Vehicle Emission Models and Traffic Simulators: A Review

Abstract

Accurate estimations and assessments of vehicle emissions can support decision-making processes. Current emission estimation tools involve several calculation methods that provide estimates of the exhaust components that result from driving on urban arterial roads. This is an important consideration, as the emissions generated have a direct impact on the health of pedestrians near the roads. In recent years, there has been an increase in the use of emission models, especially in combination with traffic simulator models. This is because it is very difficult to obtain an actual measurement of road emissions for all vehicles travelling along the analysed road section. This paper concerns a review of selected traffic simulations and the estimation of exhaust gas components models. The models presented have been aggregated into a group with respect to their scale of accuracy as micro, meso, and macro. This paper also presents an overview of selected works that combine both traffic and emission models. The presented literature review also emphasises the proper calibration process of simulation models as the most important factor in obtaining accurate estimates. This work also contains information and recommendations on modelling that may be helpful in selecting appropriate emission estimation tools to support decision-making processes for, e.g., road managers.

Evaluating Traffic-Calming-Based Urban Road Design Solutions Featuring Cooperative Driving Technologies in Energy Efficiency Transition for Smart Cities

“…It reduced delays and generated the least amount of polluting emissions compared to the current situation. It is worth noting that the driving behavior of road users, influenced by both internal and external factors affecting traffic flows, significantly impacts vehicle emissions even within a simulation environment [61,62]. Pollutant emissions tend to increase with higher average vehicle speeds and during sudden acceleration or deceleration maneuvers [63].…”

An Evaluation and Prioritization Framework for Pilot First- and Last-Mile Ridesharing Services

“…The estimation of emissions is usually based on models, which have been advanced significantly due to different vehicle characteristics, road and environmental conditions, the diversity of pollutants and fuel types [61]. For example, ignoring the effect of acceleration and deceleration of the vehicles could pose challenges to the precision of outcomes, particularly in urban traffic zones featuring signalized intersections [62].…”

“…[63], a significant parameter is the scale at which emissions are calculated, which determines the required data. Emission models are divided according to their precision scale into macro (regional, national area), meso (local area) and micro (areas of a dedicated part of a city, intersection road sections) [61]. For instance, the COPERT and MOVES [61] models calculate emissions at the macro scale using the vehicle class, number of vehicles, weather conditions, load, average speed, distance travelled and so on.…”

“…Emission models are divided according to their precision scale into macro (regional, national area), meso (local area) and micro (areas of a dedicated part of a city, intersection road sections) [61]. For instance, the COPERT and MOVES [61] models calculate emissions at the macro scale using the vehicle class, number of vehicles, weather conditions, load, average speed, distance travelled and so on. On the other hand, MODEM and EMPA [61], which are microscopic models, use the speed and the acceleration profiles of vehicles as input.…”

“…For instance, the COPERT and MOVES [61] models calculate emissions at the macro scale using the vehicle class, number of vehicles, weather conditions, load, average speed, distance travelled and so on. On the other hand, MODEM and EMPA [61], which are microscopic models, use the speed and the acceleration profiles of vehicles as input. Several studies have focused on different factors of traffic emissions [64], such as land use [65], socio-economic parameters [66], urbanization [67] and transportation structure [68].…”