Investigating the Performance of Rule-based Models with Increasing Complexity on the Prediction of Trip Generation and Distribution

Abstract

Modelling travel behaviour has always been a major research area in transportation analysis. After the second World War, due to the rapid increase in car ownership and car use in Western Europe and the United States, several models have been developed by transportation planners. In the fifties and sixties, travel was assumed to be the result of four subsequent decisions that were modelled: trip generation, trip distribution, mode choice and the assignment of trips to the road network (Ruiter & Ben-Akiva, 1978). These original tripbased models have been extended to ensuing tour-based models (Daly et al., 1983) and activity-based models (Pendyala et al., 1995; Ben-Akiva & Bowman, 1998; Kitamura & Fujii, 1998; Arentze & Timmermans, 2000; Bhat et al., 2004). In tour-based models, trips are explicitly connected in tours, i.e. chains that start and end at the same home or work base. This is carried out by introducing spatial constraints, hereby dealing with the lack of spatial interrelationship which was so apparent in the traditional four-step trip-based model. In activity-based models, travel demand is derived from the activities that individuals and households need or wish to perform. Decisions with respect to travel are driven by a collection of activities that form an activity diary. Travel should therefore be modelled within the context of the entire agenda, or as a component of the activity scheduling decision. In this way, the relationship between travel and non-travel aspects is taken into account. The reason why people undertake trips is one of the key aspects to be modelled in an activity-based model. However, every working transportation model still exists of at least these original four components of trip generation, distribution, mode choice and assignment. In order to fully understand the structure of a traditional transportation model, we need to elaborate on it some more. As shown in Figure 1, trip generation encompasses both the modelling of production (P) and attraction (A) of trips for a certain region (zone). Production is mainly being modelled at the level of the household, incorporating household characteristics (income, car ownership, household composition, ...), features of the zone (land price, degree of urbanization) and accessibility of the zone, whereas attraction is modelled at zone level,