A simultaneous model of household activity participation and trip chain generation

Abstract

A trip generation model has been developed using a time-use perspective, in which trips are generated in conjunction with out-of-home activities, and time spent traveling is another component of overall time use. The model jointly forecasts three sets of endogenous variables -(1) activity participation and (2) travel time (together making up total out-of-home time use), and (3) trip generation --as a function of household characteristics and accessibility indices. It is estimated with data from the Portland, Oregon 1994 Activity and Travel Survey. Results show that the basic model, which has ten endogenous time use and trip generation variables and thirteen exogenous variables, fits well, and all postulated relationships are upheld. Test show that the basic model, which divides activities into work and nonwork, can be extended to a three-way breakdown of subsistence, discretionary and obligatory activities. The model can also capture the effects of in-home work on trip chaining and activity participation. We use the model to explore the effects on time use and trip chaining of GIS-based and zone-based accessibility indices.A simultaneous model of household activity participation and trip chain generation Tom Golob 2 BACKGROUND AND OBJECTIVES

The changes of activity-travel participation across gender, life-cycle, and generations in Sweden over 30 years

“…The model structure follows the classic conceptual framework proposed by Golob (2000), as shown in Fig. 5a.…”

“…This approach is to model trip generation jointly with time use. The models can reveal how the generation of simple and complex trip chains is interrelated with the demand for out-of-home activities, and how the travel "time budget'' effects can affect activity demand and trip generation (Golob 2000;Susilo and Avineri 2014).…”

From Macro to Microscopic Trip Generation: Representing Heterogeneous Travel Behavior

“…These disaggregate models based on discrete choice analysis are considered to be a major innovation in the field [7]. While commonly used for mode choice modeling, some applications of discrete choice algorithms have also considered destination choice, and even more recently generation choice [8]. Generation choice models estimate the frequency of daily person trips or tours [6].…”

The Effect of Density and Trip-Chaining on the Interaction between Urban Form and Transit Demand

“…SEM is used to capture the causal influences of the exogenous variables on the endogenous variables and the causal influences of the endogenous variables on one another. In the transportation literature there exist several applications of SEM using cross-sectional data, for example, Pendyala (1998), Fuji and Kitamura (2000), and Golob (2000). Additional examples are discussed by Golob (2003).…”