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# A Handbook of Transport Economics

## Edited by André de Palma, Robin Lindsey, Emile Quinet and Roger Vickerman

Bringing together insights and perspectives from close to 70 of the world’s leading experts in the field, this timely Handbook provides an up-to-date guide to the most recent and state-of-the-art advances in transport economics. The comprehensive coverage includes topics such as the relationship between transport and the spatial economy, recent advances in travel demand analysis, the external costs of transport, investment appraisal, pricing, equity issues, competition and regulation, the role of public–private partnerships and the development of policy in local bus services, rail, air and maritime transport.
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# Chapter 9: Dynamic Traffic Modeling

## Extract

André de Palma and Mogens Fosgerau INTRODUCTION This chapter provides a brief introduction to dynamic congestion models, based on the Vickrey (1969) bottleneck model which has become the main workhorse model for economic analysis of situations involving congestion dynamics. The word dynamic can have several possible meanings. One possibility is that it relates to the way traffic systems evolve and users learn from day to day. In the context of the bottleneck model, it relates to intra-day timing, that is, to the interdependencies between traffic congestion at different times within a given day. We shall discuss dynamic approaches against the background of static models. Static models assume that congestion is constant over some given time period. A congestion law provides the travel time as a function of the entering flow. The time dimension is not explicitly involved: all quantities are computed as single figures specific to a time period. The basic static model considers a network comprising nodes and links. The nodes are centroids of zones, associating trip ends within a zone with a point that is a node in the network. Links connect the nodes. A cost function describes the cost of using each link. Congestion means that the cost increases as the number of users of the link increases. The demand is given by the origin–destination (O–D) matrix, indicating the number of trips between pairs of nodes. The solution involves the choice of route within the network for each O–D pair. Traffic volume on each...

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