Final Report Summary - OPTION (Optimizing Policies for Transport: accounting for Industrial Organisation in Network markets)
The study developed new approaches to explicitly incorporate strategic behaviour of large actors into transport economic multi-level network modelling, with the aim to develop models that adequately describe the impact of these actors’ behaviour on the formation of network equilibrium, and therewith also lend themselves for the design and evaluation of second-best policies in transport network markets in which large actors are active and the market’s industrial organization is therefore decisive for its functioning. The project focused on a number of specific markets: aviation, public transport, private roads, traffic safety and insurance companies, automated highways (“robot cars”), electric vehicles, taxi markets, traffic information, and tax competition by local governments.
The project confirms that indeed it is of great importance to take into account the strategic behaviour of large actors in transport network markets in evaluating the impacts of transport policies. These actors not only have a decisive impact on the eventual equilibrium on these markets, so that understanding of their behaviour is central to making reliable predictions. Their behaviour can also mitigate or aggravate distortions in the functioning of these markets. In such cases, conventional policy prescriptions, that ignore this behaviour of larger actors, are less than optimal and can even be counter-productive (welfare reducing).
The extent to which the strategic behaviour of large actors affects the efficiency of the markets, and therewith the extent to which conventional policy rules require adjustment, depends on many factors. These include: the number and sizes of actors, their distribution over the network, their identity (private vs public), the extent to which they serve the same consumers (e.g. complementary vs substitute products), the extent to which their consumers interact, the nature of consumer interaction (e.g. safety, static congestion, dynamic congestion), the degree of substitutability between actors’ services.
Specific to the modelling of congestion, details of the congestion technology (e.g. static vs dynamic modelling) can have huge impacts on the impacts of strategic interaction and on the policy prescriptions for the associated markets.
And finally, for the modelling of dynamic congestion, it was found that the representation of scheduling decision making can have important implications for the valuation of time and schedule delays, and therewith for the specification of optimal policies.
The project confirms that indeed it is of great importance to take into account the strategic behaviour of large actors in transport network markets in evaluating the impacts of transport policies. These actors not only have a decisive impact on the eventual equilibrium on these markets, so that understanding of their behaviour is central to making reliable predictions. Their behaviour can also mitigate or aggravate distortions in the functioning of these markets. In such cases, conventional policy prescriptions, that ignore this behaviour of larger actors, are less than optimal and can even be counter-productive (welfare reducing).
The extent to which the strategic behaviour of large actors affects the efficiency of the markets, and therewith the extent to which conventional policy rules require adjustment, depends on many factors. These include: the number and sizes of actors, their distribution over the network, their identity (private vs public), the extent to which they serve the same consumers (e.g. complementary vs substitute products), the extent to which their consumers interact, the nature of consumer interaction (e.g. safety, static congestion, dynamic congestion), the degree of substitutability between actors’ services.
Specific to the modelling of congestion, details of the congestion technology (e.g. static vs dynamic modelling) can have huge impacts on the impacts of strategic interaction and on the policy prescriptions for the associated markets.
And finally, for the modelling of dynamic congestion, it was found that the representation of scheduling decision making can have important implications for the valuation of time and schedule delays, and therewith for the specification of optimal policies.