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Emission Modeling

Road traffic congestion has become an everyday phenomenon in today’s cities all around the world. The reason is clear: at peak hours, the road network operates at full capacity. In this way, growing traffic demand cannot be satisfied, not even with traffic-responsive signal plans. The external impacts of traffic congestion come with a serious socio-economic cost: air pollution, increased travel times and fuel consumption, stress, as well as higher risk of accidents.


We tackle emission modelling with different approaches:

With these approaches, we investigated the impact of speed limit reduction on Budapest’s arterial roads using microsimulation.

The most important contribution of the study is that a thorough and in-depth transport engineering study is definitely needed before a possible speed limit reduction measure can be introduced, followed by professional and social discussion and then consensus. A city road network is always very complex. The introduction of speed limit reduction can be particularly beneficial on some roads and controversial on others. Although microscopic traffic simulation is a necessary tool to evaluate the effect of speed limit reduction, it is not sufficient. The modal shift and other externalizes induced by it require further discussion and the involvement of additional data sources and stakeholders.

The main conclusions of the simulation studies are as follows. It is not clear in which direction the emission changes because it depends on the current state of traffic (congested or not) and the composition of vehicles. However, in general, it can be said that the reduction from 50 to 30 ​km/h is essentially an increase in emissions, and the reduction from 70 to 50 ​km/h brings mixed results (rather a slight reduction in emissions).

Emission-based control

Since keeping emissions as low as possible is becoming increasingly important, ITS-based control algorithms (e.g., perimeter control, ramp metering, variable speed limits) shall consider this as an additional objective, often conflicting with the main objective of the control. Thus, multi-objective control approaches are desirable.

For example, we can control traffic flow on a highway to reduce emissions:

Without control
With control

We can perform dynamic re-routing of traffic to mitigate network emissions

When developing trajectory control for autonomous vehicles, emissions shall also be a control objective.

Read the related publications:

Mánuel Gressai, Balázs Varga, Tamás Tettamanti, István Varga: Investigating the impacts of urban speed limit reduction through microscopic traffic simulation. In: Communications in Transportation Research, vol. 1, pp. 100018, 2021, ISSN: 2772-4247.
Balázs Varga, Tamás Tettamanti, Balázs Kulcsár: Energy-aware predictive control for electrified bus networks. In: Applied Energy, vol. 252, pp. 113477, 2019, ISSN: 0306-2619.
T. Tettamanti, A. Csikós, I. Varga: Macroscopic modeling and control of emission in urban road traffic networks. In: Transport, vol. 30, no. 2, pp. 152, 2015.
Written by: BME Traffic Lab | Date: 07/02/2023 2:50 PM