G S Root

A model of complex travel behavior: Part I—Theoretical development

This paper presents a policy sensitive approach to modeling travel behavior based on activity pattern analysis. A theoretical model of complex travel behavior is formulated on a recognition of a wide range of interdependencies associated with an individuals travel decisions in a constrained environment. Travel is viewed as input to a more basic process involving activity decisions. A fundamental tenet of this approach is that travel decisions are driven by the collection of activities that form an agenda for participation; the utility of any specific travel decision can be determined only within the context of the entire agenda. Based on the theoretical model of complex travel behavior, an operational system of models, STARCHILD (Simulation of Travel/Activity Responses to Complex Household Interactive Logistic Decisions), has been developed to examine the formation of household travel/activity patterns, and is presented in a companion paper (Recker et al, 1985).

A model of complex travel behavior: Part II—An operational model

Abstract Based on the theoretical model of complex travel behavior developed in a companion paper (Recker et al. , 1986), an operational system of models, STARCHILD (Simulation of Travel/Activity Responses to Complex Household Interactive Logistic Decisions), has been developed to examine the formation of household travel/activity patterns. The system employs a simulation approach in combination with techniques of pattern recognition, multiobjective optimization and disaggregate choice models. Initial empirical verification of the system of models is presented based on results obtained from a sample data set.

Travel/activity analysis: Pattern recognition, classification and interpretation

This paper presents a methodology for the analysis of travel/activity patterns based on a classification procedure in which the set of measurements that define human movement is represented by a pattern vector. Transformation techniques are then applied to the pattern vectors to develop a taxonomy for the pattern space. Subsequent inversion of the transformed patterns yields representative activity patterns that can be related to socioeconomic and urban form characteristics. Pattern recognition theory is demonstrated to be an effective means by which complex travel/activity patterns can be transformed into a structurally simpler space for purposes of planning and analysis.