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 v i, 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) MICRO (approach) MACRO (approach) MACRO (approach) MESO (approach) MESO (approach) Mesoscopic: DTA (Dynamic Traffic Assignment) Large area road network 24 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

Emissions comparison Low congestion in the network – very similar emission values

Emissions comparison Low congestion in one arteria – large difference in emission values

Emissions comparison Low congestion in the network – very similar emission values

Impact evaluation of different policies VKTVHD Av. Speed CONOxPM10 a b-1%-5%3%-2% c -13%7%-15%-11% d0% -3%0% b+c-4%-19%12%-14%-12%-11% b+c+d-3%-17%10%-13%-11%-10%

Application in Eur Rome network

High congestion in the network – large increase in emission values

Application in Eur Rome network High congestion in the network – large increase in emission values

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