TY - GEN
T1 - From aggregated traffic models to emissions quantification
T2 - 3rd International Conference on Transport Infrastructure and Systems, TIS ROMA 2022
AU - Marques, Jorge
AU - Batista, S. F.A.
AU - Menendez, Monica
AU - Macedo, Eloisa
AU - Coelho, Margarida C.
N1 - Publisher Copyright:
© 2023 The Authors. Published by ELSEVIER B.V.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - Traffic models based on the Macroscopic Fundamental Diagram have great potential for future applications of large-scale estimation and monitoring of exhaust emissions. These traffic models offer an aggregated representation of the traffic dynamics in the network, considering mean spatial speeds and average travel distances per zone in the city, which are then used to determine the exhaust emissions using a macroscopic emission model. This aggregated estimation of the travel emissions has an inherent bias when compared to a benchmark scenario consisting of a microscopic estimation of the emissions at the network level. This paper analyzes this discrepancy qualitatively and quantitatively. As our testbed, we consider the traffic data of the 1st of November collected by the project pNEUMA in downtown Athens, Greece. The benchmark scenario consists of the estimation of exhaust emissions using the Vehicle Specific Power model. For the aggregated estimation of travel emissions, we use the aggregated traffic dynamics based on the Macroscopic Fundamental Diagram dynamics and COPERT V emission model. Our results show that the aggregated methodology underestimates CO2 emissions by 30% and NOx emissions by 32%, in agreement with the existing literature. Despite the observed discrepancies between the estimated emissions, the aggregated traffic dynamics approach based on the Macroscopic Fundamental Diagram is computationally lighter than a microscopic one, and therefore more appropriate for large-scale applications.
AB - Traffic models based on the Macroscopic Fundamental Diagram have great potential for future applications of large-scale estimation and monitoring of exhaust emissions. These traffic models offer an aggregated representation of the traffic dynamics in the network, considering mean spatial speeds and average travel distances per zone in the city, which are then used to determine the exhaust emissions using a macroscopic emission model. This aggregated estimation of the travel emissions has an inherent bias when compared to a benchmark scenario consisting of a microscopic estimation of the emissions at the network level. This paper analyzes this discrepancy qualitatively and quantitatively. As our testbed, we consider the traffic data of the 1st of November collected by the project pNEUMA in downtown Athens, Greece. The benchmark scenario consists of the estimation of exhaust emissions using the Vehicle Specific Power model. For the aggregated estimation of travel emissions, we use the aggregated traffic dynamics based on the Macroscopic Fundamental Diagram dynamics and COPERT V emission model. Our results show that the aggregated methodology underestimates CO2 emissions by 30% and NOx emissions by 32%, in agreement with the existing literature. Despite the observed discrepancies between the estimated emissions, the aggregated traffic dynamics approach based on the Macroscopic Fundamental Diagram is computationally lighter than a microscopic one, and therefore more appropriate for large-scale applications.
KW - COPERT V emission model
KW - Vehicle Specific Power model
KW - aggregated traffic dynamics
KW - exhaust emissions
KW - network modelling
UR - http://www.scopus.com/inward/record.url?scp=85152030933&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85152030933&partnerID=8YFLogxK
U2 - 10.1016/j.trpro.2023.02.209
DO - 10.1016/j.trpro.2023.02.209
M3 - Conference contribution
AN - SCOPUS:85152030933
T3 - Transportation Research Procedia
SP - 568
EP - 575
BT - AIIT 3rd International Conference on Transport Infrastructure and Systems,TIS ROMA 2022 - Conference Proceedings
A2 - Cantisani, Giuseppe
A2 - Le Pira, Michela
A2 - Zampino, Stefano
PB - Elsevier B.V.
Y2 - 15 September 2022 through 16 September 2022
ER -