Pricing local emission exposure of road traffic: An agent-based approach
Authored by Benjamin Kickhoefer, Julia Kern
Date Published: 2015
DOI: 10.1016/j.trd.2015.04.019
Sponsors:
German Research Foundation (Deutsche Forschungsgemeinschaft, DFG)
Platforms:
MATSim
Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
Model code not found
Abstract
This paper proposes a new approach to iteratively calculate local air
pollution exposure tolls in large-scale urban settings by taking the
exposure times and locations of individuals into consideration. It
explicitly avoids detailed air pollution concentration calculations and
is therefore characterized by little data requirements, reasonable
computation times for iterative calculations, and open-source
compatibility. In a first step, the paper shows how to derive
time-dependent vehicle-specific exposure tolls in an agent-based model.
It closes the circle from the polluting entity, to the receiving entity, to damage costs, to tolls, and back to the behavioral change of the
polluting entity. In a second step, the approach is applied to a
large-scale real-world scenario of the Munich metropolitan area in
Germany. Changes in emission levels, exposure costs, and user benefits
are calculated. These figures are compared to a flat emission toll, and
to a regulatory measure (a speed reduction in the inner city), respectively. The results indicate that the flat emission toll reduces
overall emissions more significantly than the exposure toll, but its
exposure cost reductions are rather small. For the exposure toll, overall emissions increase for freight traffic which implies a potential
conflict between pricing schemes to optimize local emission exposure and
others to abate climate change. Regarding the mitigation of exposure
costs caused by urban travelers, the regulatory measure is found to be
an effective strategy, but it implies losses in user benefits. (C) 2015
Elsevier Ltd. All rights reserved.
Tags
Congestion
Model
Environments
Costs
Demand
Air-pollution