Travel-Demand Models
4.1 Introduction
As stated in Chap. 1, travel demand derives from the need to carry out activities in multiple locations. Thus, the level and characteristics of travel demand are influ- enced by the activity system and the transportation opportunities in the area.
In order to analyze and design transportation systems, it is necessary to estimate the existing demand and to predict the changes in it that will result from the projects being studied and/or from changes in external factors. Mathematical demand models can be used for all these purposes.
Atravel-demand modelcan be defined as a mathematical relationship between travel-demand flows and their characteristics on the one hand, and given activity and transportation supply systems and their characteristics.
A demand flow is an aggregation of individual trips, and each trip is the result of multiple choices made by the transportation system users, that is, an individual traveler in the case of passenger transportation or an operator (manufacturer, ship- per, and carrier) for freight transportation. For a traveler, these choices range from long-term decisions, such as residence and employment location and vehicle own- ership, to shorter-term decisions such as trip frequency, timing, destination, mode, and path. In freight transportation, long-term decisions influencing transportation demand include the location of production plants and purchasing/selling markets, ownership of a fleet of freight vehicles, storage facilities, and the like. Short-term decisions include such factors as shipment frequency, choice of mode, intermodal operator, and path. The choices underlying a journey are made with respect to differ- entchoice dimensions; these are defined by a set of available alternatives and by the values of their relevant attributes. For example, the mode choice dimension is de- fined by the alternative transportation modes available for a given origin–destination pair together with their attributes. In a given trip, the user may also make choices involving other dimensions, such as path and destination.
A large number of mathematical models have been developed to forecast travel demand1; the different models are based on different assumptions and have differ- ent specifications. Before describing some of these model families in detail, some classification criteria are introduced (see Fig.4.1).
The first classification factor is related to the type of choice (i.e., choice dimen- sion) that is implicitly or explicitly represented by the model. Decisions in some
1For now the discussion is in terms of passenger travel demand, even though many of the concepts introduced can be extended to freight transportation demand models. Section4.7deals specifically with freight models.
E. Cascetta,Transportation Systems Analysis, Springer Optimization and Its Applications 29,
DOI10.1007/978-0-387-75857-2_4, © Springer Science+Business Media, LLC 2009
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170 4 Travel-Demand Models TYPE OF CHOICE Mobility or context models
Travel models
SEQUENCE OF CHOICES Trip-based demand models Trip chaining models Activity-based models LEVEL OF DETAIL Disaggregate models
Aggregate models BASIC ASSUMPTIONS Behavioral models
Descriptive models Fig. 4.1 Classification of travel-demand models
choice dimensions influence individual trips indirectly, by defining the trip context or conditions. Decisions about residence and workplace locations, possession of a driver’s license, and the number of cars owned by the household are examples of this type of dimension. Residence and workplace locations determine the origin and destination of work trips, having a driver’s license makes the car available as a trans- portation mode, and so on. These choice dimensions and the models that represent them are known asmobility choicesandmodels.Usually, mobility choices are rela- tively stable over time because there is a high cost associated with changing them;
they can be assumed invariant in the short term.
Travel choicesandmodelsrefer to the dimensions that characterize journeys (se- quences of trips) and/or the individual trips that comprise journeys. Decisions about frequency, destination, transportation mode, and path are examples of this type of choice dimension.
The second classification factor relates to the approach taken for modeling travel demand, that is, for predicting the outcome of the travel choice decisions and rep- resenting the mutual effects of the different decisions on each other.Trip-based travel-demand modelsimplicitly assume that the choices relating to each origin–
destination trip are made independently of the choices for other trips within the same and other journeys. This approximation is made to simplify the analysis, and is reasonable when most of the journeys in the modeling period consist of round trips (origin–destination–origin).
Trip-chaining travel-demand models, on the other hand, assume that the choices concerning the entire journey influence each other. In this case, the choice of an intermediate destination, if any, takes into account the preceding or following des- tinations on the trip chain, the choice of transportation modes takes into account the whole sequence of trips in the chain, and so on. Models of this type have been studied for several years and have been applied to real situations, although less fre- quently than trip-based demand models. Examples of models of this type are pre- sented in Sect.4.4.
Finally,activity-based demand modelspredict travel demand as the outcome of the need to participate in different activities in different places and at different times.
They therefore take into account the relationships among different journeys made by the same person during a day and, in the most general case, between journeys
made by the various members of the same household. They are often implemented as microsimulation models, in which the decisions, activities, and trip-making of a large number of individual households and their members are explicitly represented.
Models of this type are obviously more complex than those described previously and are aimed at understanding relationships between the demand for travel and the organization of the different activities of a person and his or her household.
These models are presently at the research stage and are only discussed briefly in Sect.4.5.
Models of all types can also be classified as eitheraggregateor disaggregate, depending on the level of detail of the representation of demand and/or the factors that influence it. In aggregate models, the variables (attributes) included in the model apply to a group of users (e.g., the average times or costs of all the trips between two traffic zones, or the average number of cars owned by families of a certain category). In disaggregate models, the variables refer to the individual user (e.g., the times or costs of travel between the actual origin and destination points of a trip, or the number of cars in a specific traveler’s household). The appropriate level of aggregation of model variables depends on the purpose of demand modeling.
The prevailing use considered in this book is modeling of the entire transportation system, as represented by a network model. This implies an aggregation level that is at least zonal because, as explained in Chaps. 1 and 2, the level-of-service variables obtained from network models relate to pairs of centroid nodes that represent traffic zones.2
The last classification factor considered here relates to the basic model assump- tions. Models are calledbehavioralif they derive from explicit assumptions about users’ choice behavior and descriptive if they capture the relationships between travel demand and activity and transportation supply-system variables without mak- ing specific assumptions about decision-makers’ behavior. There are also mixed model systems in which some submodels are behavioral and others are descriptive.3 Finally, it should be noted that transportation demand models, as are all models used in engineering and econometrics, are schematic and simplified representations of complex real phenomena. They are intended to quantify certain relationships be- tween the variables relevant to the problem under study. They should not be ex- pected to reproduce reality perfectly, especially when the reality being modeled is
2It should also be noted that the appropriate level of aggregation might be different in a model’s calibration and application phases. In other words, it is possible, and even advisable in some cases, to use disaggregate data for model specification and calibration, as shown in Chap. 8, while using aggregate (e.g., average) values of zone, user, and transportation system characteristics in model applications. This corresponds to the application of the aggregation techniques “by representative user” or “by category” described in Sect. 3.7.
3Differences between behavioral and descriptive models are becoming less important. Indeed, functional forms such as logit and hierarchical logit, which can be derived from random utility theory, are increasingly being used to predict aspects of demand that have no direct behavioral interpretation in terms of a decision-maker’s choice. From this point of view, it would be more appropriate to classify the models based on their functional form, distinguishing between models that can or cannot be derived from random utility theory.
172 4 Travel-Demand Models largely dependent on individual behavior, as is the case with transportation demand.
Furthermore, as shown later, different models with different levels of accuracy and complexity can describe the same situation. However, more sophisticated models require more resources (data, specification and calibration effort, computing time, etc.), which must be justified by the application requirements.
The sections in this chapter present the characteristics of different types of trans- portation demand models, with an emphasis on passenger travel demand. Sec- tion4.2presents the partial share systems of trip-demand models. Individual sub- models, including trip production (or frequency), distribution, mode choice, and path choice, as well as an example of an overall model system for interurban travel, are presented in Sect.4.3. Sections4.4and4.5present trip-chaining and activity- based demand models, respectively. Section4.6discusses the interpretation of re- sults obtained with demand models and the application of these models for different purposes. Finally, Sect.4.7 describes some models used to predict freight trans- portation demand.