developing a gating management strategy for salterhebble hill, halifax

8/22/2019 Developing a Gating Management Strategy for Salterhebble Hill, Halifax

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School of Civil EngineeringFaculty of Engineering

CIVE5708M

Individual Research Project Dissertation

Submitted in partial fulfilment of the requirements for the degree of

MEng in Civil and Structural Engineering


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ACKNOWLEDGEMENTS

I would like to thank my supervisor, Dr J E Tate, for giving me his undivided attention throughout theyear to discuss the project, as well as for providing me with the resources to conduct the vehicle

survey and model the gating strategy.


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ABSTRACT

Although the relocation of a queue can disperse emissions from one area to another, this studyinvestigated whether a queue relocation gating strategy could be used to reduce the total quantity

of emissions released into the atmosphere, by relocating the traffic queue from an inclined section

to an area that produced less strain upon the vehicle. Queue relocation strategies are used typically

to reduce traffic congestion, however in recent years with the rising concern regarding air quality;

they have become more commonly used to reduce vehicle emissions in urban areas. As road

transport is accountable for approximately 20% of all air pollution emissions produced in the EU, it is

essential to employ traffic management schemes to reduce the production and release of exhaust

emissions into the atmosphere (EUROPA 2007).

The collection of primary data was used to update the signal control plan settings and vehicle fleet

composition to calibrate and validate the existing traffic model. A gating strategy, devised in Aimsun

microsimulation software was coupled with the instantaneous emission model, PHEM, to map the

emissions produced at different sections throughout the network. Furthermore, the proportion

contributed by different vehicles to the total emissions produced in the traffic model were

compared for the base model, the original gating strategy developed by the Institute for Transport

Studies and the new gating strategy developed in this study The emissions for each of these models


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TABLE OF CONTENTS

Acknowledgements iiAbstract iii

List of Figures vii

List of Tables xii

List of Abbreviations xiii

CHAPTER 1: INTRODUCTION

1.1. Background 11.2. Hypothesis 2

1.3. Aims and Objectives 2

CHAPTER 2: LITERATURE REVIEW

2.1. Introduction 4

2.2. Calderdale Council - Air Quality Management 4

2.3. Traffic Congestion Theory 62.4. Traffic Gating 8

2.5. Gating Management Strategies in Practice 8

2.5.1. Kingston-upon-Thames Gating Scheme 8

2.5.2. Southampton Gating Scheme 9

2.5.3. Nottingham Traffic Collar Scheme 1975-76 10

2.5.4. Hampton Court Palace Flower Show SCOOT Gating 11

2.5.5. Leicester Gating Scheme 11

2 5 6 2006 C lth G T ffi N t k O ti S h 13


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2.13. Calibration and Validation 26

CHAPTER 3: DATA COLLECTION AND AIMSUN MODEL SET-UP

3.1. Data Collection 28

3.1.1. Vehicle Tracking Survey 28

3.1.2. Traffic Counting 31

3.1.3. Saturation Flow Rate 31

3.2. Model Set-up 33

3.2.1. Aimsun Microscopic Traffic Simulator 33

3.2.2. Model Development 333.2.3. Calibration 34

3.2.4. Validation 35

CHAPTER 4: DEVELOPMENT OF A NEW GATING STRATEGY

4.1. Introduction 39

4.2. Overview of the Gating Management Strategy 40

4.3. Design of the Gating Management Strategy 404.3.1. Coordination of Exley Bank Signals 40

4.3.2. Coordination of Dryclough Lane Signals 41

4.4. Overall Signal Control Plan 42

4.5. Trial Simulation 45

CHAPTER 5: PHEM EMISSION MODELLING

5 1 I t d ti 46


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Appendix B: Risk Assessment 76

Appendix C: Time-Series Profiles for Petrol and Diesel EURO 0 5 Passenger Cars 80


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LIST OF FIGURES


Figure 2.1 Map of modelled Concentrations of NO2 (gm-2

) 5

Figure 2.2 Diagram of the Kingston-upon-Thames gating scheme (Department for

Transport 2000 p.3)

9

Figure 2.3 Diagram of the Southampton gating scheme (Department for Transport 2000

p.4)

10

Figure 2.4 Map of the Hampton Court Palace Flower Show Gating Scheme (Thomas,

Baffour and Brown 2008 p.117)

11

Figure 2.5 Diagram of Leicester Gating Scheme (Tate and Bell 2000 p.2) 12

Figure 2.6 Map of Cupar town centre 13

Figure 2.7 Snapshot from SIAS Paramics displaying the emissions on Bonnygate Road

adjacent to Crossgate Junction (Neil and Sykes 2008 p.6)

14

Figure 2.8 Snapshot from SIAS Paramics displaying the emissions relocated to West of

Lady Wynd Road (Neil and Sykes 2008 p.6)

14

Figure 2.9 The four-stage transport model (Ortuzar and Willumsen, 2001) 15

Figure 2.10 Diagram of the set-up for CVS modelling (Ajtay, Weilenmann and Solic 2005) 23Figure 2.11 Flow chart displaying the integration of traffic, emission and air quality models

(Tate 2005)

25


Figure 3.1 Satellite images of vehicle turning areas (GOOGLE EARTH 2011) 29

Figure 3.2 Map of the position data for the tracked survey, Google Earth elevation profile

& t llit i f th it (GOOGLE EARTH 2011)

30


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Figure 5.1 Petrol EURO 5 passenger car time-series profile (observed model): (a) Speed,

(b) Fuel Consumption, (c) NOx and (d) Particulate Matter

48

Figure 5.2 Petrol EURO 5 passenger car time-series profile (base model): (a) Speed, (b)

Fuel Consumption, (c) NOx and (d) Particulate Matter

49

Figure 5.3 Bar chart displaying the FC for each section of the road network for the north

bound route

51

Figure 5.4 Bar chart displaying the FC for each section of the road network for the south

bound route

51

Figure 5.5 Bar chart displaying the NOx emissions for each section of the road network for

the north bound route

52

Figure 5.6 Bar chart displaying the NOx emissions for each section of the road network for

the south bound route

52

Figure 5.7 Bar chart displaying the PM emissions for each section of the road network for

the north bound route

53

Figure 5.8 Bar chart displaying the PM emissions for each section of the road network for

the south bound route

53

Figure 5.9 Map of Aimsun road network displaying the location of each section referred

to in figures 5.3 5.8.

54

Figure 5.10 Chart displaying the proportion of each vehicle contribution to FC in the base

model

59

Figure 5.11 Chart displaying the proportion of each vehicle contribution to FC in the OGS 59

Figure 5.12 Chart displaying the proportion of each vehicle contribution to FC in the NGS 59

Figure 5.13 Chart displaying the proportion of each vehicle contribution to NOx emissions

i th b d l

60


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5) passenger cars in the OGS

Figure 5.27 Chart displaying the proportion of PM contributed by petrol and diesel (EURO 0

5) passenger cars in the NGS

65

APPENDICES

Figure A.1 VBox II Lite GPS & CAN Logger Basic User Guide 75

Figure B.1 Risk Assessment 77

Figure C.1 Trial 1 petrol EURO 0 passenger car time-series profile: (a) Speed, (b) Fuel

Consumption, (c) NOx and (d) Particulate Matter

81



82

Figure C.3 Trial 3 Petrol EURO 0 passenger car time-series profile: (a) Speed, (b) Fuel


83



84



85



86



87



88


C ti ( ) NO d (d) P ti l t M tt

89


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103



104



105



106



107



108



108



110

Figure C.31 Trial 1 diesel EURO 0 passenger car time-series profile: (a) Speed, (b) Fuel


111



112



113



114


C ti ( ) NO d (d) P ti l t M tt

115


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129



130



131



132



133



134



135



136



137



138



139


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LIST OF TABLES


Table 2.1 CO Emissions (g/hr) for the Leicester Gating Scheme (Tate and Bell 2000 p.4) 12

Table 2.2 Comparison of microsimulation modelling software 21


Table 3.1 Observed and modelled vehicle proportions and the GEH statistic for north

bound traffic

36

Table 3.2 Observed and modelled vehicle proportions and the GEH statistic for north

bound traffic

37

Table 3.3 Observed and modelled saturation flow rate for north bound traffic 37

Table 3.4 Observed and modelled saturation flow rate for south bound traffic 38


Table 4.1 A comparison of the average vehicle travel times in the three Aimsun models 45


Table 5.1 Comparison of the vehicle proportions from the observed and modelled data,

and the base network.

46

Table 5.2 Total FC, NOx and PM emissions in the OGS, NGS and base model for road

section 251 335

55


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LIST OF ABBREVIATIONS

AIMSUN Advanced Interactive Microscopic Simulator for Urban and Non- urban NetworksAQMA Air Quality Management Area

AQAP Air Quality Action Plan

CAN Control Area Network

CHEM Comprehensive Modal Emissions Model

CMS Changeable Message Sign

CO Carbon Monoxide

CO2 Carbon Dioxide

CVS Constant volume sampler

DEFRA Department for Environment, Food and Rural AffairsDfT Department for Transport

DRACULA Dynamic Route Assignment Combining User Learning and microsimulAtion

EPCC Edinburgh Parallel Computing Centre

FC Fuel Consumption

GEH Geoffrey E. Havers

GPS Global Positioning SystemHC Hydrocarbons

HGV Heavy Goods Vehicle

ITS Institute for Transport Studies

LTP Local Transport Plan

LWR Lighthill Whitham Richards

MODEM Modelling Emissions and Consumption in Urban Areas

NMHC Non-methane hydrocarbons

NO Ni O id


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CHAPTER 1: INTRODUCTION

1.1. Background

The implementation of a traffic queue relocation strategy can contribute to the reduction of vehicle

emissions in an area where there is substantial poor air quality. In response to the 1995

Environmental Act, the National Air Quality Strategy was introduced to tackle issues concerning the

negative impact that vehicle emissions can have upon human health and the environment (Air

Quality UK no date). A lack of air quality can potentially lead to respiratory illnesses as a result of

alterations in the lung; whilst the formation of acid rain or eutrophication can come about due to an

excess concentration of nitrogen dioxide in the atmosphere. Part IV of the Environmental Act 1995

places a statutory duty on the municipal council to monitor the levels of air quality against a set of

eight objectives for different atmospheric pollutants (GREAT BRITAIN 2000; GREAT BRITAIN 2002).

After the economic downturn in 2008, 2013 has seen the British economy finally begin to recover

from the economic recession. One of the key aspects for restoring the nation to economic fruition is

a fully functional transportation network that meets travel demand. In 2012 the UK government

identified the need to rapidly improve its infrastructure, following Prime Minister David Camerons

speech at the Institute of Civil Engineers on key aspects of the UK transportation network in need of


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1.2. Hypothesis

Traffic congestion in a particular area releases high concentrations of vehicle emissions into the

atmosphere. The atmospheric pollutants reduce the air quality which consequently has harmful

effects on living organisms and the surrounding environment. Traffic gating is a method which is

used to prevent congestion by enabling a limited quantity of vehicles through a signalised advanced

stop line over a short period of time. Theoretically, the technique will reduce vehicle emissions from

an area where there is high concentration by relocating the traffic queue from an inclined elevation

to a neutral gradient. Conventionally traffic queue relocation has the effect of relocating vehicle

emissions to another road section without any particular effect on the overall reduction of

pollutants released from vehicles into the atmosphere. Therefore we can expect that by relocating

the traffic queue from an inclined elevation where there is greater engine strain and power output

demand upon the vehicle to an area of neutral gradient where this is less, will reduce exhaust

emissions. Gating has been used, though rarely, for the purposes of relocating atmospheric

emissions, however to date it has not been investigated whether 'gating' can be used to relocate and

reduce overall vehicle emissions, which is what this dissertation seeks to answer.

1.3. Aims and Objectives


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Objective 2: Collect data of the study site in order to calibrate the traffic model.

Instrumentation will be used to log vehicle trips through Salterhebble Hill and collect data of the

vehicle speed and acceleration. In order to update the traffic model, the cycle time, offset, and

phase timings will be measured at the three main signalised junctions, as well as categorising the

composition of the vehicle fleet and measuring the saturation flow. The travel time will be measured

during the off-peak period for use in developing a signals timing plan later in the project.

Objective 3: To model an updated traffic simulation of vehicle movements through Salterhebble

Hill during the morning peak period and produce an emissions map of the study site.

The collated data will be used to calibrate and validate the traffic simulation model. The software for

simulating the traffic model will be used in conjunction with an instantaneous emissions model to

schematically display a map of vehicular emissions along the A629 corridor at Salterhebble Hill.

Objective 4: To develop and simulate a gating strategy using traffic modelling and

instantaneous emissions software.

Collated data of the current signals timing plan and off-peak travel time will be used to design a

ffi i Thi ill b hi d h h d i i di d i l i i l h


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2.1.Introduction

Poor air quality negatively impacts our health and quality of life. The most vulnerable members of

our society, infants, the elderly and those already suffering from health conditions, experience the

most adverse effects from air pollution. Collectively, poor air quality can also indirectly influence the

state of the local economy, causing a loss of working days, reduced productivity and a drain on

national health resources. Impacts of air pollution upon vegetation are also significant to the

ecosystem in the UK. Depositions of high nutrient nitrogen concentrations lead to the growth of

algae causing eutrophication, consequently affecting wildlife and causing a loss of biodiversity.

2.2.Calderdale Council - Air Quality Management

The monitoring of nitrogen dioxide levels from passive diffusion tubes scattered throughout the

A629 corridor have concluded the area at Salterhebble Hill and Huddersfield Road to be in

exceedance of 40gm-3

. In October 2005 the area was designated as an Air Quality Management

Area (AQMA) as defined in the Air Quality (England) Regulations 2000. Section 83 of the

Environmental Act 1995 requires the local authority, Calderdale council, to review and assess the air


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The annual mean objective of less than 40gm-3

of NO2 had been expected to be met by 2010;

however this is yet to be achieved in areas of high vehicular emission such as Salterhebble Hill. The

Department for Environment, Food and Rural Affairs (DEFRA) recommends that in areas where road

transport and traffic emissions are significantly high, local authorities co-ordinate their AQAPs with

h l l ( ) ld d l il h l d i d i i

Figure 2.1 Map of modelled Concentrations of NO2 (gm-2

)

and the extent of the Calderdale AQMA (Calderdale Council

2006 p.5)


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of Calderdale council. The following section will detail from a research point of view - theory and

best practice regarding the management of traffic congestion.

2.3.Traffic Congestion Theory

Traffic congestion can vary depending on the situation and road surroundings. Roberg-Orenstein

(1997) suggests two types of traffic congestion: recurrent and incident-induced congestion.

Recurrent congestion can be defined as the everyday queue that builds up during certain periods

of the day, whereas incident-induced congestion occurs, for example as a result of a traffic accident

which further decreases the orderly movement of traffic. The interaction of incident-induced

congestion and recurrent congestion results in what is commonly known as a jam. Traffic jams

can be best understood by what Huddart and Wright (1989) define as the queuing mechanism. A

queuing mechanism due to a bottleneck occurs when the capacity of a road cannot meet thedemands of arriving traffic. The vehicle service is the vehicle travel time through a bottleneck. High

demand can hinder vehicle service and cause a shock wave to form. The shock wave effect can

be observed when a fleet of vehicles travelling closely together successively reduce their speed with

increased intensity from vehicle to vehicle as a result of the leading vehicle initially braking. This

occurs until individual vehicles in the fleet become stationary and then gradually initiate a slow crawl


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employees loading a truck. Therefore what is considered to be the cause of bottlenecking is

associated more so with disruptions to the traffic flow rather than the road layout (Shepherd 1992).

Queue control measures are a more effective and less costly technique for the management of

overloaded conditions. By restraining traffic entering an already congested route, the arrival rate is

regulated which improves the road capacity and eliminates a bottleneck by relocating the traffic

queue. This is achieved through planning and programming traffic signal settings. Conventionally

traffic signal arrangements are linked with minimising delay at the approach of vehicles to a stop-

line. However during periods of high demand the cycle time increases at signalised intersections,

thus having the effect of lengthening the traffic queue. Smith (1988) had shown that maximising

capacity by allocating additional 'green time' to a heavily saturated route was of greater beneficence

than minimising delay. Restraining traffic can achieve this by implementing a pre-planned traffic

signal strategy to the traffic network. This enables the creation of more road space whichcontributes to the overall road safety, environmental quality and economic growth. The principle

advantage of programming traffic signal control settings is that they can be used flexibly and are

significantly less expensive than other methods of traffic congestion relief. By pre-planning, control

settings signals can be adjusted to the traffic demands during the peak period.


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2.4.Traffic Gating

A congestion offset can move a queue to a different arm of a junction whereas gating can relocate a

queue to a completely different node. Gating is designed to relocate queues away from sensitive

areas of the network to more acceptable locations. The sensitivity of an area may come as a result of

poor environmental conditions, a lack of road space or due to an overloading of traffic. Gating can

be implemented through action at a distance which involves adjusting traffic signal settings that

are at a distance from the problem area. In the UK Split Cycle Offset Optimisation Technique is a tool

that is used by Transport Engineers to programme gating strategies into traffic networks

(Department for Transport 2000).

The implementation of gating is most advantageous for preventing the rapid spread of congestion to

adjoining areas of the network. This can be achieved by identifying the critical link (also known as

the bottleneck link) and adjusting the green time settings on gated links when the critical link

becomes oversaturated. The gated links serve to store queues to prevent congestion building up on

the critical link. During the peak period, when congestion is imminent, the green time at gated links

is reduced (Shepherd 1992). Gating can be used to prevent grid-lock at a roundabout, particularly if

there is a restriction on a major exit. The use of SCOOT technology to prevent grid-lock in the


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As shown in figure 2.2, gating the junctions at the approach to the roundabout enables the traffic

queue at the trigger link to clear which prevents the roundabout from 'locking-up'. During the hours

of the peak period when congestion builds, inductive loop traffic detectors detect the level of

saturation and if it exceeds the assigned critical saturation flow rate, the gated links receive less

green time, whilst the critical link remains in free flow. The reduced flow rate around the

Figure 2.2 Diagram of the Kingston-upon-Thames

Gating Scheme (Department for Transport 2000 p.3)


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lane. Due to the quantity of exhaust emissions released from buses being held in a queue, the

installation of a bus priority system reduced atmospheric pollution.

The narrow road lanes on Northam Bridge limited the vehicle road capacity which in turn resulted in

the formation of traffic queues. Therefore to increase the traffic flow, additional green time was to

be allocated to traffic on Bursledon Road, whilst having the side roads gated. This had consequently

reduced the level of traffic at the approach to Northam Bridge (Department for Transport 2000).

The control and flexibility through use of gating produced results that improved the efficiency of the

Figure 2.3 Diagram of the Southampton Gating

Scheme (Department for Transport 2000 p.4)


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The Hampton Court Palace Flower Show is an annual event that is organised each year by the Royal

Horticultural Society. In order to effectively manage the event traffic a gating strategy is

implemented during the weeklong event to prevent traffic oversaturation. The A244 which passes

through the centre of Esher becomes severely congested due to the long traffic queue which

extends onto the A3 mainline, where congestion becomes particularly problematic.

Figure 2.4 Map of the Hampton Court

Palace Flower Show Gating Scheme

(Thomas, Baffour and Brown 2008 p.117)


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A simplified diagram of the control area is displayed in figure 2.5. Prior to the implementation of the

gating strategy there was poor platooning from London Road to Regent Road which resulted in

severe congestion on this route. The gating strategy was implemented by co-ordinating the traffic

signals along three routes; from London road to the city centre (along Regent Road); from Regent

Road to head outbound onto London/Evington Road; and traffic from city centre bound traffic from

Evington Road. To achieve the goal of the scheme, the main purpose of the gating scheme was to

platoon the traffic along Regent Road. This was achieved by holding inbound traffic on London Road.

The queues were created by increasing the green time on the second stage, at the expense of the

first and third and thus the traffic queue was relocated to an area adjacent to an open park where

there is a reduced population and roadside activity.


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routes around activity centres as well as modifications to existing traffic signal phasing were

implemented to the traffic network. Critical routes were designed as protected corridors by gating

traffic from side roads which increased the capacity of protected routes by up to 20% as compared

with regular traffic flow (Austroads 2010).

2.5.7. Cupar Queue Relocation SchemeIn 2007 SIAS PARAMICS was used to modify the transportation network at the town centre of Cupar.

The microsimulation modelling software was used primarily to alter traffic signal control settings

with the objective to reduce the high levels of atmospheric pollutants on and around Bonnygate

Road. A solution was presented which involved relocating pedestrian crossings, removing signal

junctions, as well as adding signals at un-signalised junctions in order to relocate the traffic queue.

As Bonnygate Road is located in an area with very narrow side streets and tall buildings, the strategyfocused on relocating the traffic queue to an area where emission could be dispersed.

At present vehicles queuing at the Crossgate junction spillback onto Bonnygate Road causing severe

congestion and high idling emissions. By relocating the traffic queue from the Crossgate junction to

Lady Wynd Road, for outbound traffic, the main corridor does not become oversaturated. This was


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The two snapshots taken from SIAS PARAMICS display the reduction in emissions that were achieved

as a result of predominantly gating traffic on Lady Wynd Road. The average reduction in exhaust

emissions over the morning and evening peaks for CO 27.4%, HC's 26.0%, NO 16.8% and PM10

22.5% (Neil and Sykes 2008).

Figure 2.7 Snapshot from SIAS

Paramics displaying the emissions on

Bonnygate Road adjacent to Crossgate

Junction (Neil and Sykes 2008 p.6)

Figure 2.8 Snapshot from SIAS

Paramics displaying the emissions

relocated to West of Lady Wynd

Road (Neil and Sykes 2008 p.6)


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2.6.Modelling Software

2.6.1. IntroductionWith increased vehicle ownership in the UK transportation routes are increasingly becoming more

congested due to the lack of road space. Although investing in new infrastructure seems to resolve

the problem; the associated cost and time required to build new routes or expand existing roads is

not a feasible solution and therefore technological solutions that can meet and deliver client and

user demands are a preferable choice. Accurately modelling vehicle interactions and driver

behaviour on transportation networks can be used by transportation planners and engineers to

maximise the network capacity through use of suitable traffic management strategies. Additionally

transport models can be used to evaluate existing services and schemes that are in place to improve

infrastructure and meet the demands of growing transport use in the UK.

Most transport models are based on the four stage classical model as displayed in figure 2.9. Input

data is initially collected and then processed through a series of stages to determine the frequency

and destination of trips, chosen method of transport and the assigned route. The output of the

model is used to forecast potential problematic traffic scenarios such as congestion.


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measures that need to be taken in order to tackle atmospheric pollution from exhaust emissions as

well as the increased concern regarding car dependency.

There are three main categories of traffic simulation which examine the network flow on a different

scale. Likewise for each traffic simulation, there are also emission and air pollution models for each

of these. To examine how vehicle interactions affect the dispersion of vehicle emissions, an

integrated approach is adopted which combined traffic simulation with emissions modelling.

2.6.2. Macroscopic Traffic simulationMacroscopic traffic flow modelling was originally developed in 1955 by Lighthill and Whitham who

noticed similar characteristics of both flood movements in long rivers and traffic flow on crowded

roads (Wikipedia 2013).Traffic streams were compared with fluid streams which later served as a

platform for the development of Richards LWR (Lighthill Whitham Richards) theory in 1956.

Macroscopic modelling is used to identify problematic areas in the traffic network and to evaluate

the impacts of urban development on the performance of existing infrastructure. Over recent years

macroscopic modelling has increasingly gained popularity for modelling major urban areas and

motorways due to the ease in which input data measurements can be obtained (Tate 2005).


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modelling approach is adopted. Meso-scale traffic simulation models are able to replicate average

vehicle speeds, link and junction capacities, signal phasing, green splits and saturation flows. When

combined with emission modelling software, this information can be used to estimate quantities of

vehicle emissions that are emitted into the atmosphere.

Mesoscopic traffic models simulate groups of vehicles in platoons. Each platoon acts as a single

entity where the speed of the platoon is measured from a speed-density function which relates the

speed of the vehicle to the saturation density. Another form of a mesoscopic simulation is that of

individual vehicles that are grouped into cells. The cells determine the speed of the vehicle and

enable vehicles to enter and leave cells as necessary - effectively controlling the individual behaviour

and decisions of the driver (Burghout 2005).

2.6.4. Microscopic Traffic SimulationA microsimulation model describes individual vehicle movements and behaviour around a traffic

network. Individual vehicles usually follow a pre-determined route which is defined according to

simple car following, lane changing and gap acceptance rules. Microscopic simulation is able to

model complex traffic systems and congested networks whilst providing a graphical representation


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trajectory, speed, acceleration and driver behaviour have upon the general traffic network. Traffic

micro-simulators can also extract detailed vehicle information when the traffic network is connected

to a responsive Urban Traffic Control (UTC) system. Systems such as SCOOT can be used by transport

planners to predict traffic flow and therefore intercept the occurrence of traffic congestion (Tate and

Fox 1997).

2.7.Microsimulation Modelling Software

2.7.1. AIMSUNAIMSUN (Advanced Interactive Microscopic Simulator for Urban and non-urban Networks) is a

transport microsimulation software that is capable of reproducing real life traffic conditions. The

software operates on three parts of input data to accurately simulate the traffic network. The road

network is described by providing details of the physical layout including road length, width and

elevation. The software also offers a wide range of geometries that can be used to define various

types of intersection. Traffic signal settings are described which involve specifying phase cycle,

staging times and cycle time; and adjusting these for different periods throughout the day. These are

discrete elements of the software. Traffic conditions are modelled through determination of the

saturation flow, type of vehicle and driver behaviour. As these are factors which constantly change,


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macroscopic traffic models that simulate driver movements along a chosen route, to modelling

individual driver choices at the micro level. At the most detailed level, the model has six main input

demands: travel cost, origin-destination matrix, route choice, time of departure and external factors

(such as different weather conditions). This input data is uploaded to a second by second interval

model; which forms an average day travel cost for each vehicle on the network (Tate 2005 and Fox

1997).

There are many fixed assumptions in the software that can be adjusted and vice versa flexible

criteria that can be aggregated. The flexibility and range of assumptions that DRACULA can

incorporate offers numerous advantages over traditional modelling. DRACULA is ideal for modelling

events that can impact road capacity and driver behaviour such as extreme weather conditions or

diversions. Due to the softwares ability to simulate how drivers react to such situations using lane

changing and gap acceptance rules, driver behaviour can also be modelled under congested

scenarios such as queue spillback or dynamic propagation (Tate 2005 and Fox 1997).

2.7.3. PARAMICSParamics (PARAllel MICroscopic Simulation) was originally developed by SIAS and EPCC. The


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2.7.4. SISTMSISTM (SImulation of Strategies for Traffic on Motorways) was developed for the Highways Agency

to create strategies in order to deal with motorway traffic congestion. The software is used to study

the effects of various motorway layouts, speed limits and ramp metering on congested traffic

systems, as well as assess the impact of modified vehicle characteristics and driver behaviour. Speed

and headway are determined according to driver behaviour which are controlled by the level of

aggressiveness and awareness they're assigned. Lane changing behaviour is controlled by

implementing a lane changing stimulus which allows the driver to merge smoothly onto an adjacent

lane. A car following sub model is incorporated into the model which uses an algorithm to simulate

car following behaviour. Although SISTM boasts many advanced technical features, the software is

limited by its inability to model detailed road features such as ghost islands, narrow lanes, bends, as

well as having a limited database for modelling different types of vehicle (Fox 1997).

2.7.5. VISSIMVISSIM (Traffic In-cities Simulation Model) is a discrete time step based microsimulation model


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Additionally the software provides an open interface to enable its users to define the UTC logic.

Owing to its ability to integrate add on features, the software has gained commercial popularity (Fox

1997).

2.7.6. Review of Microsimulation Modelling Software

Factors

Microsimulation Software

VISSIM AIMSUN PARAMICS

(SIAS)

PARAMICS

(QUADSTONE)

DRACULA SISTM

2D Visual

Representation

3D Visual

Representation

Simulation of

various vehicle

types

Displays shapes and

dimensions of

vehicles

Data collection

Table 2.2Comparison of microsimulation modelling software


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2.8.Emission modelling

2.8.1. IntroductionEmission models must be able to model current vehicle emissions and evaluate how alternate

driving patterns affect the dispersion of atmospheric pollutants. There are two alternative models

that are most commonly used: the average speed emission model - used predominantly for

analysing emissions over large spatial areas; and instantaneous emission modelling - which produces

second by second emission measurements according to the speed of the vehicle. The accuracy of the

emissions modelled depend upon input factors such as the vehicle load and type, type of fuel used,

distance travelled, road inclination, gear box, speed, acceleration and weather conditions. Therefore

it is important when selecting the model, to evaluate the input parameters that are necessary for

modelling and the output range of pollutants that are produced (Tate 2005).

2.8.2. Average-Speed Emission ModellingAverage speed models have gained popularity in use over recent years by transport engineers and

planners as they are relatively easy to use due to the information for input being widely readily

available to its users. The average speed approach produces measurements for different pollutants


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emission rates are determined by conducting a series of cycle trials with a tracked vehicle to

determine second by second pollutant emissions.

Instantaneous emission models are not actually able to precisely replicate real-time emissions due to

the "dynamic" delay in processing the speed and acceleration of the vehicle. This makes it difficult to

accurately map the vehicle exhaust emissions. Some emissions modelling software apply corrective

measures to account for these effects. Figure 2.10 below displays how data from the vehicle is

processed and the set-up that is used for CVS modelling.


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of the following pollutants: CO, HC, NOx, CO2 and fuel consumption. The software demands vehicle

engine operation parameters to be specified in order to generate second by second vehicle emission

predictions. The updated version, CHEM phase 2, takes a more deterministic approach which utilises

data from vehicle weight, engine size and aerodynamic drag for instantaneous emissions modelling;

in contrast to the descriptive approach employed in CHEM phase 1 and MODEM modelling (Tate

2005; Stroumtsas 2012).

2.9.3. PHEMPHEM (Passenger and Heavy-duty Emission Model) uses instantaneous engine power demand and

user specified driving patterns. The passenger car adjusted-model is capable of simulating fuel

consumption, NOx, NO2, HCs, Particulate Mass (PM10), Particulate Number (PN) and CO exhaust

emissions for petrol and diesel EURO 0 - 5 vehicles, and offers an extensive database of vehicle

types and engines. PHEM heavy-duty is an unadjusted model that is capable of mapping emissions

for heavy-duty vehicles including bus, coach, rigid and articulated HGVs, making it ideal for vehicle

fleet sampling.

The main input required from the microsimulation model are vehicle speed and road gradient to


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categorised as macro, meso or micro scale models. Macro-scale models typically simulate the global

dispersion of pollutants; meso-scale models operate at regional/city level, whilst micro-scale models

are more concerned with more detailed air flow patterns. Air pollution models are currently the

subject of ongoing research and are rarely used in practice in conjunction with traffic and emission

models. Therefore due to the lack of development in integrating traffic, emission and air quality

models at the micro-scale level, this study will solely focus on combining and integrating traffic

microsimulation and vehicular emissions.

2.11. Model Integration

In order to map vehicle emissions, an integrated modelling approach is adopted which combines and

simulates vehicle fleet travel patterns to map exhaust emissions. Figure 2.11 below illustrates the

different scales and parameters that link traffic, emission and air quality models. This study will focus

on combining a microscopic transportation model with an instantaneous emission model.


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emission models. Traffic microsimulation applications are used to simulate vehicle and driver

behaviour in a road network whereas emission modelling is concerned with simulating exhaust

emissions. The importances of parameters that are required for traffic microsimulation differ largely

to those used in instantaneous emission modelling. Therefore it is essential to ensure the

microsimulation software output variables match those that are required as input factors for the

instantaneous emission model.

All instantaneous emission models require information regarding the traffic flow in the network,

which can be specified by the total number of vehicles modelled in the microsimulation programme

over a period of time. One of the main challenges involved is matching the vehicle classification.

Poor traffic-emission model representations use speed profiles to predict the atmospheric

dispersion of exhaust emissions. A more accurate approach is to use the acceleration profile and

vehicle power demand data. Software such as PHEM requires such information. Specifying road

length and gradient enable the creation of a microsimulation model that accurately represents the

terrain. These can be provided as output information by AIMSUN and VISSIM and are required as

input information in the PHEM instantaneous emission model (Stroumtsas 2012; Tate 2005).


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Different researchers suggest alternate methods for model calibration. Lee, Yang and Chandrasekar

(2001) and Park and Schneeberger (2003) suggest focusing on specific key routes rather than the

entire network, which is in contrast to Cascetta and Postorinos (2001) opinion who emphasise that

the focus should be on route and OD flows. On the other hand Toledo et al (2004) presents a

combined integrated approach to calibration.

Likewise for traffic model validation - the approach that is adopted depends primarily upon the

software that is used. Toledo et al(2003) suggest using aggregate data such as speeds and flows to

validate the three sub - models and using disaggregate data to investigate the capability of the

model to represent reality. Rao and Owen (2000) suggest using visual representations of the

outputs, such as graphs, as well as a statistical validation method which uses confidence intervals

and other statistical tests to compare modelled and observed data. The AIMSUN manual provides

statistical methods such as regression analysis, Root Mean Square Errors (RMSE) and hypothesis

testing to test data between observed and modelled parameters. However for the purposes of this

dissertation the microsimulation model's calibration and validation will be based upon the collection

of data from a GPS tracking device. This method is based on that of Brockfeld, Kuehne and Wagner

(2004) who adjust the deviations between the observed and modelled data to calibrate and validate


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3.1.Data Collection

3.1.1. Vehicle Tracking SurveyIn order to accurately simulate an up to date version to describe the traffic movements of the

transportation network measurements were recorded by tracking a vehicle during the morning

peak. A VBOX II Lite GPS & CAN Logger was used to record the vehicle speed and specify longitude

and latitude co-ordinates of the vehicle to determine its position. The GPS antenna was magnetically

secured to the roof of a 2001 1.6L Vauxhall Astra with the CAN Bus lead connected to the VBOX II

Lite GPS & CAN unit within the vehicle (see appendix A for user guide).

Before setting out, a risk assessment was completed (see appendix B), the route pre-determined and

weather conditions checked to ensure each trip conducted represented an average day. The vehicle

left the parking area adjacent to Wakefield Road and travelled up Salterhebble Hill. The vehicle then

turned at the area identified in figure 3.1 before returning in the opposite direction along the

southbound route. At the end of each trial the memory card was ejected and re-inserted before

recording the following set of measurements. The survey was performed to collect a variety of data

on three separate days between 08:00AM and 09:00AM when the road network is at its peak. GPS


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Figure 3.1 Satellite images of vehicle

turning areas (GOOGLE EARTH 2011)


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After extracting and processing the raw data from the FLASH memory card, the readings were

assorted to produce measurements at 1Hz (one-second intervals). In order to integrate the data with

emissions modelling software to predict vehicle emissions, it was necessary to define the road

gradient of the traffic network. The elevation profile tool available through Google Earth was used in

combination with the map of the longitude and latitude measurements to determine the gradient of

different road sections (see figure 3.2).


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3.1.2. Traffic CountingTraffic counting was performed in order to classify the vehicle fleet in order to calibrate and validate

the microsimulation model. The survey took place on Thursday 1/12/2012 and had involved

categorising the number of vehicles that passed over a certain point over a set time period. Vehicles

were classified as a car, van, HGV (rigid), HGV (articulated), bus or coach and were counted using a

multi-unit tally counter. Traffic counting was performed for a fifteen minute period at the location

displayed in figure 3.3 for both northbound and southbound traffic. The results obtained will be

discussed in the model set-up.

Figure 3.3 Satellite image

highlighting the location at

which traffic counting

measurements were conducted

(GOOGLE EARTH 2011)


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In order to ensure representative measurements were recorded, it was important to consider the

number of vehicles in the queue to ensure the flow of traffic could be recorded over fifteen seconds.

Other practical constraints meant it was not possible to classify the vehicle fleet whilst measuring

the saturation flow rate. Therefore to obtain the saturation flow rate in PCUs per hour the number

of vehicles were multiplied by the PCU weighting of the vehicle fleet. The results of the saturation

flow rate will be discussed further in the model set-up.

Figures 3.4 - 3.6 display the location at which the saturation flow rate recordings were taken.

Figure 3.4Satellite image

highlighting the location at Shaw Hill

intersection where the saturation

flow rate was measured (GOOGLE

EARTH 2011


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3.2.Model Set-up

3.2.1. Aimsun Microscopic Traffic Simulator

Based on the comparison of microsimulation software in table 2.2 Aimsun was selected for this

project. The softwares ability to model urban and rural site characteristics and simulate discrete and

continuous parameters makes it ideal for modelling Salterhebble Hill. Input requirements needed to

calibrate and validate the transportation network included the vehicle fleet composition, traffic

signal control plans, road geometry and saturation flow rate.

Gippss (1981) car following model is based on safe distances between vehicles to avoid the

occurrence of a collision; whereas the car following logic used in Aimsun is dictated by driver

behaviour which is defined by the level of or lack of aggressiveness of the driver (i.e. the tendency to

reach high speeds or accept speed limits). Lane changing logic is defined in the network as a decision

process that analyses the necessity, desirability and feasibility of changing lanes. Defining lanechanging behaviour is essential for vehicles to avoid obstructions or overtake or give way to other

vehicles. Aimsun 6.1 uses a lane changing sub-model developed by Gipps (1986) in combination with

the car following model previously described. Gap acceptance models determine whether a gap

between two consecutive vehicles is acceptable. Gap acceptance logic is related to lane changing

behaviour as acceptable gaps are used to define the desire of vehicles to switch lanes. The principle


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saturation flow rate were represented correctly in the model as previously noted in sections 3.1.2 &

3.1.3.

Current signal control plans were compared with the traffic signal settings used in the model to

determine whether any alterations had taken place; as these were determined from pre-dated signal

control plans that were collected from data in 2010. The following section discusses this aspect of

the model in greater detail.

3.2.3. CalibrationBetween 08:00am and 09:00am the signal phase, cycle length, offset, green time and stop time were

observed for each traffic signal in the survey site. The measurements observed were compared with

the signal control plans in the model, and updated to reflect the true traffic signal settings.

During the peak period the traffic signal settings at Exley Bank, Stafford Road and Stafford Avenue

were in free flow. The signals were programmed to constantly display a green light unless triggered

by a pedestrian pushing the button to cross the road. The signals were fixed on a 270 second cycle

time and displayed a stop light for 10 seconds every four and a half minutes for crossing pedestrians.


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display the original control plan and the amended control plan which will be used in the calibrated

model.

Figure 3.7 Snapshot from Aimsun highlighting the

signal phases at Dryclough Lane junction


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3.2.4. ValidationValidating the Aimsun model meant comparing the observed and modelled vehicle proportions and

traffic flow. Modelled vehicle proportions were measured by creating a detector at the location

where data was collated and simulating the network between 08:00am and 09:00am. At the end of

the simulation the number of each type of vehicle was displayed which were used to determine the

vehicle proportions simulated in the model.

The vehicle fleet data that was collected in the survey was converted to an hourly equivalent to

compare the observed and modelled vehicle proportions. The GEH statistical method was then used

to gauge the performance of the Aimsun model. This method is commonly used in traffic modelling

to compare two sets of traffic volumes. For modelling in the base scenario, a maximum allowable

variation of 5% is considered a good match between the modelled and observed hourly volumes.

Volume 12 of the UK Highways Agencys Design Manual for Roads and Bridges states that at least

over 85% of the different vehicle proportions should have a GEH value of less than or equivalent to

5.0%. If the total percentage for the vehicle proportions is less than 85% further investigation of the

vehicle fleet should be conducted.


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South bound

Vehicle Type Modelled (%) Observed (%) GEH (%)

Car 87.6 79.1 32.7

Van 8.5 16.1 4.9

HGV (rigid) 2.8 3.4 4.7

HGV (articulated) 0.8 0.2 4.5

Bus 0.2 0.6 0.4

Coach 0.2 0.6 0.4

Tables 3.1 and 3.2 confirm that over 85% of the GEH statistics are less than 5.0%, thus verifying the

validity of the vehicle fleet composition in the updated model.

The validity of the network traffic flow was verified by evaluating the saturation flow rate data

collected and comparing it to that in the model. The Aimsun model data was collected by placing a

traffic detector at each of the three locations where saturation flow data was collected. By setting

the time series tab on the detector to display the number of vehicles counted in 5 second intervals it

was possible to measure the saturation flow throughout the simulation.

Table 3.2 - Observed and modelled vehicle proportions and the GEH statistic for south bound traffic


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The observed and modelled values calculated in tables 3.3 and 3.4 display minute discrepancies and

therefore this had confirmed that the model had accurately reflected true traffic conditions. Data

collected regarding the vehicle fleet composition and saturation flow rate in this section validated

the model.

South bound Traffic Saturation flow rate (PCU = 1.0314)

Location Modelled (PCUs/hour) Observed (PCUs/hour)

Exley Bank 1609 1753

Dryclough Lane 2070 2733

Shaw Hill 3218 3197

Table 3.4 Observed and modelled saturation flow rate for south bound traffic


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4.1. Introduction

An existing traffic management plan developed in Aimsun was examined prior to devising the gating

strategy in this dissertation. The traffic management plan was designed by the Institute for

Transport Studies at the University of Leeds and implemented a VMS to queue traffic north of

Jubilee Road junction as shown in figure 4.1. The gated signal was based on traffic detector readings

measured at Salterhebble Hill at the location circled in figure 4.2.

Left:Figure 4.1 Snapshot from Aimsun displaying the

VMS in operation


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4.2.Overview of the Gating Management Strategy

The primary objective in developing the gating strategy was to relocate the traffic queue from

Salterhebble Hill. By co-ordinating the signals at Exley Bank with those in the rest of the network it

was possible to prevent multiple stop-start traffic movements of vehicles travelling through

Salterhebble Hill. Vehicles were gated at the traffic signals at Exley Bank and pulsated through the

network in platoons at fixed intervals. The key to designing an effective gating strategy was to create

a green wave whereby the vehicle platoon passed through subsequent signals at green. This was

possible by matching the time for the length of the vehicle platoon to clear with that of the green

time set in the traffic signal control plan.

4.3.Design of the Gating Management Strategy

4.3.1. Coordination of Exley Bank SignalsBefore designing the signal control plan at Exley Bank the phase times for the signal settings at

Dudwell Lane and Dryclough Lane were evaluated. It was essential to design the signal control plan

at Dudwell Lane to give the green light as the traffic fleet approached the signals. The signal settings


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Before designing the control plan, the average time for a vehicle to travel from Exley Bank to

Dudwell Lane was measured in order to set the time for the vehicle platoon to set out. The average

vehicle travel time measured for this route was 46 seconds. The green signal at Exley Bank was set to

display at 44 seconds after the green signal was displayed for the north bound route at Dudwell

Lane. The signal plan at Dudwell Lane consisted of 57 seconds green time followed by 33 seconds

stop time (57 + 33 = 90 second cycle time). Therefore the green signal at Dudwell Lane was offset by

46 seconds from the Exley Bank (13 seconds remaining green time from the previous phase + 33

seconds of red signal time) which was equivalent to the vehicle travel time from Exley Bank toDudwell Lane, thus ensuring a smooth transition of the vehicle fleet through Dudwell Lane (refer to

time-space diagram in figure 4.7).

The duration for the green signal at Exley Bank was set to 60 seconds as this was the time measured

for the vehicle platoon to clear the traffic signals (refer to figure 4.6 image 2). The stop time at

Exley Bank was set to 30 seconds to balance the length of the vehicle queue on Huddersfield Road

without increasing congestion or furthering traffic delay on Elland Wood Bottom (refer to figure 4.6

image 3). Figure 4.4 below illustrates the adjusted control plan for the signals at Exley Bank.


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enable vehicles held at the end of the platoon to pass through without being stopped. Figure 4.5

below displays the signal control plan for Dryclough Lane.

4.4.Overall Signal Control Plan

Figure 4.6 can be used to map the location of vehicles progressing through the traffic network as

described by the time-space diagram in figure 4.7. The time space diagram illustrates the

relationship between the time and location of the vehicles in the traffic network. The individual

signal control plans at the location listed on the X-axis are represented by the vertical bars. These are

Figure 4.5 Snapshot from Aimsun displaying

the signal control plan for Dryclough Lane


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Dryclough Lane

Junction Traffic

Signals Dudwell Lane Junction

Traffic Signals

Shaw Hill Traffic Signals Group

IMAGE 1

IMAGE 2

Figure 4.6 Snapshot

from Aimsun of the

Traffic Network

Figure 4.7 Time-Space Diagram of the co-ordinated signals in the New Gating Strategy


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44

150

120

90

60

30

0

517m 169m 166m 178m 459m

Exley Bank Dudwell Lane Dryclough Lane Shaw HillStafford Road Stafford Avenue

Distance

[NOT TO SCALE]

Time (s)


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4.5.Trial Simulation

In order to ensure the developed gating strategy performed as designed, the average travel time of

vehicles travelling from the SOUTH centroid to the NORTH centroid were measured in the base

model, the OGS and the NGS in this dissertation. The results displayed below compare the average

delay experienced by a vehicle in the traffic network and had confirmed that the developed gating

strategy reduced overall congestion. The figures displayed in table 4.1 represent the travel time from

the previous location. E.g. in the base network 5:34 represents the travel time from Junction A6026

to Exley Bank Signals. The locations listed in the far right column can be referred to in figure 4.6.

Average Travel Time (minutes:seconds)

Location Base Model Original Gating Strategy New Gating Strategy

Centroid: SOUTH 0 0 0

Junction A6026 41:15 38:10 36:22

Exley Bank Signals 5:34 5:25 4:15

Dudwell Lane Junction 3:01 1:33 0:59

Centroid: NORTH 4:00 2:00 2:00

TOTAL 53:50 47:08 43:36


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5.1. Introduction

Trajectories for the base traffic network, ITS designed network and the designed gated strategy

produced in this dissertation were harvested in the microsimulation model and then processed in

the emissions model. Ten simulations were run for each of the transportation plans to ensure the

simulation ran smoothly without locking up any intersections throughout the traffic network.

5.2. Validation of the Vehicle Fleet

The vehicle fleet composition from the ten simulations in the base network was used to verify the

modelled and observed proportions in table 3.1 and 3.2. Table 5.1 below confirms the proportions of

the vehicle fleet:

Type of vehicle Observed (%) Modelled (%) Base Network (%)

Veh 100: Car 80.6 86.2 85.1

Veh 300: Van 13.4 8.4 9.2

Veh 200: HGV (rigid) 3.3 2.8 2.0

Veh 400: HGV (articulated) 1.3 1.6 2.5


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passenger car for trial 3. The vertical lines at the top of the graph divide the length of the trial into

links according to the variation in gradient throughout the duration of the route. The vertical dashed

line between link 17 and 18 is highlighted in each one of the profiles as it represents the point at

which the route changes from the north bound to the south bound route.

Profiles a-d in figure 5.1 display a directly proportional relationship between FC, NOx and PM

emissions and between the profiles for speed and fuel consumption. Figure 5.2 displays the speed,

FC, NOx and PM profiles for a petrol EURO 5 vehicle simulated in the base model. By comparison of

figure 5.1 and 5.2 it is clear that the time spent in each part of the network between the two models

differ. The values for the profile in figure 5.2 are condensed further than those displayed in figure

5.1 and therefore appear differently as a result of the travel time differences between the survey

trial and the simulation in the base model. The data used to simulate the base network was collected

in 2010 by Dr James Tate and the observed data was collected in 2012. As a result of changes caused

by the economic slowdown and the addition of bus ways to the road network by Calderdale Council,

there were observed differences in the traffic fleet and travel times for the data collected in 2010

and in 2012.


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48

Figure 5.1 Petrol EURO 5 passenger car time-series profile (observed model): (a) Speed, (b) Fuel Consumption, (c) NOx and (d) Particulate Matter

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 30 31 32 33 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 30 31 32 33 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 30 31 32 33 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 30 31 32 33 34


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49

Figure 5.2 Petrol EURO 5 passenger car time-series profile (base model): (a) Speed, (b) Fuel Consumption, (c) NOx and (d) Particulate Matter


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5.4. Distribution of Vehicle Emissions

This section compares the distribution and quantity of emissions emitted in the base network,

original gating strategy and the developed gating strategy to determine whether the gating strategy

that was devised improved the base scenario and whether it improved emission reductions from the

original gating strategy. Simulations between 08:00am and 09:00am were conducted for the ten

replications in each of the models. Data from the simulations were then processed in PHEM to

determine the total emissions for each road section of the network. Total emission contributions for

NOx and PM were measured as well as the fuel consumed on each section of the road. These two

pollutants were examined as they are main contributors to local air pollution and therefore of

primary interest to this study. Figures 5.3 5.8 below display the emissions and fuel consumption for

each section of the main route in the north and south bound directions for the base network and the

two gating strategies. The road segments are listed on the bar chart in consecutive order; for ease

of referral these have been labelled in the map shown in figure 5.9. The road segments for the north

bound route are labelled in black and the south bound route in red.

700


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51

0

100

200

300

400

500

600

700

FC (g/km)

Road Section

Base Network

Original Gating Strategy

New Gating Strategy

0

50

100

150

200

250

300

350

400

450

FC (g/km)

Road Section

Base Network


New Gating Strategy

Figure 5.3 Bar chart displaying the FC for each section of the road network for the north bound route

Figure 5.4 - Bar chart displaying the FC for each section of the road network for the south bound route

12


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52

0

2

4

6

8

10

12

NOx (g/km)

Road Section

Base Network


New Gating Strategy

0

1

2

3

4

5

6

NOx (g/km)

Road Section

Base Network


New Gating Strategy

Figure 5.5 - Bar chart displaying the NOx emissions for each section of the road network for the north bound route

Figure 5.6 - Bar chart displaying the NOx emissions for each section of the road network for the south bound route

0.25


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53

0

0.05

0.1

0.15

0.2

PM (g/km)

Road Section

Base Network


New Gating Strategy

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

PM (g/km)

Road Section

Base Network


New Gating Strategy

Figure 5.7 - Bar chart displaying the PM emissions for each section of the road network for the north bound route

Figure 5.8 - Bar chart displaying the PM emissions for each section of the road network for the south bound route


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796

2003331

942335

352

386

384

392

400

1070

409417 415

4151068

402

391

382378

375374

354

333

329933

295

Figure 5.9 Map of Aimsun road network displaying the

location of each section referred to in figures 5.3 5.8.


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5.4.1. North bound Traffic

Results for the north bound route display a significant difference between the section emissions inthe base model to those in the gated model. Between sections 251 to 335 the three models display

considerable differences in the measured quantities of FC, NOx and PM. In contrast, between

sections 352 and 417 the quantity of fuel consumed, NOx and PM emitted for the three models

display little variation. The bar chart was therefore analysed in two parts to separately examine the

characteristics of the north bound route.

The parts of the network represented by sections 251 - 335 were Elland Wood Bottom, Huddersfield

Road (south of Salterhebble Hill) and Salterhebble Hill. From figures 5.3, 5.5 and 5.7 it can be

observed that the NGS (New Gating Strategy) produced significantly lower emissions than the base

network and the OGS (Original Gating Strategy). The total emissions were accumulated for each

section and are displayed in table 5.2 for the NGS, base and OGS model.

Section 251 - 335

FC

(g/km)

NOx

(g/km)

PM

(g/km)

Table 5.2 Total FC, NOx

and PM emissions in the

OGS, NGS and base model

for road section 251 335


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The road network between sections 352 and 417 starts from Dudwell Lane junction through to Shaw

Hill. During the simulation phase of the project it was noted that there was little congestion on this

part of the road in the base model and therefore little room for improvement in the OGS and NGS.

As a result of this, there was little variation in FC, NOx and PM emissions in the three models, as

displayed in table5.3.

Section 352 - 417

FC(g/km)

NOx(g/km)

PM(g/km)

Base 1706.28 21.303 0.576

OGS 1750.66 19.791 0.575

NGS 1699.69 17.963 0.568

Despite little variation in FC and PM emissions in the three models, the NGS was successful at

reducing NOx emissions by 15.7% from the base model and by 9.2% from the OGS. The NGS had

effectively improved the base model and OGS by further reducing emissions along the north bound

route. The next section will examine emissions along the south bound route.


and PM emissions in the

OGS, NGS and base modelfor road section 352 417


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South bound route (415 242)

FC (g/km) NOx (g/km) PM (g/km)

Base 2975.82 34.119 0.876

OGS 2869.91 33.185 0.927

NGS 3223.66 37.115 1.044

The NGS had shown an increase by 7.7% in FC, 8.1% in NOx and 16.1% in PM emissions compared to

the base model. Relative to the OGS, the NGS had displayed an increase in FC by 11.0%, in NOx by

10.6% and in PM emissions by 11.2%. For the south bound route the emissions produced by the NGS

were larger than those in both the OGS and the base model. Section 5.2.2 compares NOx, PM and FC

for the combined south and north bound routes.

5.4.3. North and South bound Traffic

The total emissions and FC for the north and south bound routes were combined below in table 5.5.

The results compare the total emissions produced in the base model, OGS and NGS.


and PM emissions in theOGS, NGS and base model

for the south bound route


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5.4.4. Relocated Vehicle Emissions

The difference in emissions between the north bound route and the south bound route werecalculated in order to determine the proportion by which the gating strategies had reduced or

relocated emissions in the traffic network. As the only factor affecting the movements of the traffic

fleet were caused by alterations to the traffic signal settings an increase in congestion on the south

bound route could only be a direct consequence of alterations to the signal settings of the north

bound route. For example, adjusting the amount of green time given in the NGS to north bound

traffic would consequently change the phase times for the south bound route and affect the traffic

flow.

The OGS had transferred 36.3% of NOx and PM emissions to another area of the traffic network as a

result of relocating the traffic queue. The NGS was successful at transferring 28.3% of total emissions

and therefore relocated a lower proportion of emissions from the north bound route to the south

bound route. The NGS therefore eliminated a greater proportion of emissions from the traffic

network than the OGS.

5.5. Emission Contributions per Vehicle Type


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Passenger Car

70.9%

Rigid HGV

9.4%

Van

6.5%

Articulated

HGV

9.0%

Bus

1.3%

Coach

2.9%

Passenger Car

66.2%

Rigid HGV

Van

6.7%

Articulated HGV

13.0%

Bus

1.7%

Coach

4.1%

Figure 5.10 Chart displaying the proportion of each vehicle contribution to FC in the base model


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Passenger Car

36.4%

Rigid HGV

16.5%

Van

8.9%

Articulated

HGV

22.5%

Bus

5.1%

Coach10.6%

Passenger Car

38.7%

Rigid HGV

23.5%

Van

8.6%

Articulated HGV15.1%

Bus

4.8%Coach

9.3%

Figure 5.13 - Chart displaying the proportion of each vehicle contribution to NOx emissions in the base model


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Passenger Car

50.2%

Rigid HGV

13.8%

Van

14.2%

Articulated

HGV

11.0%

Bus

3.3%

Coach

7.5%

Passenger Car

48.8%

Rigid HGV

Van

13.5%

Articulated

HGV

12.1%

Bus

4.0%Coach

10.3%

Figure 5.16 - Chart displaying the proportion of each vehicle contribution to PM emissions in the base model


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5.5.1. Fuel ConsumptionAlthough passenger cars accounted for two thirds of the fuel consumed in the three models, almost

two thirds of NOx emissions and over half of PM emissions were accounted for by heavy duty

vehicles. The proportion contributed by HGVs, buses, coaches and vans to PM and NOx emissions

significantly outweighed that contributed by passenger cars.

5.5.2. NOxAs a result of the NGS, the total NOx emitted by coaches was reduced by 2.5% whereas the OGS was

unsuccessful at reducing this value from that of the base network. Surprisingly, for articulated-HGVs

the total NOx emissions in the OGS increased by 7.4% and in the NGS by 5.7%. Emissions

contributed by rigid HGVs were reduced in the OGS by 7% and in the NGS by 2.6%. Overall this had

confirmed that the OGS and NGS were both successful at reducing the proportion of NOx emissions

contributed by heavy duty vehicles.

5.5.3. Particulate MatterSimilar patterns to NOx emissions were displayed for the proportion of PM reduced in the NGS for


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5.6. Emission Contributions in Petrol and Diesel Engine Passenger Cars

Car (Petrol) EU0

0%

Car (Petrol) EU1

1%

Car (Petrol) EU2

7%

Car (Petrol) EU3

25%Car (Petrol) EU4

22%

Car (Petrol)EU5

7%

Car (Diesel)

EU0

0%

Car (Diesel) EU10%

Car (Diesel) EU2

2%

Car (Diesel) EU3

9%

Car (Diesel) EU421%

Car (Diesel) EU5

6%

Car (Petrol)

EU1

Car (Petrol) EU2

8%

Car (Petrol) EU3

27% Car (Petrol) EU4

21%

Figure 5.19 - Chart displaying the proportion of FC contributed by petrol and diesel (EURO 0 5) passenger cars in

the base model


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Car (Diesel) EU2

3%

Car (Diesel) EU3

26%

Car (Diesel) EU4

36%

Car (Diesel) EU5

16%

Car (Petrol) EU0

1%Car

(Petrol)

EU1

1%

Car (Petrol)

EU2

4%

Car (Petrol) EU3

4%

Car (Petrol) EU4

5%

Car (Petrol) EU5

1%

Car (Diesel) EU0

0%

Car (Diesel) EU1

0%

Car (Diesel) EU24%

Car (Diesel) EU3

24%

Car (Diesel) EU4

43%

Car (Diesel) EU5

13%

Figure 5.22 - Chart displaying the proportion of NOx contributed by petrol and diesel (EURO 0 5) passenger cars in

the base model


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Figure 5.25 - Chart displaying the proportion of PM contributed by petrol and diesel (EURO 0 5) passenger cars in the

base model

Car (Petrol)Car (Petrol) EU2

Car (Diesel)

EU2

8%

Car (Diesel) EU3

28%Car (Diesel) EU4

28% Car (Diesel)

EU5

2%

Car (Petrol)EU0

0%Car (Petrol)

EU1

1%Car (Petrol) EU2

8%

Car (Petrol) EU3

12%

Car (Petrol) EU4

5%

Car (Petrol) EU5

1%

Car (Diesel) EU0

1%

Car (Diesel) EU1

0%

Car (Diesel) EU2

11%

Car (Diesel) EU3

28% Car (Diesel) EU4

32%

Car (Diesel)

EU5

1%


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5.6.1. Analysis of Emission ContributionsAlthough over 60% of FC was attributed to petrol engine cars in the base model, OGS and NGS; 80%

of NOx emissions produced in the OGS and base model, and 67% in the NGS, were emitted by diesel

cars. Despite the low quantities of CO2 produced from diesel exhaust emissions, diesel engines

produce 24 times more NOx than petrol engines (ECO Travel 2007). Diesel cars accounted for 72% of

PM emissions in the base model and 67% in the NGS.

5.7. EURO Class Petrol and Diesel Vehicles

European emission standards were introduced to limit the quantity of exhaust pollutants emitted for

new vehicles sold in the EU. The stringency of the emission standards are progressively introduced in

order to reduce the levels of CO2 and other chemicals that contribute to atmospheric pollution.

Passenger cars are classified as EURO 0 - 5 according to their engine technology, conventionally

determined by the manufacturing year of the vehicle. At present limitations on the emission

concentrations of the following exhaust emissions are upheld in the EU: NOx, HC, CO, PM and

NMHC. Table 5.6 below displays emission limits for NOx and PM for EURO 0 - 5 diesel and petrol

passenger cars.


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Table 5.7 displays the FC, NOx, PM, mass and power ratings for EURO 0 5 passenger cars. The FC,

NOx and PM emissions for each type of vehicle did not directly correlate with the mass and power

output of petrol engine vehicles. By contrast diesel engine vehicles showed a directly proportionalrelationship between vehicle generation and the level of NOx and PM emitted. Although the mass

and power output in modern diesel vehicles exceeded that of cars using an older engine technology,

as a result of modern vehicles being more fuel efficient, the quantities of NOx and PM emitted were

far lower in modern diesel engines vehicles than vehicles using older engine technology.

Generation FC (g) NOx (g) PM (g)Power Rating

(kW)Mass

(kg)

Petrol

EURO 0 411.95 3.682 0.075 100 1230.84

EURO 1 136.14 0.461 0.024 80 1125.16

EURO 2 117.20 0.381 0.032 74 1080.52

EURO 3 90.07 0.104 0.010 80 1169.57EURO 4 113.34 0.167 0.005 82 1177.11

EURO 5 142.26 0.222 0.006 81 1194.92

Diesel

EURO 0 385.56 5.639 0.626 54 1370

EURO 1 176.93 2.602 0.327 70 1415.71

EURO 2 246.20 3.842 0.274 80 1339.97


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CHAPTER 6: CON

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