After developing the framework, the book explores the formalization of spatial interaction models, especially the work of Alan Wilson (chapter 3), as well as the emergence of choice models in the 1960s, through development and generalization of the multinomial logit in its many, many forms (chapters 4 and 5), and the unification of the two. I have experience working as a travel demand modeler, so while it was familiar territory, it was also clarifying in many ways. Reading the book, I understood the linkages far more deeply than before. One of the merits of the book is the hiding of most of the mathematics in the end notes. (Note 1) The book also provides a very detailed history of developments in the United Kingdom and some discussion of developments in other European countries, in addition to the history from the United States’ perspective, providing a more global perspective on the issue. I was not aware the East Germans had developed their own modeling methods, and remain curious about practices elsewhere in the Eastern Bloc.

The book turns to the present trend in microscopic models, including activity-based models (chapter 6), which model tours of individuals rather than trips of aggregate flows, and the Transims project (chapter 8). From a methodological perspective, this is clearly an improvement, and the direction most applied models seem to be moving toward. By recognizing time-space constraints as formulated by Ha ̈gerstrand, the models become more realistic. If it were presented clearly to the public, it would probably be more intuitive and comprehensible than traditional aggregate models. This has not improved their predictive value, however, and Transims was, as the book describes, a diversion of resources with little to show for it.

After dealing with the first three steps of the travel demand model, the book then discusses network equilibrium and solution methods for route assignment or route choice (chapter 7). These evolved independently from the work of Beckmann, McGuire, and Winsten in the 1950s (coinci- dentally also in Chicago, though not interacting with Carroll’s group), were operationalized with steadily improving algorithms in the 1970s, and were mainstreamed into practice by the late 1980s. While equilibrium for route choice is now standard (whether or not traffic is in equilibrium, and whether or not route choice depends solely on travel cost), there is no standard “feedback” method to ensure that the travel time inputs to the travel demand process are consistent with the travel time outputs of route choice procedures, one of the many problems with actual transportation modeling practice, and a problem that as the authors note has received insufficient attention in the literature (the authors notably excepted).

Travel demand models have their uses. Long-term forecasting—their putative rationale—is probably the weakest. Yet, this entire enterprise was nominally driven by the desire to forecast travel demand, not simply understand or model it. It is indeed in the title of the book. On that score, the field has, in my opinion, failed miserably. Boyce and Williams note that accuracy is poor, and not improving over time. While one can understand the naivety of early modelers in the 1950s and 1960s (who undoubtedly well understood the limitations), by the 1970s (and certainly by the 2010s), the futility of accurate forecasting should have become apparent to those both within and outside the field. The forecasts are driven by the assumption that behavior in the future, given identical characteristics, will be the same as today. Culture is outside the scope of models, with good reason, but if culture matters, or anything else that is also outside the model’s data, there will be misses. Modelers may claim data issues, or poor inputs, and those certainly matter, yet as the authors note, estimation of models across time is never done in practice. There are always reasons— incompatibility of surveys, time, budget, and so on. The excuse for using cross-sectional analysis in the 1950s was that there was no time series; only one survey (at most) had ever been done in any metropolitan area. The excuse today is what?