Modelling a tradable transport permit scheme using system dynamics

Introduction: In this paper we consider system dynamics (SD) modelling of tradable transport permit schemes (TTPS). TTPS are complex and uncertain, having not yet been implemented beyond experimental trials anywhere in the world, yet have for some time been suggested as an efficient and equitable policy response to reducing car-based travel and its associated externalities.

Method: Although there is a large number of econometric models that explore the potential of TTPS, there have been none published (to our knowledge) that have used the SD approach. SD is an appropriate method for developing our understanding of TTPS efficiency and equity as it captures feedbacks and tracks the influence of the TTPS over a period of time. Thus, we have developed an SD model of TTPS, drawing from the literature that already exists. It is a micro-level model that considers heterogeneity within a population on a daily basis over a matter of months. We carry out sensitivity testing of price sensitivity, initial permit price, daily preference and permit allocation and develop combined scenarios of these attributes.

Results: Our modelling results suggest a sensitivity of TTPS to heterogeneity across the population, which will influence the final equilibrium price. Trading encourages a redistribution of wealth across the population but may also mean that some travel needs are not met, which may require some allocation according to need or a certain portion of protected permits. The inclusion of non-car users within the allocation further encourages trading behaviours and allows for compensation for externalities of car use.

Discussion: Through an analysis of outputs we are able to bring new insights into the governance frameworks required for a successful TTPS. In particular we have shown the importance of allocation considerations in the design of a TTPS when issues of fairness across the population are considered. Although there is no data of existing schemes available for us to calibrate our models, we compare the trends with data from an in-the-field TTPS experiment.