Trip Choice Research Articles

Information gain, novelty seeking and travel: a model of dynamic activity-travel behavior under conditions of uncertainty

When making a trip, individuals make observations that may increase their knowledge about their environment. In this paper, we develop a measure of expected information gain based on a Bayesian model of mental maps and belief updating. We argue that expected information gain is an element of the utility function of trip choice alternatives under conditions of limited information and learning. Theory and models are developed. The simulations conducted illustrate that expected information gain tends to favor longer trips and variety seeking in terms of both route and destination choice. We argue, therefore, that individuals may perceive a positive utility of travel through environments with which they are less familiar.

Modeling trip choice behavior of the longline fishers in Hawaii

Abstract Fishers’ trip choice behavior in Hawaii’s longline fishery was analyzed by applying a utility theoretic mixed model (a combination of the conditional and multinomial logit (unordered) models) which accounts for both choice- and individual-specific attributes. The results indicate that fishers demonstrated utility maximizing and risk-averse behavior. They exhibited ‘inertia’ in switching to alternate trip choices. The stock level of major species, vessel age and size also significantly influenced fisher’s trip choice behavior. There was a high proportion of concord between the actual choice and model’s in-sample prediction of choices. Trip choice behavior was also simulated under different fleet structure and stock conditions.

Reverse-Engineered Land Use Patterns to Minimize Congestion

A neighborhood land use pattern designed to accommodate the most frequently taken nonwork trips within an acceptable distance from home was developed. Instead of starting from a specified set of land uses and studying the travel characteristics, mixed land use patterns that fit the observed trip-making behavior of people were formulated. The result is called a reverse-engineered neighborhood, or REN. The REN was tested against a Euclidean development that had separated land uses. Results show a substantial reduction in nonwork trip lengths (in terms of both travel time and distance) in the REN compared with the Euclidean development. The efficiency of the REN is the result of having more trip destination choices available to residents at acceptable distances. The procedures in the analysis are described and demonstrated, the results of the analysis are presented, and directions for further study are suggested.

Modeling overnight recreation trip choice: application of a repeated nested multinomial logit model.

In this paper we apply the repeated nested multinomial logit model, a version of a random utility model (RUM), to estimate the choice of an overnight versus single day recreation trip, along with the other usual choice of which of the sites to visit, and less typically, the choice of whether to participate (in our application – to fish) at all. We also find statistically significant income effects in the empirical results. The application is to Atlantic Salmon fishing and the data set is for Maine resident angler's fishing trips to rivers in Maine and Canada.

Analysis of travel mode and departure time choice for urban shopping trips

Travel mode and departure time choice of urban trips are important determinants of urban travel demand and the temporal distribution of this demand. While mode choice has received substantial attention in travel demand modeling, relatively little attention has been directed toward departure time choice. Further, the work trip has been the focus of most earlier mode choice research, and almost all earlier departure time choice research. The current paper examines the joint nature of mode and departure time choice for urban shopping trips. The model formulation in the paper adopts a nested structure with mode choice at the higher level of the hierarchy and departure time choice at the lower level. A multinomial logit (MNL) form is used for modeling mode choice and an ordered generalized extreme value (OGEV) form, which recognizes the natural temporal ordering of the departure time alternatives, is adopted for departure time choice. The proposed MNL-OGEV model is applied to data obtained from the 1990 San Francisco Bay area travel survey and is found to perform better than the MNL and nested logit models. In addition, the results indicate that the MNL and nested logit models lead to biased level-of-service estimates and to inappropriate policy evaluations of transportation control measures in the current empirical context.

Modeling Consumers’ Choices of Multiple Items

Contemporary choice models focus on choice opportunities in which consumers purchase a quantity of a single item in a product category. However, failing to recognize the possibility of assortments of multiple-Item purchases can lead to incorrect conclusions about the impact of past purchase behavior on current choices. The authors propose a model that allows for multiple-item shopping trips and apply it to scanner data for powdered soft drinks. The model provides descriptions about the influence of (1) consumer's prior behavior across previous shopping trips, (2) behavior within the same shopping trip, (3) the in-store shelf assortment available at the time of purchase, and (4) marketing mix variables on multiple-item shopping trip choices. The authors’ model provides better choice predictions in a holdout sample at the aggregate and assortment composition levels than a traditional, single-purchase choice model. Using the model, they present simulation and naturally occurring experiment results in the powdered soft drinks category. Finally, they discuss the value of their results for understanding the consequences of consumers’ choices and their implications for manufacturers.

Effects of Improving Infrastructures in Shopping Area on Its Destination Choice of Trips

Effects of Improving Infrastructures in Shopping Area on Its Destination Choice of Trips

A multinomial logit approach for multimodal choice of work trips

This paper reports the results of an explorative study on the possibility of using a multinomial logit model to replicate and predict multimodal choice for work trips in a Canadian urban context. Modal choice data from Toronto, where a multimode choice situation exists in the line-haul portion of the work trip, are used to develop the model.Both socioeconomic and system variables are used in developing the model and the parameters are estimated using maximum likelihood technique. The developed model is evaluated in terms of statistical criteria and reasonableness tests. It is further examined for its predictive ability by making choice predictions of the sample used in developing the model in addition to comparing the results with the published ones in this area. The overall modeling strategy to be used while developing and testing multimodal choice models has also been illustrated.The study shows that disaggregate models can provide reasonable results when used in forecasting the modal choice between various modes.