Outline

Background – Modelling Road Transport Emissions

Large-scale Networks e.g. Regional / National

City Networks

Modelling a “Virtual World”

Framework

Microscopic traffic simulations

Instantaneous vehicle emission modelling

Calibration & Validation

Results

Mapping vehicle emissions

Spatial & temporal variations

Summary & Conclusions

Work in progress

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MODELLING LARGE-SCALE NETWORKSRepresented a Line Sources

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City of York

Source: http://ntis.trafficengland.com/map 7.02 am

16/09/2014

100 km

MODELLING CITY NETWORKSShort links

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5 km 500 m

A “VIRTUAL” YORKCoupled micro-scopic traffic & instantaneous emission model

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TRAFFIC MICROSIMULATIONS1

TRAFFIC DEMAND

Average weekday (May 2011)

Automatic Traffic Count (ATC) & Manual Count data

J ANPR surveys (19th May 2011, 0700 – 1900hrs)

TIME PERIODS

AM shoulder

AM peak

Inter-Peak

PM peak

PM shoulder

Evening

NIGHTtime

24-hour weighted average

1 The York 2011 S-Paramics network created by David Preater (Halcrow,

2011)

• CALBRATION

• Demand/ Flows (DMRB procedure, GEH stat)

• Journey times (DMRB criteria)

+ Vehicle type proportions ( ± 1% )

Car, Van, HGV (rigid & artic), Bus, Coach

• Vehicle dynamics

• SIMULATIONS

• Harvest ALL vehicle trajectories (1Hz, 10

replications)

• >1 million vehicle kms for the ‘Base’ scenario6

MODELLING FRAMEWORKCoupled micro-scopic traffic & instantaneous emission model

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Vehicle

trajectory data

at 1Hz.

Dis-aggregate

emission data

TRAFFIC MICROSIMULATION

S-Paramics, Version 2011.1

Multiple simulations (x10)

VEHICLE EMISSION MODEL

Instantaneous emission

model PHEM 11.

RESULTS

Road section, time-of-day,

vehicle sub-category or an

individual vehicles’ trajectory

VEHICLE TYPE PROPORTIONS

% Car, Taxi, LGV, HGV Rigid &

Artic, Bus (scheduled), Coach.

VEHICLE SUB-CATEGORIES

% Euro, Fuel (Petrol/ Diesel),

EGR/ SCR, Weight etc.

DETAILED VEHICLE REGISTRATION

INFORMATION (LOCAL).

ANPR surveys 0700 -1900hrs.

VEHICLE DYNAMICSComparing observed and modelled vehicle dynamics

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OBSERVEDPassenger Car Tracking: GPS + Road speed (CAN)

MODELLEDTraffic microsimulations (Paramics) – Passenger car

Sample: AM +PM peak period

100 kms, 4 hours (stationary excluded)

Sample: one replication AM +PM peak

12, 000 kms, 600 hours (stationary

excluded)

INSTANTANEOUS EMISSION MODELPHEM version 11

Comprehensive power-instantaneous emission model for the EU fleet

Simulates fuel consumption (FC) and tail-pipe emissions of NOX, NO2,

CO, HCs, Particulate Mass (PM), Particle Number (PN)

Whole European vehicle fleet:

Euro 0 to Euro 6

Petrol, diesel and hybrid powertrains

Light and Heavy-duty vehicles etc.

Simulations:

Consider all driving resistances including GRADIENT

Gear shift model

Transient engine maps (with time correction functions)

Thermal behaviour of engine, catalyst, SCR etc.

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Emission ratiosFrom peak exhaust plume conc.

NO / CO2

Predict NO2 and NOX / CO2

CO / CO2

HC / CO2 &

PM (opacity measure)

Local measurements4-days surveys September 2011

> 10,000 ‘valid’ records

Camera(Number plate)

Vehicle Detector(Speed andAcceleration)

Source/Detector

Mirror Box

Source

Detector

Emissions Analyser(Common

Configurations)

Camera(Number plate)

Vehicle Detector(Speed andAcceleration)

Source/Detector

Mirror Box

Source

Detector

Emissions Analyser(Common

Configurations)

REMOTE SENSING VEHICLE EMISSIONSSurveying the vehicle fleet on the road

ESP RSD-4600 instrumentwww.esp-global.com

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EMISSION MODELLING VALIDATION (1)Comparison with Remote Sensing Emission Factors

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𝑅𝑆𝑀𝐴𝑁𝑈. =𝑁𝑂𝑋𝐶𝑂2 𝑅𝑆

×𝐶𝑂2𝑘𝑚

𝑀𝐴𝑁𝑈.

Euro class

NO

X (

gra

ms/k

m)

0.0

0.5

1.0

E0 E1 E2 E3 E4 E5 E6

Car_diesel

E0 E1 E2 E3 E4 E5 E6

Car_petrol

EMISSION MODELLING VALIDATION (2)Comparison with Remote Sensing Emission Factors

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𝑅𝑆𝑁𝐸𝑇𝑊𝑂𝑅𝐾𝑀𝑂𝐷𝐸𝐿 =𝑁𝑂𝑋𝐶𝑂2 𝑅𝑆

×𝐶𝑂2𝑘𝑚

𝑁𝐸𝑇𝑊𝑂𝑅𝐾 𝑀𝑂𝐷𝐸𝐿

Euro class

NO

X (

gra

ms/k

m)

0.0

0.5

1.0

E0 E1 E2 E3 E4 E5 E6

Car_diesel

E0 E1 E2 E3 E4 E5 E6

Car_petrol

𝑅𝑆𝑂𝐵𝑆𝐸𝑅𝑉𝐸𝐷 𝑇𝑅𝐴𝐽. =𝑁𝑂𝑋𝐶𝑂2 𝑅𝑆

×𝐶𝑂2𝑘𝑚

𝑂𝐵𝑆𝐸𝑅𝑉𝐸𝐷 𝑇𝑅𝐴𝐽.

CAR-petrol CAR-diesel VAN HGV COACH

NO

X (

%)

05

10

15

20

25

30

35

BUS

EMISSION CONTRIBUTIONSOxides of Nitrogen (NOX)

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CAR-petrol CAR-diesel VAN HGV COACH

NO

2 (

%)

01

02

03

04

05

06

0

BUS

EMISSION CONTRIBUTIONSNitrogen dioxide (NO2)

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A “VIRTUAL” YORK 2Coupled micro-scopic traffic & instantaneous emission model

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MAPPING VEHICLE EMISSIONSThe spatial variation in NOX – AM peak

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BOOTHAM

GILLYGATE

GRAPHING VEHICLE EMISSIONSThe spatial variation in NOX – AM peak

{©Copyright GoogleTM 2014}

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BUS

STOP

INFLUENCE TIME OF DAYBootham to Gillygate direction

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VEHICLE TYPE CONTRIBUTIONSBootham to Gillygate direction

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{©Copyright GoogleTM 2014}

BOOTHAM GILLYGATE (South East)NOX emissions: EFT v5.2c & PHEM11

AM Peak [08:00 09:00hrs]

0 100 200 300 400 500

0.0

0.5

1.0

1.5

2.0

Distance (metres)

NO

X (

gra

ms /

hr

/ m

)

BOOTHAM GILLYGATE

EVening [19:00 23:00hrs]

0 100 200 300 400 500

0.0

0.5

1.0

1.5

2.0

Distance (metres)

NO

X (

gra

ms /

hr

/ m

)

BOOTHAM GILLYGATE

BOOTHAM GILLYGATE (South East)NOX emissions: EFT v5.2c & PHEM11

SummaryMETHOD

Detailed, coupled traffic-vehicle emission simulations are now feasible

Emission Factors are in agreement with remote sensing

measurements

The PHEM (total) NOX emissions from Bootham and Gillygate

over a typical weekday are higher than those predicted by the UK

EFT 26%

The approach, moving towards a “virtual” representation of local

traffic networks and the local vehicle fleet:

naturally encapsulates events that influence emissions e.g. Bus stops

Complex traffic situations and interventions can be assessed:

Congestion

Demand management

Control strategies e.g. Smoothing flow, penetration new Driver Assist

Systems

Allows the distribution of emissions through urban streets and

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Conclusions

During periods of light traffic demand, NOX emissions are

concentrated around the intersection itself, with emissions

at mid-link locations where vehicles are typically ‘cruising’

at a low-level

In Peak periods with slow moving queues on links,

emissions are elevated in the vicinity of the intersection,

but also spread along the length of the links

? Does the uniform ‘line source’ assumption still hold for

local-scale vehicle emission assessments & micro-scale

scale dispersion modelling in street canyons

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Further work

MODEL VERIFICATION & VALIDATION:

Developing methods to quantify differences in vehicle

dynamics

e.g. variability in cruising speeds

Further PHEM validation

Light- and Heavy-duty chassis dyno measurements (London Drive

Cycle)

Evaluating the complete Traffic – Vehicle Emissions –

Dispersion Modelling chain, comparison to ambient

measurements.

APPLICATIONS:

Fleet renewal e.g. Low Emission Zone evaluation, Bus

replacement

Sustainable transport policies e.g. reducing the demand for

travel

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