O slideshow foi denunciado.
Seu SlideShare está sendo baixado. ×

James Tate - DMUG 2014

Próximos SlideShares
Sean Beevers DMUG 2014
Sean Beevers DMUG 2014
Carregando em…3

Confira estes a seguir

1 de 25 Anúncio

Mais Conteúdo rRelacionado

Diapositivos para si (20)

Quem viu também gostou (19)


Semelhante a James Tate - DMUG 2014 (20)

Mais de IES / IAQM (20)


Mais recentes (20)

James Tate - DMUG 2014

  1. 1. 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 2
  2. 2. MODELLING LARGE-SCALE NETWORKS Represented a Line Sources 3 City of York Source: http://ntis.trafficengland.com/map 7.02 am 16/09/2014 100 km
  3. 3. MODELLING CITY NETWORKS Short links 4 5 km 500 m
  4. 4. A “VIRTUAL” YORK Coupled micro-scopic traffic & instantaneous emission model 5
  5. 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 • 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) 6
  6. 6. 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 7 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.
  7. 7. VEHICLE DYNAMICS Comparing observed and modelled vehicle dynamics 8 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)
  8. 8. 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. 9
  9. 9. 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 10
  10. 10. EMISSION MODELLING VALIDATION (1) Comparison with Remote Sensing Emission Factors 11 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
  11. 11. EMISSION MODELLING VALIDATION (2) Comparison with Remote Sensing Emission Factors 12 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.
  12. 12. CAR-petrol CAR-diesel VAN HGV COACH NOX (%) 0 5 10 15 20 25 30 35 BUS EMISSION CONTRIBUTIONS Oxides of Nitrogen (NOX) 13
  13. 13. CAR-petrol CAR-diesel VAN HGV COACH NO2 (%) 0 10 20 30 40 50 60 BUS EMISSION CONTRIBUTIONS Nitrogen dioxide (NO2) 14
  14. 14. A “VIRTUAL” YORK 2 Coupled micro-scopic traffic & instantaneous emission model 15
  15. 15. MAPPING VEHICLE EMISSIONS The spatial variation in NOX – AM peak 16 BOOTHAM GILLYGATE
  16. 16. GRAPHING VEHICLE EMISSIONS The spatial variation in NOX – AM peak {©Copyright GoogleTM 2014} 17 BUS STOP
  17. 17. INFLUENCE TIME OF DAY Bootham to Gillygate direction 18
  18. 18. VEHICLE TYPE CONTRIBUTIONS Bootham to Gillygate direction 19 {©Copyright GoogleTM 2014}
  19. 19. 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
  20. 20. 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
  21. 21. 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 22  Allows the distribution of emissions through urban streets and
  22. 22. 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 23
  23. 23. 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 24  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.