James Tate - DMUG 2014
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Transcript of James Tate - DMUG 2014
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
3
City of York
Source: http://ntis.trafficengland.com/map 7.02 am
16/09/2014
100 km
MODELLING CITY NETWORKSShort links
4
5 km 500 m
A “VIRTUAL” YORKCoupled micro-scopic traffic & instantaneous emission model
5
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
11
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
12
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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