Harry Timmermans

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.

Consumer store choice dynamics: an analysis of the competitive market structure for grocery stores

This study aims at formulating and testing a model of store choice dynamics to measure the effects of consumer characteristics on consumer grocery store choice and switching behavior. A dynamic hazard model is estimated to obtain an understanding of the components influencing consumer purchase timing, store choice, and the competitive dynamics of retail competition. The hazard model is combined with an internal market structure analysis using a generalized factor analytic structure. We estimate a latent structure that is both store and store chain specific. This allows us to study store competition at the store chain level such as competition based on price such as EDLP versus a Hi-Lo pricing strategy and competition specific to a store due to differences in location.

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.

A Multi-Agent Cellular Automata System for Visualising Simulated Pedestrian Activity

This paper describes the first impressions of the development of a multi-agent system that can be used for visualising simulated pedestrian activity and behaviour to support the assessment of design performance. This system is based on cellular automata and agent technology. Agents represent objects or people with their own behaviour, moving over a pedestrian network. Each agent is located in a simulated space, based on the cellular automata grid. Each iteration of the simulation is based on a parallel update of the agents conforming local rules. Agents positioned within an environment have sensors to perceive their local neighbourhood and affect their environment. In this manner, autonomous individuals and the interaction between them can be simulated by the system.

Spatial context and the complexity of daily travel patterns : an international comparison

The analysis of travel patterns is an important research topic in transportation research and urban planning. It provides the background information necessary to better understand the complex relationship between urban structure, the transportation system and household travel patterns. To what extent do travel behaviour reflect the properties of the urban structure and the transportation network, or do these patterns largely follow their own regularities? Can different patterns be observed across different space-time settings, or can common patterns be observed, largely independent from such contexts? To better understand these relationships, this paper reports on some of the findings of analyses, conducted to identify underlying structures in various aspects of travel patterns. Travel patterns, derived from activity and travel diary data collected in Portland (USA), Midlands (UK), Fukuoka (Japan), Canadian metropolitan areas, and the South-Rotterdam region (The Netherlands) are compared. The results indicate that travel patterns are largely independent from spatial setting, except for some extreme cases.

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.

RAMBLAS: A Regional Planning Model Based on the Microsimulation of Daily Activity Travel Patterns

In this paper we report on the development of a microsimulation model of daily activity patterns in the context of a regional planning model. One of the aims of the simulation model is to predict traffic flows on a transportation network for various times of the day by using available statistical data sources as input. We first discuss the general conceptual underpinnings of the model, and against a brief discussion of existing activity-based models of transport demand we outline the major differences between these models and the current one. Next, major operational decisions, data input, and model architecture are discussed, followed by an illustration of the model for the Eindhoven region in the Netherlands. We conclude the paper by critically discussing its basic assumptions and identifying avenues of future research.

Experimental choice analysis of shopping strategies

Recent changes in retail structure have created additional ways for consumers to organize their shopping trips. This study examines the prevalence of different shopping strategies and the impact of managerial decisions related to pricing, promotions, service, and assortment on the choice of shopping strategy. A conjoint choice model is developed to address these questions. The model differs from most previous conjoint choice models in retailing in that it incorporates the similarity of competing strategies and allows one to test whether consumer choices of shopping strategy are dependent on contextual variables such as weekday vs. weekend vs. month-end shopping.

Responses to Transit Information among Car-drivers: Regret-based Models and Simulations

Abstract This article investigates the use and effects of transit information among car drivers that consider transit as a mode-option in their choice set. It does so by first presenting a theoretical model of travel information use and effect, based on the integration of notions of Bayesian updating into a regret-based framework of travel choice. Subsequently, numerical simulation of the model provides insights into the mechanisms behind information use and effect in a mode-choice context where a traveler has both car– as well as transit-options in their choice set, and prefers traveling by car over riding by transit. These simulations show that the perceived value of acquiring transit information is limited by a number of factors. Furthermore they demonstrate that, even in the case where transit information is acquired, and the message is favorable to transit, its impact on mode choices will also be limited. Given these results for non-habitual car-drivers, it is suggested that for car-drivers in general (thus including the large share of habitual drivers), conservative estimates regarding the impact of transit information provision on modal shift would be realistic.

The Built Environment and Health: Introducing Individual Space-Time Behavior

Many studies have examined the relationship between the built environment and health. Yet, the question of how and why the environment influences health behavior remains largely unexplored. As health promotion interventions work through the individuals in a targeted population, an explicit understanding of individual behavior is required to formulate and evaluate intervention strategies. Bringing in concepts from various fields, this paper proposes the use of an activity-based modeling approach for understanding and predicting, from the bottom up, how individuals interact with their environment and each other in space and time, and how their behaviors aggregate to population-level health outcomes.