The present historical moment may seem a particularly inopportune time to review Bowling Alone, Robert Putnam's latest exploration of civic decline in America. After all, the outpouring of volunteerism, solidarity, patriotism, and self-sacrifice displayed by Americans in the wake of the September 11 terrorist attacks appears to fly in the face of Putnam's central argument: that \"social capital\" -defined as \"social networks and the norms of reciprocity and trustworthiness that arise from them\" (p. 19)'has declined to dangerously low levels in America over the last three decades. However, Putnam is not fazed in the least by the recent effusion of solidarity. Quite the contrary, he sees in it the potential to \"reverse what has been a 30to 40-year steady decline in most measures of connectedness or community.\"' As an example of how the current \"war on terrorism\" could generate a durable civic renewal, Putnam points to the burst in civic practices that occurred during and after World War II, which he says \"permanently marked\" the generation that lived through it and had a \"terrific effect on American public life over the last half-century.\" 3 If Americans can follow this example and channel their current civic

Bowling alone: The collapse and revival of American community

Social Network Analysis

How we Think

An Introduction to MultiAgent Systems.

Foundations Of Statistics

This paper describes the conceptual development, operatonalization and empirical testing of Albatross: A Learning-based Transportation Oriented Simulation System. This activity-based model of activity-travel behavior is derived from theories of choice heuristics that consumers apply when making decisions in complex environments. The model, one of the most comprehensive of its kind, predicts which activities are conducted when, where, for how long, with whom, and the transport mode involved. In addition, various situational, temporal, spatial, spatial-temporal and institutional constraints are incorporated in the model. The decision tree is proposed as a formalism to represent an exhaustive set of mutually exclusive rules for each decision step in the model. A CHAID decision tree induction method is used to derive decision trees from activity diary data. The case study conducted to develop and test the model indicates that performance of the model is very satisfactory. We conclude therefore that the methodology proposed in this article is useful to develop computational process models of activity-travel choice behavior.

A learning-based transportation oriented simulation system

This paper discusses some experiences with the development of ALBA TROSS, a rule-based system for predicting transport demand, currently under development for the Dutch Ministry of Transport, Public Works and Water Management. The model belongs to the class of activity-based models, implying that it attempts to predict which activities are conducted where, when, with whom, for how long, and the transport mode involved. In principle, this increased complexity allows one to predict the impact of urban, and transport policies and institutional change on activity pattems and hence transport demand, but this increased complexity also involves new theoretical, and methodological challenges and problems of data collection. Some of these challenges are briefly discussed in this paper. In particular, the conceptualisation of activity behaviour, the derivation of choice heuristics from diary data, the development of appropriate goodness-of-fit measures and the problem of data quality are discussed.

Experiences with developing ALBATROSS: a learning-based transportation oriented simulation system

This paper presents an alternative to Random Utility-Maximization models of travel choice. Our Random Regret-Minimization model is rooted in Regret Theory and provides several useful features for travel demand analysis. Firstly, it allows for the possibility that choices between travel alternatives may be driven by the avoidance of negative emotions, rather than the maximization of some form of payoff. Secondly, it acknowledges that traveler decision-making in the context of multiattribute alternatives may not be fully compensatory. Besides this, we show how the Random Regret-Minimization approach is straightforwardly extended towards the case of risky travel choice, using the notion of Expected Regret. Finally, our Random Regret-Minimization model provides a straightforward and intuitive way to incorporate the notion that travelers, when faced with knowledge limitations, may wish to postpone their choice and search for more information first. The developed model is estimated on data from a multimodal travel simulator, where participants could choose between travel alternatives with risky travel times and costs, and the option of travel information acquisition. Estimation results support the validity of the proposed model of Random Regret-Minimization.

A Random Regret-Minimization model of travel choice

This paper describes the conceptual development, operationalization and empirical testing of Albatross: A Learning Based Transportation Oriented Simulation System. This activity-based model of activity-travel behavior is derived from theories of choice heuristics that consumers apply when making decisions in complex environments. The model, one of the most comprehensive of its kind, predicts which activities are conducted when, where, for how long, with whom, and the transport mode involved. In addition, various situational, temporal, spatial, spatial-temporal and institutional constraints are incorporated in the model. The decision tree is proposed as a formalism to represent an exhaustive set of mutual exclusive rules for each decision step in the model. A CHAID decision tree induction method is used to derive decision trees from activity diary data. The case study conducted to develop and test the model indicates that performance of the model is very satisfactory. We conclude therefore that the methodology proposed in this article is useful to develop computational process models of activity-travel choice behavior.

TA Theo Arentze

Papers

A learning-based transportation oriented simulation system

Experiences with developing ALBATROSS: a learning-based transportation oriented simulation system

A Random Regret-Minimization model of travel choice

ALBATROSS : a learning based transportation oriented simulation system

Multimodal public transport: an analysis of travel time elements and the interconnectivity ratio