Post on 11-Jul-2015
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 ofYork
Source: http://ntis.trafficengland.com/map 10.55 am 02/12/2014
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 TheYork 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 scenario
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MODELLING FRAMEWORKCoupled micro-scopic traffic & instantaneous emission model
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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 period100 kms, 4 hours (stationary excluded)
Sample: one replication AM +PM peak12, 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 / CO2Predict 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 (2)Comparison with Remote Sensing Emission Factors
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= Euro class
NO
X(g
ram
s/km
)
0.0
0.5
1.0
E0 E1 E2 E3 E4 E5 E6
Car_diesel
E0 E1 E2 E3 E4 E5 E6
Car_petrol
. = .
. = .
CAR-petrol CAR-diesel VAN HGV COACH
NO
X(%
)
05
1015
2025
3035
BUS
EMISSION CONTRIBUTIONSOxides of Nitrogen (NOX)
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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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GRAPHING VEHICLE EMISSIONSThe spatial variation in NOX AM peak
{Copyright GoogleTM 2014}
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BUSSTOP
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(g
ram
s/h
r/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(g
ram
s/h
r/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 andintersections to be mapped
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Conclusions
During periods of light traffic demand, NOX emissions areconcentrated around the intersection itself, with emissions atmid-link locations where vehicles are typically cruising at a low-level
In Peak periods with slow moving queues on links, emissions areelevated in the vicinity of the intersection, but also spread alongthe length of the links
? Does the uniform line source assumption still hold for local-scale vehicle emission assessments & micro-scale dispersionmodelling in street canyons
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Further workMODELVERIFICATION &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 DispersionModelling 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 Motorway / Highway environment
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