THE ISSUE Workshop on Air Quality in Cities
M. Petrelli - Roma Tre University
February 2014
The evaluation of
road traffic emissions
1. Model for emissions estimation in large scale urban network• Urban network congestion• Large scale city (not single arterial)• with relatively low calibration & computational cost/time• taking into account different time slices (time variability)• taking into account queue phenomena
2. Evaluation of traffic management impacts from emissions point of view
• Traffic management such as arterial signal optimization (cycle, phases, offset), ramp metering, one-way system, reversible lanes, ITS solutions and so on……
• optimum for traffic (generalized cost/time) ≠ optimum for emissions
• Real time estimation
Which evaluation and why……….
State of the Art
Two main approaches:
• Microscopic (USA)
based on the evaluation of driving phases of a vehicle (acceleration, steady state, deceleration)
• Macroscopic (EU)based on computation of specific vehicle emission factors, average vehicles speed and distance travelled
1) Macroscopic model based on v, k, q (CORINAIR)• reference model for estimating emissions in
Europe[Lumbreras et al.; European Environment Agency]
• in congested network, usually macroscopic models underestimate emissions
[Shukla-Alam; Rakha-Ding; Rouphail et al.]
2) Microscopic model based on vi, a, d, delay (MOVES)• mainly useful for emission estimation in arterials
or single intersection[Stevanovic et al.]
• good results in arterial or single intersection optimization
[Midnet et al.; Coelho et al.; Rakha et al.]
Traffic model
(congestion)
Emission model
Dispersion model
Proposed approach
Estimation of pollutant emissions in a large area network with a suitable level of accuracy
Possible use of the model:
• Offline for planning
• Real Time for control
MICRO(approach)
MACRO(approach)
MESO(approach)
Mesoscopic:DTA (Dynamic Traffic Assignment)
Large area road network24 h analysis
Realistic emissions estimation
New Model for emission estimation
The idea is to divide each link in 3 different parts:• LA - vehicles are at free-flow speed• LB - vehicles are in queue• LC - vehicles are in acceleration phase
Post processor module:Model for queue assessment + Assessment of 3 different emission factors
• The model has been applied to the city of Brindisi (100K inhabitants)
• Traffic flows have been simulated from 5 am to 23 pm
• 884 links• 306 nodes• 14 signalized
intersection
Application in Brindisi network
Total daily CO emission at intersections
Application in Brindisi network
Level of congestion in the road network
Impact evaluation of different policies
VKT VHDAv.
SpeedCO NOx PM10
a - - - - - -
b -1% -5% 3% -2% -2% -2%
c -2% -13% 7% -15% -11% -11%
d 0% 0% -3% 0% 0% 0%
b+c -4% -19% 12% -14% -12% -11%
b+c+d -3% -17% 10% -13% -11% -10%
Model Layout
• Meso-simulation model (Dynameq) has been used to evaluate traffic congestion and related traffic flow parameters
• CORINAIR has been used to evaluate the specific vehicle emissions
• Dispersion model has to be developed to estimate air pollutants dispersion
Need of dispersion model and data for model validation