Transportation and Network Analysis: Current Trends: Miscellanea in honor of Michael Florian (Applied Optimization, 63)
Language: English
Published by Springer, 2002
- Hardcover
- New

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- Title
- Transportation and Network Analysis: Current Trends: Miscellanea in honor of Michael Florian (Applied Optimization, 63)
- Publisher
- Springer
- Publication year
- 2002
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 1402004885
- ISBN 13
- 9781402004889
MICHEL GENDREAU AND PATRICE MARCOTTE As an academic, Michael Florian has always stood at the forefront of transportation research. This is reflected in the miscellaneous contributions that make the chapters of this book, which are related in some way or another to Michael's interests in both the theoretical and practical aspects of his field. These interests span the areas of Traffic Assignment, Network Equilibrium, Shortest Paths, Railroad problems, De mand models, Variational Inequalities, Intelligent Transportation Systems, etc. The contributions are briefly outlined below. BASSANINI, LA BELLA AND NASTASI determine a track pricing policy for railroad companies through the solution of a generalized Nash game. BEN-AKIVA, BIER LAIRE, KOUTSOPOULOS AND MISHALANI discuss simulation-based estimators of the interactions between supply and demand within a real-time transportation system. BOYCE, BALASUBRAMANIAM AND TIAN analyze the impact of marginal cost pricing on urban traffic in the Chicago region. BROTCORNE, DE WOLF, GENDREAU AND LABBE present a discrete model of dynamic traffic assignment where flow departure is endogenous and the First-In-First-Out condition is strictly enforced. CASCETTA AND IMP ROTA give a rigorous treatment of the problem of estimating travel demand from observed data, both in the static and dynamic cases. CRAINIC, DUFOUR, FLo RIAN AND LARIN show how to obtain path information that is consistent with the link information provided by a nonlinear multimodal model. ERLANDER derives the logit model from an efficiency principle rather than from the classical random utility approach.
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