986 10 41 8 1 0 0 0 2 0

Difference (T - M) -143 -3 23 -4 -1 0 0 0 -1 0

Percentage Difference -4.79 -30.00 35.94 -50.00 0.00 0.00 0.00 0.00 0.00 0.00

Percentage Error -4.69 -0.10 0.75 -0.13 -0.03 0.00 0.00 0.00 -0.03 0.00

12-Jun-02Lane 1 – Kerb* Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10


Manual Count (M) 28 DW 1998 4 172 10 0 0 0 0 0 0

Difference (T - M) -423 -2 -22 -8 0 0 0 0 0 0

Percentage Difference -21.17 -50.00 -12.79 -80.00 0.00 0.00 0.00 0.00 0.00 0.00

Percentage Error -19.37 -0.09 -1.01 -0.37 0.00 0.00 0.00 0.00 0.00 0.00



Manual Count (M) 29 AW 3628 11 155 21 10 0 3 6 12 0

Difference (T - M) -337 63 228 56 -9 9 -3 13 8 0



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In Table 1, for each of the six locations the table shows the total of the four hours counted i

AUSTROADS classification scheme. Next to the label “Tube Count (T)” is the tube c

example 308, and the tube method, either Nested or Single. Next to the label “Manual

manual count number and the initials of the counter. The line labeled “Difference (T-M)” sh

between the tube and manual counts for each class (Tube minus Manual). The “Perce

displays the Difference as a percentage of the maximum of the tube and the manual count

Error” displays the Difference as a percentage of the maximum of the tube and manual co

The Percentage Error represents the amount by which the Tube Total would be multiplied

added to the Tube Amount to reach the Manual Amount (for each class).

* Note : The results of the pneumatic tube count number 328 undertaken on Lane 1 Ker

Gladesville on June 11th and June 12 th 2002, presented in this document are different to th

in the Pilot Study 1 report. A human error in the loading of the traffic counts resulted in

being loaded against the Kerb lane whereas the correct count for this lane was count 328.

1) Result Ranges

The results generally display an undercounting of traffic in the Nested Kerbside lane

overcounting of traffic in some of the Nested non-kerbside lanes. The differences in the vo

kerbside and non-kerbside lane do not indicate an incorrect assignment of the vehicles betw

that the individual lane totals are incorrect but the combined total is correct. The differencesthe three tube layout used, that the kerbside lane was less accurate than the non-kerb la

manual count. This was not the situation that was expected to occur because internal test

CFE suggested that the kerbside lane should be more accurate than the non-kerbside lane. T

commonsensical because the vehicles in the kerbside lane do not receive interference from v

kerbside lane.

2) Lane Splits

One of the significant results of the Pilot Study #1 is the ability of the Nested configuration

accurately, segregate the traffic into multiple lanes. This was the first demonstration of thability to use a third tube to determine the lane that the vehicle is travelling in, with

classification of multilane roads with a single counter was not thought to be acceptably accur

3) Closed System

The two concurrent count results, totaling eight lanes of traffic, on Victoria Road sho

correlation. Over a seven day period, two “4 tube” counters collected traffic over 4 lanes a

of a segment of Victoria Road while at the downstream end, two additional “4 tube” counte

over the same 4 lanes. At the upstream and downstream sites, each of the four counters

kerbside or median lane in a Nested configuration while at the same time collecting a non-k

lane (in a Nested configuration). Each counter reported approximately 290000 vehicles ov

a 1.66% variation in flow between the two locations. This is significant because the two

section of Victoria Road have different traffic characteristics, one end is more free flowin

congested (in the peak) and yet the difference in volumes was within an acceptable level.


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relationship between the inaccuracies at these two sites. However, CFE determined that to

recommend the broader use of any derived adjustment factors, CFE would have to more fu

operation of the post-processing software that was to be used in the Pilot Study and subseq

That is, to be able to use the derived adjustment factors more broadly, the causes of th

understood so that the appropriateness of one set of adjustment factors, derived at one site,

site can be determined.

On request, QTC provided the source code of the post-processing QTC classification softw

source code was then read to gain an understanding of the way in which the post-proces

producing the results indicated in the pre-Pilot counts.

Once the source code was studied and comprehended, it required more rigorous resear

determine the basic causes of the inaccuracies in the tube counts. To aid in this testing, a co

was installed adjacent to CFE’s offices, northbound on King Georges Road Beverly Hill

layout consisted of 8 tubes over various of the three northbound lanes, using three QTC c

nested and single lane configurations. It should be noted that King Georges Road is a satisf

this type of research as it has a high proportion of commercial vehicles, which are extrem

pattern, and has high volume traffic flows (400+ vph/lane in non peak periods) which are

CFE’s offices by traffic signals. The traffic was then recorded over several hours and on dif

digital video camera.

CFE then post-processed the resultant event files using the then current QTC classification

report listing individual vehicles and showing class, speed, acceleration, axle pattern and axl

was audited vehicle by vehicle to second accuracy. Whenever a misclassification occurred,

in the report did not match the vehicle on the video in terms of class, lane number, time to

length and speed, the QTC post-processing software was analysed to determine why th

occurred. This analysis was undertaken for every discrepancy and for each type of inaccurac

the post-processing classification software was identified.

Once the source code limitations were determined it was a logical next step to determine en

post-processing software to overcome the limitations. It must be stressed that the limitation

processing code related to multilane processing and were not overly significant in single lan

Being itself an experienced software development company and QTC being otherwise fully

projects, CFE enhanced the QTC post-processing software in an iterative process until it wa

classification for a two and a half hour period on King Georges Road over a single lane and

the same period using a Nested configuration in both kerbside and non-kerbside lanes. O

were individually audited and analysed in two and one half hours of video. This is not meansame level of accuracy was guaranteed when the modified software was used in other traffic

included only to inform the reader of work undertaken by CFE in this project.

CFE then reprocessed the original event files (that had been downloaded from the counte

Pilot 1 sites) with the enhanced classification software. The effect of the enhancem

discrepancies between the Pilot 1 tube counts and the on-site manual counts were significan


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the validity of the manual count as it can be very difficult to estimate axle spaces on unco

vehicles moving at medium to high speed. Secondly, the required level of concentration o

four-hour period, to enable the correct classification of each vehicle may be subject to v

people as the task is onerous and physically and mentally taxing. Thirdly, it is a character

counting industry that the majority of counting staff are part time employees, may be tempo

employees and are predominantly either university students or retirees.

Finally, people require relief breaks such as toilet breaks and meal breaks. Startling even

(such as car backfires, horns, screeching tyres and traffic accidents). During these periods v

counted and human counters are able to, and may, compensate for vehicles that are kno

missed, or even thought to have been missed. There are also times when non-kerb lanes are

view of the manual counter due to the presence of tall vehicles in the kerb lane.

Although the manual counting companies are reputable and honest, they are working wi

restrictions of the industry, and hence it is reasonable to suggest that variations in skills, m

capacity and experience between available persons from job to job, may result in variation

accuracy. Some common mis-classifications that a visual counter may make are:

i) white short wheel based (under 3.2 metres) trucks, commonly used by landscaping conurseries are classed incorrectly as Class 3 when they are in fact Class 1

ii) long wheel based utilities and station wagons (with axle lengths > 3.2 metres) are clvehicles when in fact they are Class 3 vehicles

iii) Class 9 or 8 vehicles may have one rear wheel raised above the pavement, the counteor 7 respectively, while the person may not identify that the wheel is off the grou

incorrectly. This is even a problem when using video and the vehicle is moving at re

the lighting is bad

iv) if the first axle spacing of a class 5 is over 2.1 meters it is a class 7 vehicle. It is person to measure the axle spacing while the truck is moving hence 5 and 7’s m

counted

v) if the first axle spacing of a three-axle vehicle is over 3.2 meters it is a class 6 vehiclefor a person to measure the axle spacing while the vehicle is moving hence 2

incorrectly counted

vi) in some situations it may be difficult for a person to identify the number of axles available time before the next vehicle arrives, especially where the vehicle is non s

very fast or obscured by another vehicle

vii) Buses and other commercial vehicles in the kerb lane may obscure vehicles travellingmedian lanes resulting in a guess by the manual counter.

viii) Booking and transcription errors, either on-site booking errors or errors in collating a

resultant data, may occur.

When dealing with high volumes of traffic and small time periods (15 minutes), some

between the on-site manual count and the tube count in each period may be due to the diffe

manual count time, that is, people’s watches, and the pneumatic counter’s internal clock. Th

at the beginning or end of the manual count period when the volume is increasing or decre

i d h diff b h i d h h i i h d


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At a meeting between the parties on 19 th July 2002 it was decided to undertake a second Pi

effectiveness of video auditing and to determine the effect of video auditing on the level

tube count discrepancy.

Since the mini-DV tape format had at that time a maximum capacity of 120 minutes it was

undertaken in the second Pilot should be two hours in duration rather than four hours. This c

as a cost rationalisation as the cost of video auditing is higher that of on-site auditing. A

auditing one hour before and one hour after the two central peak hours, in terms of providi

error situations, was far less than the cost of undertaking the extended audit. In other words,

before and one hour after the two central peak hours doubles the cost of the video audit, ye

inaccuracies occur at the highest volume, it may not provide any new or additional discrepan

those already occurring in the highest volume periods.

Tube Configuration

During the analysis of the Pilot 1, pre-pilot and internal King Georges Road testing, CF

changing the tube configuration may have a significant impact on the level of accuracy

counts (as indicated in the Pilot 1 results). A modified tube configuration was proposed by

meeting and it was agreed that Pilot 2 be undertaken with the modified tube configuration.


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Pilot Study #2

The following objectives were determined at a meeting between the parties to discuss Pilot

meeting the ambiguity of discrepancies between the manual counts and the tube counts r

discrepancy cause determination. It was suggested by CFE at this meeting that the on-site m

vehicle classification) counts undertaken in the Pilot Study were partially adequate for deter

the discrepancy between manual and tube counts though were totally inadequate for determ

each discrepancy. As a result of the research work undertaken by CFE during Pilot Study

was suggested that video and off-site audit be tested as an alternative to the method of on-sit

undertaken in Pilot Study #1.

Pilot Study #2 - Objectives

The objectives of the second Pilot Study were to:

1. Investigate the benefits of the use of video for the purposes of

i. Manual counting accuracy

ii. Tube discrepancy cause determinationiii. Other benefits

2. Investigate the use of a different tube configuration and determine new counting techaccuracy

3. Determine and describe the causes of the inaccuracies in the tube counts.

Pilot Study #2 - Method

Four of the six Pilot Study #1 locations were selected for Pilot Study #2 and pneumatic tuinstalled at these sites to again collect seven day classification data in each lane. For compar

tube counters in Pilot Study #2 were installed in the same position to those in Pilot Study #1

exception of Victoria Road (wherein the counter was installed on the Gladesville Bridge in

was situated on the Iron Cove Bridge in Pilot Study #2).

As a result of the research and testing undertaken by CFE prior to Pilot Study #2 a signi

made in this pilot study to the tube layout for a nested count. In the first pilot study, the tub

the A and B tubes and B and C tubes were 2 and 1 metres respectively (i.e. the distance A to

In this pilot study the spaces were modified to 4 and 0.01 metres respectively (i.e. the disrelatively the same as the distance A to B).

The increase in spacing between the A and B tubes from 2 metres to 4 metres was contrary

tube counting convention in congested urban traffic and was intended so that a car could

Pilot Study 1 Tube Layout Pilot Study 2 Tub


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The decrease in the spacing between B and C from 1 metre to “as close as possible” was m

accuracy in the determination of each vehicle axle’s tail event in the kerbside lane. In theCFE had used QTC’s recommendation for tube spacing. When CFE originally proposed Ne

QTC, CFE had proposed the Pilot Study #2 configuration and QTC, when implem

recommended and then programmed their software for the Pilot Study #1 configuration. Wh

first pilot study was analysed by CFE it was found that the 1 metre spacing between the B

an ambiguity when trying to match an event on the C tube with the correct event on the B

slow moving traffic. This was because the C tube event was sufficiently far (in time) from

that multiple A and B event combinations could match the position of the C event, the base i

B tube event was consistent with the A and C events. In addition, with a 1m spacing

accelerate or decelerate significantly over the space between B and C making correlation bet

C events much more difficult, therefore potentially leading to inaccuracies as with a redu

inaccuracies become more probable. Placing the B and C tubes together enables the associa

B and C events with a higher degree of certainty. This change in tube layout required a su

the analysis software because the method of matching C events to B events was now very di

Another more generic advantage in using a 4 meter A-B spacing is related to the calculation

Since the axle speed is calculated as the spacing of A and B (distance traveled) divided by th

times between the A and B events (time), then any error in the installed distance or the t

effects the speed calculation. For example, if the tubes are not parallel or are not placed exa

distance then the speed may be increased or decreased depending on whether B is closer to

Likewise, if one tube is shorter “behind the kerb” than the other, the difference in time betw

events will be greater or less depending on whether A is shorter or longer than B behin

spacing is 1 meter, then the effect of the error is (Ed/Et)/1; where Ed is the error in the dist

error in the time measurement. If the errors remain the same and the tube spacing is increas

error is the (Ed/Et)/2, that is, half as significant, and if increased to 4 metres the error is qu

as it was at 1 metre.

CFE sub-contracted the videotaping of the sites to Traffic Impact Surveys Pty Ltd. Each s

for 2 hours producing one tape covering all lanes in one direction at that location. After th

complete and the tube counts were downloaded and post-processed by the modified Classif

spreadsheet was produced which listed each vehicle by lane and by time (to the second

details of the vehicle’s classification, speed, acceleration and axle configuration. The time

was then synchronised with the time on the videotape to enable a vehicle by vehicle audit of

lane recorded on the videotape.

In the first pilot study the manual count data reported only the total number of vehicles ofeach lane in each 15-minute period. In comparison, the method of off-site auditing of the

the discrepancies between each vehicle classified by the software and the actual vehicle sho

be determined. When each discrepancy was found, it was checked and then the event file

subject to the amount of discrepancy and the type of discrepancy, the analysing software wa

audit spreadsheet was reproduced. That part of the audit in error was then re-audited to v

corrections made to the software


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Pilot Study #2 – Audit Results

Table 2. Pilot 2 Audit Results.

Great Western HwyLane 1 - Driver Side Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls

Tube Count (Nested) 1584 8 83 7 4 2 4 1 2Manual Count 1624 7 65 11 5 2 2 3 7Difference (Tube – Manual) -40 1 18 -4 -1 0 2 -2 -5

Percentage Difference -2.46 12.50 21.69 -36.36 -20.00 0.00 50.00 -66.67 -71.43

Percentage Error -2.32 0.06 1.04 -0.23 -0.06 0.00 0.12 -0.12 -0.29

Lane 2 – Passenger Side Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls

Tube Count (Nested) 1179 12 146 35 6 1 3 6 22Manual Count 1369 10 97 20 6 5 1 3 32Difference (Tube – Manual) -190 2 49 15 0 -4 2 3 -10

Percentage Difference -13.88 16.67 33.56 42.86 0.00 -80.00 66.67 50.00 -31.25

Percentage Error -12.29 0.13 3.17 0.97 0.00 -0.26 0.13 0.19 -0.65

Pennant Hills RoadLane 1 – Kerb Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls

Tube Count (Single) 1072 9 51 4 1 0 1 0 8Manual Count 1082 9 54 4 1 0 1 0 8Difference (Tube – Manual) -10 0 -3 0 0 0 0 0 0

Percentage Difference -0.92 0.00 -5.56 0.00 0.00 0.00 0.00 0.00 0.00

Percentage Error -0.86 0.00 -0.26 0.00 0.00 0.00 0.00 0.00 0.00

Lane 2 – Middle Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls

Tube Count (Nested) 873 13 75 27 1 1 10 15 63Manual Count 902 21 54 16 1 3 2 13 86Difference (Tube – Manual) -29 -8 21 11 0 -2 8 2 -23

Percentage Difference -3.22 -38.10 28.00 40.74 0.00 -66.67 80.00 13.33 -26.74 Percentage Error -2.61 -0.72 1.89 0.99 0.00 -0.18 0.72 0.18 -2.07

Lane 3 – Median Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls

Tube Count (Nested (C)) 1107 11 31 2 0 1 1 2 9Manual Count 1080 11 29 4 0 2 0 2 9Difference (Tube – Manual) 27 0 2 -2 0 -1 1 0 0

Percentage Difference 2.44 0.00 6.45 -50.00 0.00 -50.00 100 0.00 0.00

Percentage Error 2.32 0.00 0.17 -0.17 0.00 -0.09 0.09 0.00 0.00

Newbridge RoadLane 1 – Kerb Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls

Tube Count (Single) 1449 12 44 12 3 1 1 1 4Manual Count 1450 10 49 7 1 3 1 2 5Difference (Tube – Manual) -1 2 -5 5 2 -2 0 -1 -1

Percentage Difference -0.07 16.67 -10.20 41.67 66.67 -66.67 0.00 -50.00 -20.00

Percentage Error -0.07 0.13 -0.33 0.33 0.13 -0.13 0.00 -0.07 -0.07


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Overall Result Improvements

At three of the four Pilot Study #2 sites, being Great Western Highway, Pennant Hills RoaRoad, the results in this study showed significant improvement over those at the same sites

Of most notable improvement was the non-kerbside (Nested) lane of Great Western Highwa

The one exception to the improved results is Victoria Road in which the tube count show

accuracy. It should be noted however, that in this case alone, the location of the site on Vict

between Pilot Study #1 and Pilot Study #2. The location of the site on Pilot Study 1 was G

while in Pilot 2 the location of the site was Iron Cove Bridge. Iron Cove Bridge can be ch

the peak period as intensely congested whereas Gladesville Bridge, while it does become co

peak it is far less so than Iron Cove Bridge. In this particular situation, the reduction in acc

pilot studies was as a result of the increased volume and amount of congestion in the Pilot S

Had the first Pilot site been Iron Cove Bridge and the second been Gladesville Bridge then

in accuracy between the pilot studies would have resulted. In other words because the two lo

traffic character, the relative accuracy of the two pilots is unimportant, however, the low

accuracy on the Iron Cove Bridge in Pilot 2 is significant. This result indicates that the issue

and go traffic had not been fully resolved and implemented at that time, and so the s

configuration were more sensitive to the character of the traffic on the Iron Cove Bridge.

Pilot Study #2 – Analysis

Factors Affecting Tube Count Accuracy.

Traffic Density and Speed Consistency. The Probability of Hidden Events

During the analysis of the discrepancies in the Pilot Study #2, CFE researched the issue o

multilane counts, a detailed explanation of which is contained in the Appendix.

Of the locations counted in this study, only one site displayed the specific traffic cha

maximises the likelihood of hidden events, which was Great Western Highway. The videot

vehicles proceed down the road in waves towards the count site, tightly packed and with con

other sites contained traffic with much higher variations in speed and were of less density

likelihood of hidden events was reduced at these sites. This is indicated by the audit results s

Western Highway had some of the higher discrepancies in both Pilot 1 and 2 and yet was no

the peak periods. Great Western Highway also showed the greatest improvement in accur

Study #1 and Pilot Study #2, primarily because the Pilot Study #1 tube configuration and

employed by the QTC software (at that time) were particularly susceptible to the condition o

In hindsight, the videotaping process exacerbated a natural condition at Great Western High

level of discrepancy therefore being due to poor site location, particularly in respect of th

process of videotaping. A more accurate result would have been gained from positioning


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In this phase of the project CFE researched the issue of High Speed Reflections in tube co

explanation of which is contained in the Appendix, and modified the software to overcom

high speed reflections.

Traffic Congestion and Queue Formation. Slow Vehicles and Stopped Vehicles

A factor affecting the accuracy of the tube count is that of congestion and in particular th

formation and discharge. In particular, errors occur more often where a queue covering the t

forming and slow discharging.

Victoria Road on the Iron Cove Bridge has a particularly frequent queue with very slow rate

dispersal. This means that vehicles entering the queue and vehicles discharging from the quethe tubes) are travelling at low velocities, and due to the queue cycle time, this occurs often.

Even though one lane of Newbridge Road displays congestion, the queue length formation a

time is short enough not to have a significant impact upon the tube count. The Newb

operates in a “flow – form – discharge – gap – flow – form – discharge” cycle. The “gap –

of the cycle means that there are relatively less stopped vehicles per unit time in this queue t

Road queue which is “form – discharge – form –discharge”. In addition, the presence of

component in the Newbridge Road queue means that the vehicles forming and discharging

so at higher speeds than vehicles in the slow forming and slow discharging queue on Victori

Note : Prior to this Project a general conception in the industry was that tube counters unde

flows (by an unknown amount) and it was not known whether this undercounting

misclassification of groups of car axles to a larger vehicle, whether the counter produ

(unclassed vehicles) for every stopped vehicle, or whether the counter just missed the even

slow axles. It appears from our analysis however, that although undercounting is occurring

as many thought, including the author, since undercounting is related to congestion and dur

actual flow is reduced anyway as a consequence of the congestion. Reduced volumes durin

are therefore not simply undercounting by the tube counter, though undercounting is a reduced flow.

The effects of stopped and very slow vehicles on correct vehicle classification have been r

and are detailed in the Appendix.

Transit Lanes. The effect of Motorbikes on Classification.

Another factor that was identified during the second pilot and indicated by the Victoria

Bridge videotape, is that of the effect of Transit Lanes, in particular Bus Lanes, and motorbiof incorrectly classified vehicles. This is a result of the high proportion of motorbikes in t

the particular density of traffic (brought about by the speed of the vehicles). In this situati

travel in higher densities (that is, more of them travelling in groups of two or more) and th

other vehicles than would otherwise be the case (in higher speed and freer flowing traffic).

Because of the slow moving nature of the Victoria Road bus lane motorbikes tend to tr


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Pilot Study #2 – Conclusions

Benefits of video auditing on Manual Counting and discrepancy cause determin

Video was used to produce the same data format as the original pilot study method, that is, t

vehicles of each class within each lane for each 15 minute period, though to a higher level

the video can be stopped and replayed thereby reducing manual count errors). This is clearly

the videotape for manual counting purposes. More importantly however, is the benefit deriv

the audit spreadsheet in both increasing the accuracy of the manual count and indicating

discrepancies. The reason why the offsite manual count becomes more accurate than onsievery vehicle must be accounted for, and additional vehicles are recorded on the audit

identified. Because individual vehicles are matched against the video, any discrepancy is

individual vehicle basis.

The use of the audit spreadsheet and the video requires increased resources, particularly in

Typically, an offsite audit takes double the number of lane hours taken in an on-site audit pl

though these hours may be serial rather than parallel hours. That is, for three lanes of on-s

people are mandatory, i.e. parallel counting. For three lanes of off-site auditing, one person

lanes ie. in series.

The first Pilot Study #2 conclusion is that the use of video has provided increased benefits to

1) increasing the accuracy of the manual count and removing entirely all manual count unc2) providing a fixed basis for vehicle by vehicle analysis of discrepancies (now and in the 3) providing a visual representation of the character of the traffic flow that can be related t4) providing a reproducible video recording that can be used for other purposes (such as th

commercial vehicle audit undertaken by TPDC)

However, due to the increased resources required to audit a traffic video, careful considerat

to the applicability of the off-site video audit to all traffic locations. During the winter month

more than two hours during the peak period was not practical due to lighting conditions aft

6 am. However, two hours at each location provided enough information to enable the corr

discrepancy at each location. The decision of the parties to reduce the number of audited h

two was completely satisfactory from an analysis viewpoint.

Increased Correlation between Manual and Tube Counts

The second Pilot Study #2 conclusion is that, with reference to the first pilot study, the co

manual count and tube count is higher. The use of video and the ability to audit the count

accuracy in the manual count and changes to the tube spacing and the analysis software dur

also increased the accuracy of the tube count.

To what extent each of these increases is responsible for the higher level of correlat


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Main Project

In the main project at 43 main road locations, 212 lanes of traffic were collected using p

nested configurations. The summary results of each individual lane count undertaken in

together with 83 combined count results displaying the resulting data in each direction at ea

with the Pilot Study summary results are included in this report as Appendix 2. During th

hour speed and classification reports, in comma delimited and PDF format, were delivered to

Main Project – Method

As was the case in Pilot Study 1 and 2, CFE sub-contracted the videotaping of the sites

Surveys Pty Ltd. Each site was videotaped for 2 hours producing one tape covering all lanes

In the Main project, a significant change was made to the tube layout for a nested count

study, the tube spaces between the A and B tubes and B and C tubes were 2 and 1 m

(meaning that the distance A to C was 3 metres). In pilot study 2 the spaces were modifi

metres respectively (meaning that the distance A to C was relatively the same as the dista

recommendation was made after research undertaken by CFE in the first pilot study. In the m

tube was used next to the A tube in the same way that the C tube was used next to the Breading “A-B”, “C-D” the tubes were theoretically renumbered as shown below.

The 4 metre space between A and B was retained although it did produce undesirable re

traffic on Victoria Road at Iron Cove Bridge in Pilot Study 2. It was CFE’s belief that the

spacing in congested traffic could be programmatically overcome and in this event the 4-me

then be beneficial in congested traffic.

The increase in spacing between the A and B tubes from 2m to 4m was intended so that a over the tubes with one axle to the left of B and the other to the right of A. The situation

“straddled” over the tubes results in two axles with normal but low velocities and an indete

time stationary over the tubes. By prohibiting the majority of vehicles straddling the tube

removed and is replaced with either a front or back axle with a velocity in the normal range,

with a very low velocity. The low velocity relative to the normal velocity can be used to est

vehicle was stationary over the tubes

Pilot Study 1 Tube Layout

1m 2m

C B A

Pilot Study 2 Tube Layout

4m

CB A

Main Proje

4m

DB


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Table 3. Main Project Audit Results.

Military Road Cremorne Eastbound Audit DurLane 1 – Kerb Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10 C

Tube Count (Nested) 96 1 79 0 0 7 0 0 0 0

Video Count 96 1 78 0 0 7 0 0 0 0

Tube Error 0 0 +1 0 0 0 0 0 0 0

Percentage Difference 0.00 0.00 +1.27 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Percentage Error 0.00 0.00 +0.55 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lane 2 – Middle Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10 C


Video Count 1605 6 92 7 2 11 1 0 6 0

Tube Error +1 0 +2 +1 0 0 0 0 0 0

Percentage Difference +0.06 0.00 +2.12 +12.5 0.00 0.00 0.00 0.00 0.00 0.00

Percentage Error +0.05 0.00 +0.10 +0.05 0.00 0.00 0.00 0.00 0.00 0.00

Lane 3 – Median Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10 C

Tube Count (Single) 1808 4 36 3 1 1 0 0 0 0

Video Count 1810 4 35 3 1 1 0 0 0 0

Tube Error -2 0 +1 0 0 0 0 0 0 0

Percentage Difference -0.11 0.00 +2.78 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Percentage Error -0.10 0.00 +0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Mowbray Rd Chatswood, Westbound Audit Durat

Lane 1 – Kerb Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10 C


Video Count 574 1 31 0 3 0 0 0 0 0

Tube Error -1 0 +3 0 0 0 0 0 0 0Percentage Difference -0.16 0.00 +0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Percentage Error -0.16 0.00 +0.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lane 2 – Middle Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10 C


Video Count 738 1 25 11 2 0 1 0 1 0

Tube Error -15 0 12 -1 0 0 0 0 0 0

Percentage Difference -2.07 0.00 32.43 -9.09 0.00 0.00 0.00 0.00 0.00 0.00

Percentage Error -1.93 0.00 +1.54 -0.13 0.00 0.00 0.00 0.00 0.00 0.00

Princess Hwy Sylvania, Southbound Audit Dur

Lane 1 – Kerb Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10 C


Manual Count 2466 19 78 15 1 1 1 3 4 0


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Tube Error +2 0 -1 0 0 0 0 0 0 0

Percentage Difference +0.23 0.00 -0.71 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Percentage Error 0.14 0.00 -0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Cowpasture Rd Hoxton Park Audit Dur

Lane 1 –Nthbound Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10 C


Manual Count 1817 20 117 53 4 1 6 9 72 12

Tube Error -3 0 +5 +2 +1 0 -2 -1 -1 0

Percentage Difference -0.17 0.00 4.10 3.64 20.00 0.00 -33.33 -11.11 -1.39 0.00

Percentage Error -0.14 0.00 0.24 0.09 0.05 0.00 -0.09 -0.05 -0.05 0.00

Lane 2 – Sthbound Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10 C


Manual Count 1172 6 148 51 8 2 9 9 68 5

Tube Error -47 -3 10 8 -4 0 2 -4 -20 -2

Percentage Difference -4.01 -50.00 6.33 13.56 -50.00 0.00 18.18 -44.44 -29.41 -40.00

Percentage Error -3.18 -0.20 0.68 0.54 -0.27 0.00 0.14 -0.27 -1.35 -0.14

Two-Lanes NB/SBCombined

Cls1 Cls2 Cls3 Cls4 Cls5 Cls6 Cls7 Cls8 Cls9 Cls10 C

Tube Count 2939 23 280 114 9 3 15 13 119 15

Manual Count 2989 26 265 104 12 3 15 18 140 17

Tube Error -50 -3 15 10 -3 0 0 -5 -21 -2

Percentage Difference -1.67 -11.54 5.36 8.77 -25.00 0.00 0.00 -27.78 -15.00 -11.76

Percentage Error -1.39 -0.08 0.42 0.28 -0.08 0.00 0.00 -0.14 -0.59 -0.06

The Military Road data shows that high accuracy was attained for a three-lane road using

multilane four tube nested configuration and one counter in a single lane two tube configfour-metre spacing. In this situation, the multilane nesting correctly interpreted vehicles in a

and a low volume transit lane.

The Mowbray Road audit results indicates the counter accuracy in a congested, stop and go

traffic environment. The audit exhibits several misclassifications between class 1 and

particularly in the non-kerb lane. This is the result of an incorrect estimation of the amount o

stopped over the tubes. As it is not possible to directly measure the amount of time the veh

classification software currently estimates the time and removes it from the axle spacing

underestimation results in the migration of a class 1 vehicle to class 3 vehicle. Although M

higher volume of traffic during the peak period, the number of queues formed over the tub

Mowbray Road and were of shorter duration hence the count result did not suffer from lon

the same way. An additional factor in Military Road is that the kerbside lane is a transit lane

a significantly reduced peak period volume, thereby reducing the interference of kerbside

middle lane vehicles.


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The Cowpasture Road audit is a significantly accurate classification result for a bidirectio

using a single counter. Cowpasture Road has 1700+ vehicles per hour bidirectional with a

commercial vehicles and traffic congestion. Most counter manufacturers recommend thatcounter should not be used in such a traffic environment.

From the results it is clear that the Southbound lane has lower accuracy than the Northboun

of this inaccuracy is that there is a high proportion of crossing vehicles that produce hidden

kerbside southbound lane. It should be noted however that a significant number of vehicles

to 50%, passed each other over the tubes, yet the amount of error due to hidden events was v

number. When the hidden events occur on both tubes on the front or the rear axle of a two

are easy to correct however, when they occur on any axle of a commercial vehicle, the f

found before it is realised that the axle is missing, therefore causing a misclassification.

In the above audit result on the southbound lane of Cowpasture road this is indicated by

number of Class 9’s with a corresponding increase in the amount of Class 3, 4 and 7 vehicle

of this issue is more frequent in bidirectional counts than is the case in unidirectional

primarily because the rear bi-axle and tri-axle groups on the articulated vehicles have m

interfere with the axles of another articulated vehicle which is travelling in the opposite di

case if they are travelling in the same direction (in a multilane count). In both bidirectional

counts, the frequency of this occurrence is also proportional to volume. CFE expects in the f

develop more sophisticated tests for hidden axles in commercial vehicles to overcome this bidirectional counts.

In general the results of the audits indicate an increased level of accuracy in the main proje

accuracy was the result of the four tube configuration and the improved classification softw

counts the accuracy is consistently over 99% with the absolute error being in the range of on

In multilane unidirectional counts, the accuracy of the kerbside lane is the same as a single

accuracy of the non-kerbside lane is also generally well over 95% and in the main is over 9

stop and go congestion and hidden events effect the accuracy of multilane non-kerbside c

accuracies in the order of 95%.

It therefore follows that the amount of congestion and density of the traffic are factors affec

counting in multilane roads and may be one of the primary factors in assessing the accuracy

count result. In multilane bidirectional counts, the lane nearest the counter has the same ac

lane count and the accuracy of the lane furthest from the counter has an accuracy genera

95%. Higher accuracies in the furthest lane are possible in lower traffic conditions and traff

lower proportions of commercial vehicles. The accuracy of the furthest lane from the co

bidirectional counts is related to the volume and the proportion of commercial vehicles.

Furthermore, it should be remembered that these audits

represent the accuracy of the pneumatic tube counting

process within the peak period of each location. An

analysis of the Princes Highway data shows that in the

kerbside lane 38 9% of weekday traffic travels within the

Morning truck traffic – C


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Main Project – Analysis

Congested and slow moving traffic

During several of the pilot audits, CFE identified situations in highly queued traffic that aros

vehicle stopped after the first axle passed the lead tube and the final axle did not pass the ta

vehicle straddled both of the tubes (O__ o _ o __O). At the time, the tube spacing was o

frequency of this occurrence was high. It occurred to CFE that a change in tube spacing

four metres would change the pattern of recorded events and enable the stopped time of

estimated .

CFE was able to overcome the misclassification of vehicles stopping over the tubes in bo

multilane configurations by developing new software routines. The design and developme

procedures to properly handle the 4 metre spacing for stopped vehicles was long and arduo

the software need to be time sensitive (that is, the software needs to know when the peak per

and speed sensitive as well so that the routines to handle stopped vehicles are activated at

Without these three things correlated, stopped vehicle routines cause significant errors in

with vehicles that are spaced closely together and yet have not stopped. The routines deve

the ability to loop between past and future events to check on the preceding and following vspaces, an ability that post-processing software is capable of doing and real-time processin

(they may or may not be sophisticated enough to go back in time but they certainly cannot go

Upon modification of the software to accommodate a four-metre tube spacing and stopped tr

achieved greater accuracy in single lane and multilane highly queued traffic. The modified s

of correctly associating axles of a vehicle that has stopped over the tubes for 5 minutes or le

times are possible but impact on the performance of the software.

AUSTROADS Axles and Groups -> Pattern Matching -> Single Tube Classificat

When CFE audited the Pilot 1 and Pilot 2 traffic counts it became evident that some of the

occurred due to the very nature of the AUSTROAD 1994 Vehicle Classification Scheme, wh

1) The number of Axles and2) The number of Axle Groups (when consecutive axles are spaced less than 2.1 metre

After the axles and groups have been counted, a table of decisions then leads to the cl

vehicle. For example, 5 Axles and 3 Groups is a Class 8, 6 Axles and 3 Groups is a Class 9,

Groups is a Class 10.

Simple reliance on these prescriptions can cause misclassification of vehicles which are clos

not necessarily slow moving. Taking this classification one step further by placing st

generic, rules on the resulting axle pattern leads to a limited increase in the accuracy of

From an initial 12 or so Austroads prescriptions, this technique may lead to several “more st


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using the painted lines as guides. This technique built up a file containing measured a

software was then firstly modified to utilise this file and secondly was modified to add newl

this file, flagged for subsequent videotape verification. Once a new pattern had been videotape the pattern became part of an ever increasing set of axle patterns. This metho

higher degree of certainty in classifying all long vehicles and in particular classes 4, 5, 6 and

Extremely rare vehicles, such as low loaders used in the train transportation industry, will lik

a pattern in this file, simply because it is unlikely that one of these vehicles is captured in a v

required for visual verification of the new pattern). They are unlikely to be captured on

firstly they are statistically rare and secondly they would likely be prohibited on urban roads

or in the least would avoid these periods due to the congestion and the resulting transpor

consequence of rare vehicles not being included in this file is inconsequential because of theresulting in an error of say one in ten thousand vehicles per day. Depending on their actual

may still be classified using the Austroads rules, but likely would not be.

Subsequent to the use of the pattern file for commercial vehicle pattern matching usin

researched the use of the pattern file to classify vehicles using one single tube over one

suggest that the technique is over 80% accurate in classifying free flowing non peak/non

over one lane. Of course, this method is a last resort (perhaps when a tube fails on a rural r

low variations in speed to determine classes 1,2,3, and 6, though low variation in speed i

when classifying longer vehicles (7 and above).

The sophistication of the use of the pattern file is much more than the use that is described h

point is that the pattern file can be used to accept matched patterns or reject matched patte

the measurement of sub-classes of Austroads Classes.

The significant advantage of post-processing software in regards to the pattern file is that

files can be re-analysed using an updated pattern file. The pattern file is then being u

classification control mechanism. If a pattern is subsequently rejected by the system,

software has to deal with the remaining axle pattern. In contrast, when CFE recently rangwhose counters were being utilised on a Sydney metropolitan main road and class 11 an

reported, the vendor suggested that the system be configured to not report the 11s and 12s

the effect of hiding that misclassified vehicle from view and reducing the recorded volum

vehicles.

Missing Events.

During the video audit of slow moving traffic on Epping Road and Botany Road and the a

vehicles it was discovered that the counter was not recording the events from some very slowlight commercial vehicles. In other situations, vehicles changed lanes after crossing one o

missing the other tube. The resulting event pattern is the same as if the counter simply misse

During this phase of the project CFE then began an analysis of Missing Events, which

Appendix, and developed sophisticated routines in the classification software to programm

hi i i h j i f h i i


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Comments on Software Accuracy

When asked by the TPDC to explain why CFE was confident that the traffic classification

near its optimum accuracy the following explanation was given.

From CFE’s now comprehensive technical knowledge regarding traffic classification tech

improved the accuracy of the classification software significantly over this project, C

classification of

Documents

r2003 05 Cts Classified Vehicle Counts