Overview of Land Use Transport Models

The previous chapters in this Handbook have shown that spatial development, or land use, determines the need for spatial interaction, or transport, but that transport, by the accessibility it provides, also determines spatial development. However, it is difficult to empirically isolate impacts of land use on transport and vice versa because of the multitude of concurrent changes of other factors. This poses a problem if the likely impacts of integrated land-use and transport policies to reduce the demand for travel are to be predicted. There are principally three methods to predict those impacts. The first is to ask people how they would change their location and mobility behaviour if certain factors, such as land use regulations or transport costs, would change ('stated preference'). The second consists of drawing conclusions from observed decision behaviour of people under different conditions on how they would be likely to behave if these factors would change ('revealed preference'). The third method is to simulate human decision behaviour in mathematical models. All three methods have their advantages and disadvantages. Surveys can reveal also subjective factors of location and mobility decisions, however, their respondents can only make conjec-tures about how they would behave in still unknown situations, and the validity of such con

Traveler decision making under conditions of knowledge limitations and information provision

This paper presents a discrete-choice based model of traveler response to information. It contributes to existing approaches in three ways: firstly, instead of focusing on either the acquisition of information or the effect of received information on travel choices, this model describes the full sequence of possibly multiple information acquisitions, followed by a travel choice. In doing so, the model provides a more accurate account of actual travel behavior under conditions of knowledge limitations and information provision. Secondly, instead of modeling the utility of information acquisition in an ad-hoc fashion (e.g. as a linear function of attributes of the information service and the trip context), we conceive the utility of information acquisition in terms of the anticipated utility of the travel set after having received the information. Thirdly, instead of focusing on a specific type of knowledge limitation and a specific type of information, we describe response to a variety of information types, resulting from a variety of knowledge limitations. This model of travel information response is then estimated on data collected in a multimodal travel simulator-experiment with travel information provision. Estimation results provide face validity to the proposed modeling approach, as a substantial share of the variation in choices for a variety of information options and travel alternatives appears to be captured by the model. Furthermore, a number of new insights are gained with respect to the role of travel information in multimodal travel choice making. For example, it is found that travelers hold intrinsic preferences of some information types (i.e. the generation of new travel alternatives) over others (e.g. the assessment of already known alternatives) that are beyond straight economic explanations

Traveler decision making under conditions of knowledge limitations and information provision

This paper presents a discrete-choice based model of traveler response to information. It contributes to existing approaches in three ways: firstly, instead of focusing on either the acquisition of information or the effect of received information on travel choices, this model describes the full sequence of possibly multiple information acquisitions, followed by a travel choice. In doing so, the model provides a more accurate account of actual travel behavior under conditions of knowledge limitations and information provision. Secondly, instead of modeling the utility of information acquisition in an ad-hoc fashion (e.g. as a linear function of attributes of the information service and the trip context), we conceive the utility of information acquisition in terms of the anticipated utility of the travel set after having received the information. Thirdly, instead of focusing on a specific type of knowledge limitation and a specific type of information, we describe response to a variety of information types, resulting from a variety of knowledge limitations. This model of travel information response is then estimated on data collected in a multimodal travel simulator-experiment with travel information provision. Estimation results provide face validity to the proposed modeling approach, as a substantial share of the variation in choices for a variety of information options and travel alternatives appears to be captured by the model. Furthermore, a number of new insights are gained with respect to the role of travel information in multimodal travel choice making. For example, it is found that travelers hold intrinsic preferences of some information types (i.e. the generation of new travel alternatives) over others (e.g. the assessment of already known alternatives) that are beyond straight economic explanations

Sampling of Alternatives in Random Regret Minimization Models (presentation)

We propose a methodology to achieve consistency, asymptotic normality and efficiency, while sampling alternatives in Random Regret Minimization models. Our method is an extension of previous results for Logit and MEV models. We illustrate the methodology using Monte Carlo experimentation. Experiments show that the proposed methodologyInfrastructures, Systems and ServicesTechnology, Policy and Managemen

Sampling of alternatives in random regret minimization models

We propose a methodology to achieve consistency, asymptotic normality and efficiency, while sampling alternatives in Random Regret Minimization models. Our method is an extension of previous results for Logit and MEV models. We illustrate the methodology using Monte Carlo experimentation. Experiments show that the proposed methodology is practical, that it outperforms the uncorrected model, and that it yields acceptable results.Infrastructures, Systems and ServicesTechnology, Policy and Managemen