This paper considers the use of ‘long-run cost functions’ for congested networks in solving second-best network problems, in which capacity and tolls are instruments. We derive analytical results both for general cost and demand functions and for specific functional forms, namely Bureau of...

This paper considers the second-best problem where not all links of a congested transportation network can be tolled. The paper builds on earlier work, in which the second-best tax rule for this problem was derived for general static networks, so that the solution presented is valid for any...

Using game theory we investigate a new approach to formulate and solve optimal tolls with a focus on different policy objectives of the road authority. The aim is to gain more insight into determining optimal tolls as well as into the behavior of users after tolls have been imposed on the...

This paper presents the results of a large stated choice experiment among Dutch commuters facing congestion. The experiment consisted of a fractional factorial design with 15 different attributes, three alternatives were car specific and the other was always public transport. Various model...

In this paper, the impact of endogenous information provision to drivers in road transport is investigated. A static economic equilibrium model is used, which allows potential road users to buy information on the prevailing (stochastic) traffic situation. It takes for granted that an indiviual...

This paper explores interrelations between pricing, capacity choice, and financing in transportation networks. We build on the Mohring-Harwitz result on self-financing of optimally designed and priced roads and investigate it in a network environment under various types of second-best...

We explore the properties of various types of public and private pricing on a congested road network, with heterogeneous users and allowing for elastic demand. Heterogeneity is represented by a continuum of values of time. The network allows us to model certain features of real-world...