At the 2014 annual Dispersion Modellers user group meeting guest speaker James Tate spoke the topic: 'Making better use of microsimulation models for estimating vehicle emissions'

2. 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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3. MODELLING LARGE-SCALE NETWORKS
Represented a Line Sources
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City of York
Source: http://ntis.trafficengland.com/map 7.02 am
16/09/2014
100 km

4. MODELLING CITY NETWORKS
Short links
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5 km 500 m

5. A “VIRTUAL” YORK
Coupled micro-scopic traffic & instantaneous emission model
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6. 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
• 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’ scenario
1 The York 2011 S-Paramics network created by David Preater (Halcrow,
2011)
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7. MODELLING FRAMEWORK
Coupled micro-scopic traffic & instantaneous emission model
TRAFFIC MICROSIMULATION
S-Paramics, Version 2011.1
Multiple simulations (x10)
Vehicle
trajectory data
at 1Hz.
VEHICLE EMISSION MODEL
Instantaneous emission
model PHEM 11.
Dis-aggregate
emission data
RESULTS
Road section, time-of-day,
vehicle sub-category or an
individual vehicles’ trajectory
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VEHICLE TYPE PROPORTIONS
% Car, Taxi, LGV, HGV Rigid &
Artic, Bus (scheduled), Coach.
DETAILED VEHICLE REGISTRATION
INFORMATION (LOCAL).
ANPR surveys 0700 -1900hrs.
VEHICLE SUB-CATEGORIES
% Euro, Fuel (Petrol/ Diesel),
EGR/ SCR, Weight etc.

8. VEHICLE DYNAMICS
Comparing observed and modelled vehicle dynamics
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OBSERVED
Passenger Car Tracking: GPS + Road speed (CAN)
MODELLED
Traffic 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)

9. INSTANTANEOUS EMISSION MODEL
PHEM 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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10. REMOTE SENSING VEHICLE EMISSIONS
Surveying the vehicle fleet on the road
Emission ratios
From peak exhaust plume conc.
 NO / CO2
 Predict NO2 and NOX / CO2
 CO / CO2
 HC / CO2 &
 PM (opacity measure)
Local measurements
4-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)
ESP RSD-4600 instrument
www.esp-global.com
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11. EMISSION MODELLING VALIDATION (1)
Comparison with Remote Sensing Emission Factors
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Car_diesel
푅푆푀퐴푁푈. =
푁푂푋
퐶푂2 푅푆
×
퐶푂2
푘푚
푀퐴푁푈.
Euro class
NO X (grams/km)
1.0
0.5
0.0
E0 E1 E2 E3 E4 E5 E6
E0 E1 E2 E3 E4 E5 E6
Car_petrol

12. EMISSION MODELLING VALIDATION (2)
Comparison with Remote Sensing Emission Factors
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Car_diesel
푅푆푁퐸푇푊푂푅퐾 푀푂퐷퐸퐿 =
푁푂푋
퐶푂2 푅푆
×
퐶푂2
푘푚
푁퐸푇푊푂푅퐾 푀푂퐷퐸퐿
Euro class
NO X (grams/km)
1.0
0.5
0.0
E0 E1 E2 E3 E4 E5 E6
E0 E1 E2 E3 E4 E5 E6
Car_petrol
푅푆푂퐵푆퐸푅푉퐸퐷 푇푅퐴퐽. =
푁푂푋
퐶푂2 푅푆
×
퐶푂2
푘푚
푂퐵푆퐸푅푉퐸퐷 푇푅퐴퐽.

13. CAR-petrol CAR-diesel VAN HGV COACH
NOX (%)
0 5 10 15 20 25 30 35
BUS
EMISSION CONTRIBUTIONS
Oxides of Nitrogen (NOX)
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14. CAR-petrol CAR-diesel VAN HGV COACH
NO2 (%)
0 10 20 30 40 50 60
BUS
EMISSION CONTRIBUTIONS
Nitrogen dioxide (NO2)
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15. A “VIRTUAL” YORK 2
Coupled micro-scopic traffic & instantaneous emission model
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16. MAPPING VEHICLE EMISSIONS
The spatial variation in NOX – AM peak
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BOOTHAM
GILLYGATE

17. GRAPHING VEHICLE EMISSIONS
The spatial variation in NOX – AM peak
{©Copyright GoogleTM 2014}
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BUS
STOP

18. INFLUENCE TIME OF DAY
Bootham to Gillygate direction
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19. VEHICLE TYPE CONTRIBUTIONS
Bootham to Gillygate direction
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{©Copyright GoogleTM 2014}

20. 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 (grams / hr / m)
BOOTHAM   GILLYGATE

21. BOOTHAM  GILLYGATE (South  East)
NOX emissions: EFT v5.2c & PHEM11
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 (grams / hr / m)
BOOTHAM   GILLYGATE

22. Summary
METHOD
 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
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 Allows the distribution of emissions through urban streets and

23. 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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24. 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
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 Sustainable transport policies e.g. reducing the demand for