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'
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
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
Notas do Editor
This is the structure of my talk.
I am going to start by giving some background to some challenges modelling road transport for City networks, and coordinates well with the last talk in this sessions by ……
I am then going to present a detailed modelling that moves towards being a “virtual” representation of our network and interactions of vehicles and the emissions they produce.
Before showing the results – visualisations of emissions in urban streets, before drawing some with you what work is in progress.
When modelling at a National or regional level, with links at least 1 km in length, it is reasonable assumption to estimate emissions over links and represent as line sources, as variations in driver behaviour are averaged, so their emissions can be reasonably predicted using average speed relationships for example.
This is a screen grab of the from the UK motorway network monitoring system, visualising the speeds across the network at 7am one morning this week. This “live” and historic data is now readily available and accessible for traffic studies.
I’ve annotated the City of York to the North of this map, a historic City of approximately 300,000 inhabitants which is the Case study city for this research.
The challenge when modelling City networks is that transport models such as this representation of the City of York network, include most even minor roads, so links become quite short, less than 100m in length, especially close to the City centre. With some links controlled by traffic signals for example, with different strategies, driving conditions can be highly variable, average speeds very low, making uncertainties applying average speed emission models for example high.
Another approach is to use traffic microsimulations models to model traffic movements, which can now be done over quite large networks, such as this model of most of the main roads and arterials inside the City of York outer ring road. This model was developed as part of a Low Emission Zone design and evaluation project. The network covers an area of approximately 20 km2.
You can see that the model simulates the movement and interaction of vehicles as they progress through the well specified network of junctions, you can see Buses for example stopping at bus stops as they service set routes. For this presentation we are going to later focus on some results fromsee key links. But first I’m going to describe how these simulations are specified and importantly calibrated.
The traffic simulations that included were setup using the best available information, including traffic flows, journey times from a database now routinely established in the UK from a network of tracked vehicles.
We also collected number plates from 10 key sites across the City on one day. These number plates were cross referenced with the still, thankfully United Kingdom vehicle registration database, so we can specify the vehicle proportions and fleet composition accurately. Linked models were created of Peak and Inter periods as usual, so the influence of traffic congestion could for example be assessed properly. Other lighter demand periods were not calibrated, instead the traffic demand was factored down. Importantly Bus services were adjusted between time periods, with no Buses for example operating in the night-time period.
When modelling vehicle emissions second-by-second, accounting vehicle accelerations, it is critical that the vehicle dynamics in terms of the distribution of speeds and accelerations reflect reality.
Here’s admittedly what is a qualitative analysis of a sample of tracked vehicle speeds across the network in the AM + PM peak periods, compared with the modelled ALL passenger car trajectories from a replication.