Ta Theo Arentze

Investigating Consumers' Tendency to Combine Multiple Shopping Purposes and Destinations

Due to the increasing time pressure that they face, many consumers are becoming more concerned about the efficiency of their shopping patterns. Retailers have recognize this trend, have improved shopping convenience by offering greater variety in product categories and making it easier for consumers to combine visits to multiple stores. However, little is known about how consumers improve the efficiency of their shopping trips, or how changes in retail supply affect the way in which consumers combine multiple purposes and destinations. Building on previous work in consumer shopping trip modeling and conjoint design theory, this paper introduces a choice-based conjoint approach to studying and modeling this phenomenon. The approach is illustrated in a case study which investigated the tendency of Dutch shoppers to combine grocery, drugstore and clothing purchases across multiple shopping destinations. It was observed that the tendency of consumers to combine purchases differed from category to category and also depended on category availability. In general, consumers combined considerably less purchases than could be expected if their shopping trip planning were based purely on travel cost minimization.

Transport stated choice responses: effects of task complexity, presentation format and literacy

The impact of respondent burden and task complexity on quality of stated choice (SC) data remains an issue in transportation research. Furthermore, little is known on the applicability of the technique to less literate individuals in developing countries. This study describes the results of a SC experiment involving the choice of transport mode for a work trip in the South-African context. The complexity of choice task and presentation method of choice alternatives were varied independently in an SC experiment. The findings suggest that the presentation method has no significant impacts and task complexity does have significant effects on data quality. We find no effect on data quality related to literacy level.

Social Networks, Social Interactions, and Activity-Travel Behavior: A Framework for Microsimulation

We argue that the social networks and activity-travel patterns of people interact and coevolve over time. Through social interaction, people exchange information about activity-travel choice alternatives and adapt their latent and overt preferences for alternatives to each other. At the same time, social networks are not static: new social links emerge and existing social links may dissolve in time, depending on activity-travel schedules and the attributes of persons. In this paper we propose a theoretical framework to incorporate these dynamics in microsimulations of activity-travel patterns. A core assumption of the proposed theory is that the utility that a person derives from social interaction is a function of dynamic social and information needs, on the one hand, and of similarity between the relevant characteristics of the persons involved, on the other. Furthermore, persons tend to adapt their preferences so as to increase the utility they derive from their social networks. We derive the theory and models from basic principles and discuss results of a first round of simulations conducted to examine the behavior of the model. We argue that the model is consistent with existing theories and findings in social network analysis.

A Multipurpose Shopping Trip Model to Assess Retail Agglomeration Effects

Multipurpose shopping is a prominent and relevant feature of shopping behavior. However, no methodology is available to assess empirically how the demand for multipurpose shopping depends on retail agglomeration or, in general, the characteristics of retail supply, such as the numbers and types of stores in a shopping center or the number of categories in a supermarket. The authors propose a nested-logit model that captures retail agglomeration effects on consumer choice of shopping trip purpose (what to buy) and destination (where to buy). The authors estimate parameters representing trip purpose-adjustment and between-store attraction effects on shopping trip data collected from a sample of 1704 households in The Netherlands. Both effects are significant for each of the three categories for which the model is estimated. This is consistent with the idea of agglomeration effects. The findings suggest that agglomeration helps attract not only multipurpose but also single-purpose trips. A comparison of multi- and single-purpose trip model predictions shows that single-purpose models underpredict the number of trips to larger shopping centers.

Activity pattern similarity : a multidimensional sequence alignment method

The classification of activity patterns is an important research topic in activity analysis. First, it constitutes the basis for analyzing activity patterns, for instance by correlating the derived classification with spatial and/or socio-economic variables. Secondly, the underlying mechanisms can be used to assess the degree of correspondence between observed activity patterns and activity patterns predicted by some activity-based model of transport demand. Traditionally, conventional Euclidean distance measures have been used for the comparison of activity patterns. Consequently, the sequence information embedded in activity patterns has not been explicitly considered when comparing activity patterns. More recently, sequence alignment methods have been proposed. Although these methods have some advantages, they are uni-dimensional and hence cannot incorporate the interdependencies between attributes. This paper therefore proposes a multidimensional sequence alignment method to measure differences in both sequential and interdependency information embedded in activity patterns.

Implementation Framework and Development Trajectory of FEATHERS Activity-Based Simulation Platform:

To facilitate the development of dynamic activity-based models for transport demand, the FEATHERS framework was developed. This framework suggests a four-stage development trajectory for a smooth transition from the four-step models toward static activity-based models in the short term and dynamic activity-based models in the long term. The development stages discussed in this paper range from an initial static activity-based model without traffic assignment to a dynamic activity-based model that incorporates rescheduling, learning effects, and traffic routing. To illustrate the FEATHERS framework, work that has been done on the development of static and dynamic activity-based models for Flanders (Belgium) and the Netherlands is discussed. First, the data collection is presented. Next, the four-stage activity-based model development trajectory is discussed in detail. The paper concludes with the presentation of the modular FEATHERS framework, which discusses the functionalities of the modules and how they accommodate the requirements imposed on the framework by each of the four stages.

Integrating Bayesian networks and decision trees in a sequential rule-based transportation model

Several activity-based transportation models are now becoming operational and are entering the stage of application for the modelling of travel demand. Some of these models use decision rules to support its decision-making instead of principles of utility maximization. Decision rules can be derived from different modelling approaches. In a previous study, it was shown that Bayesian networks outperform decision trees and that they are better suited to capture the complexity of the underlying decision-making. However, one of the disadvantages is that Bayesian networks are somewhat limited in terms of interpretation and efficiency when rules are derived from the network, while rules derived from decision trees in general have a simple and direct interpretation. Therefore, in this study, the idea of combining decision trees and Bayesian networks was explored in order to maintain the potential advantages of both techniques. The paper reports the findings of a methodological study that was conducted in the context of Albatross, which is a sequential rule based model of activity scheduling behaviour. To this end, the paper can be situated within the context of a series of previous publications by the authors to improve decision-making in Albatross. The results of this study suggest that integrated Bayesian networks and decision trees can be used for modelling the different choice facets of Albatross with better predictive power than CHAID decision trees. Another conclusion is that there are initial indications that the new way of integrating decision trees and Bayesian networks has produced a decision tree that is structurally more stable.

Modeling learning and adaptation processes in activity-travel choice A framework and numerical experiment

This paper develops a framework for modeling dynamic choice based on a theory of reinforcement learning and adaptation. According to this theory, individuals develop and continuously adapt choice rules while interacting with their environment. The proposed model framework specifies required components of learning systems including a reward function, incremental action value functions, and action selection methods. Furthermore, the system incorporates an incremental induction method that identifies relevant states based on reward distributions received in the past. The system assumes multi-stage decision making in potentially very large condition spaces and can deal with stochastic, non-stationary, and discontinuous reward functions. A hypothetical case is considered that combines route, destination, and mode choice for an activity under time-varying conditions of the activity schedule and road congestion probabilities. As it turns out, the system is quite robust for parameter settings and has good face validity. We therefore argue that it provides a useful and comprehensive framework for modeling learning and adaptation in the area of activity-travel choice.

Identifying decision structures underlying activity patterns : An exploration of data mining algorithms

The utility-maximizing framework—in particular, the logit model—is the dominantly used framework in transportation demand modeling. Computational process modeling has been introduced as an alternative approach to deal with the complexity of activity-based models of travel demand. Current rule-based systems, however, lack a methodology to derive rules from data. The relevance and performance of data-mining algorithms that potentially can provide the required methodology are explored. In particular, the C4 algorithm is applied to derive a decision tree for transport mode choice in the context of activity scheduling from a large activity diary data set. The algorithm is compared with both an alternative method of inducing decision trees (CHAID) and a logit model on the basis of goodness-of-fit on the same data set. The ratio of correctly predicted cases of a holdout sample is almost identical for the three methods. This suggests that for data sets of comparable complexity, the accuracy of predictions does not provide grounds for either rejecting or choosing the C4 method. However, the method may have advantages related to robustness. Future research is required to determine the ability of decision tree-based models in predicting behavioral change.

Size and Composition of Ego-Centered Social Networks and Their Effect on Geographic Distance and Contact Frequency

Social activities are responsible for a substantial proportion of trips by individuals and households. Therefore, travel demand is determined primarily by the size and the spatial distribution of peoples social networks. In the field of transportation, little is known about social networks in relation to trip generation. However, research interest in social networks is rapidly emerging. This paper describes the instrument used to collect data on ego-centered social networks in a survey in the Netherlands and the analysis of these data in relation to travel demand. Five successive regression models were used to analyze and predict the size of the egos social network, distribution of network members across seven social categories, geographic distance, and contact frequency (by means of information and communication technology and face-to-face) with each social network member based on socio-demographic characteristics of the ego. The results indicate that, even though significant relationships exist, the relationships between socio-demographic variables and social network size, the choice of social category, geographic distance, and (mediated) contact frequency are not strong. A better model fit is found for predicting face-to-face contact frequency.