The theory of road pricing developed for single links suggests time andlocation varying charges equal to the marginal congestion cost at the efficientlevel of traffic. The second-best network counterpart is derived, but would beinfeasible to implement. Cordon tolls are feasible, and their...

This paper estimates the marginal accident externality of driving in Central London by exploiting variation in traffic flow induced by the London Congestion Charge Zone using an instrumental variable approach. The charge attributed to a 9.4% reduction in traffic flow, which resulted in a less...

We study road supply by competing firms between a single origin and destination. In previous studies, firms simultaneously set their tolls and capacities while taking the actions of the others as given in a Nash fashion. Then, under some widely used technical assumptions, firms set a...

One of the main unanswered questions in the field of urban economics is to which extent subsidies to public transit are justified. We examine one of the main benefits of public transit, a reduction in car congestion externalities, the so-called congestion relief benefit, using quasi-natural...

Ride-hailing applications create new challenges for governments providing transit services, but also create new opportunities to raise tax revenue. To shed light on the effect of taxing or subsidizing ride-hailing applications, we extend a pseudo-monocentric city model to include multiple...

This paper studies the second-best problem where not all links of acongested transportation network can be tolled. The second-best taxrule for this problem is derived for general static networks, so thatthe solution presented is valid for any graph of the network, and forany set of tolling...

We explore the properties of various types of public and private pricing on acongested road network with heterogeneous users and allowing for elasticdemand. Heterogeneity is represented by a continuum of values of time. Thenetwork consists of both serial and parallel links, which allows us to...