Luis G. Willumsen, Juan de Dios Ortúzar
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Already the market leader in the field, Modelling Transport has become still more indispensible following a thorough and detailed update. Enhancements include two entirely new chapters on modelling for private sector projects and on activity-based modelling; a new section on dynamic assignment and micro-simulation; and sizeable updates to sections on disaggregate modelling and stated preference design and analysis. It also tackles topical issues such as valuation of externalities and the role of GPS in travel time surveys.
Providing unrivalled depth and breadth of coverage, each topic is approached as a modelling exercise with discussion of the roles of theory, data, model specification, estimation, validation and application. The authors present the state of the art and its practical application in a pedagogic manner, easily understandable to both students and practitioners.* Follows on from the highly successful third edition universally acknowledged as the leading text on transport modelling techniques and applications* Includes two new chapters on modelling for private sector projects and activity based modeling, and numerous updates to existing chapters* Incorporates treatment of recent issues and concerns like risk analysis and the dynamic interaction between land use and transport* Provides comprehensive and rigorous information and guidance, enabling readers to make practical use of every available technique* Relates the topics to new external factors and technologies such as global warming, valuation of externalities and global positioning systems (GPS).
i, and by H q (h) the set of households of type h containing persons of type q. With this we qp can write the trip productions with purpose p by person type q in zone i, Oi , as follows: Oiqp = ai (h)t p (h) h∈H q (h) (4.16) P1: TIX/XYZ JWST054-04 P2: ABC JWST054-Ortuzar February 24, 2011 13:7 Printer Name: Yet to Come Trip Generation Modelling 159 To verify how the model works it is possible to compare these modelled values with observed values from the calibration sample. Inevitable
(2.5) j=1 In certain cases a single index is not enough and two or more may be used. For example we could define the following six variables, T 11 , T 12 , T 21 , T 22 , T 31 , T 32 as Tij , i = 1, 2, 3, and j = 1, 2. With two-subscript variables we can have double summations or double products, as in: 3 2 3 Ti j = i=1 2.2.2 j=1 (Ti1 + Ti2 ) = T11 + T12 + T21 + T22 + T31 + T32 (2.6) i=1 Functions and Graphs We have already referred to variables as being related by equations and
the rest of the book. Firstly, we will provide a brief introduction to statistical sampling theory, which will complement in part the elementary concepts discussed in section 2.5. Interested readers are advised that there is a complete book on the subject (Stopher and Meyburg 1979) which may be consulted for more details. In section 3.2 we will discuss the nature and importance of errors which can arise both during model estimation and when forecasting with the aid of models; the interesting
Department of Transport Engineering and Logistics Pontificia Universidad Católica de Chile Santiago Chile Luis G. Willumsen Luis Willumsen Consultancy and University College London London UK A John Wiley and Sons, Ltd., Publication P1: TIX/SPH JWST054-FM P2: TIX JWST054-Ortuzar February 22, 2011 13:7 Printer Name: Yet to Come This edition published 2011 C 2011 John Wiley & Sons, Ltd Previous editions published 1990, 1994, 2001 C John Wiley & Sons, Ltd Registered office John Wiley &
considered. Opportunities for re-routeing, changes in destination and so on, must be allowed for; we would like to model their effects as part of the study area itself. The region external to the study area is normally divided into a number of external zones. In some cases it might be enough to consider each external zone to represent ‘the rest of the world’ in a particular direction; the boundaries of these different slices of the rest of the world could represent the natural catchment areas of