Linking ADMS with Microsimulation Models

Prof. Duncan Laxen

and

Dr Ben Marner

Outline of Presentation

• Will look at a study AQC carried out for Reigate and Banstead Borough Council to test options to address NO2 concentrations in a small AQMA at a road junction in the north of the borough

• The study used a microsimulation traffic model to provide emission factors taking into account vehicle acceleration

AQMA

AQMA is a single property in the north of the Borough

At the junction of two main roads Reigate Road and Fir Tree Road

Benefits of Microsimulation Traffic Models

• A conventional traffic model produces Link-average speeds and flows

• A microsimulation traffic model Can provide details of flows, speeds and

acceleration for individual lanes in great spatial detail

But resource intensive, so mainly applied to smaller sets of links and usually to peak hours

Traffic Models

Saturn-Type Model Microsimulation Model

Acceleration is a Key Benefit

Instantaneous Emission Factors (from David Carslaw)

Average Speed Emission Factors

NOx from Diesel Cars – remote sensing

20mph30mph

This Study

S-Paramics Microsimulation Traffic Model (run by SIAS)

+ AIRE Instantaneous Emission Model (maintained by SIAS

for Transport Scotland)

+ ADMS-Roads Dispersion Model(Annual mean NO2 from predicted annual mean road-NOx to Annual mean NO2 using Defra background maps and NOx to NO2 calculator)

Traffic and emissions models run for 3 x 24hr periods: typical weekday, Saturday and Sunday

Emissions were extracted for each 2m section of road network for each 5-minute period and for each for 1-hour period and then input to ADMS Roads

AQMA at Junction of 2 Roads

26 µg/m3

46 µg/m3

41 µg/m3

Annual Mean NO2 concentrations in 2012

Network broken down into 3,500 2m line sources

High degree of automation, taking the data from S-Paramics to AIRE to ADMS, and then collating the results

In ADMS, all emissions set to 1g/km/s, and then the AIRE data entered as .hfc files

Also modelled the same network using 1-hour link-average speeds for each 2m link but using EFT emissions for comparison

Modelled Roads

58 road lane sections modelled

S-Paramics:

•Traffic counts

•Traffic speeds

•Vehicle mix

AIRE:

•1-hour emission profiles

•5-minute emission profiles

AIRE vs EFT Comparison

Predicted Annual Mean Road-NOx Based on AIRE vs EFT (EFT used 2m link speeds)

Modelled 109 receptors

Much Greater Range in AIRE-Based Predictions than in EFT-Based Predictions

EFT

AIRE

Results on One Link (EFT vs AIRE)

Different Movements Compare Differently

Large Variation in Concentrations Along a Relatively Short Road-Section (depend on acceleration and idle time)

Modelled Concentrations

2007 Further Assessment (2010 concentrations)

This Study

Difference in Concentrations

Standard EFT assessment (redone) vs Microsimulation AIRE assessment

Red shows EFT>AIRE

Blue shows EFT<AIRE

Traffic Management Scenarios

Option 1 – Introduce a 20 mph speed limit

Option 2 – Remove southbound left-hand lane of A240 (N)

Option 3 – Extended green traffic light

Scenario Testing – Option 3

• Option 3 – extended green traffic light

Up to 0.2 µg/m3 reduction in AQMA

Up to 0.7 µg/m3 increase in locations with no relevant exposure

Change in Predicted Annual Mean NO2 (µg/m3) between Option 3 and Do Nothing

Scenario Testing – Option 2

Change in Predicted Annual Mean NO2 (µg/m3) between Option 2 and Do Nothing

• Option 2 – removal of the southeast-bound left-hand lane on junction approach

Up to 0.9 µg/m3 reduction in AQMA

Up to 1.0 µg/m3 increase in locations with no relevant exposure

Scenario Testing – Option 1

Change in Predicted Annual Mean NO2 (µg/m3) between Option 1 and Do Nothing

• Option 1 – 20 mph speed limit (reduced from 30 mph)

Up to 4.4 µg/m3 reduction in AQMA

Up to 13.1 µg/m3 increase in locations with no relevant exposure

Traffic Management Option 1

Base Option 1

Option 1 – Introduce a 20 mph speed limit

Predicted NO2 Concentrations (µg/m3) Predicted NO2 Concentrations (µg/m3)

Study Limitations

The local monitoring was insufficient to fully verify the findings of the microsimulation modelling

The findings depend on the accuracy of the microsimulation model especially how acceleration is presented (it is understood that there are differences between microsimulation models and that S-Paramics handles acceleration more accurately)

Other Observations

2m links potentially over-kill for emissions subdivision, but facilitated automated link with ADMS source-geometry

Automation meant that a larger study area could have been modelled in ADMS relatively easily once systems in place

Even with this small network, volume of emissions data made QA onerous (1/4 million different emissions values per scenario)

Key Observations

Use of average speed emission factors would predict an adverse effect of implementing a 20mph speed limit

Use of microsimulation model shows that 20mph limit can reduce emissions by reducing accelerations, i.e. smoothing flows. There is support for this view from a recent study in London

Similar Findings for 20mph

• Used floating car on routes in London with 20 and 30mph limits

• Used drive-cycle profiles with AIRE emission factors

• Found reduction in NOx emissions on 20mph roads

Concluded: “.. it would be incorrect to assume a 20mph speed restriction would be detrimental to local air quality …”

Wide-scale Rollout of 20mph Zones

Thanks to

• Dr Leon Hibbs at Reigate and Banstead BC

• SIAS Transport Planners

• Defra (for grant funding)

• and Dr Austin Cogan at AQC

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