The purpose of this paper is to determine whether a particular context factor among the variables that a researcher is interested in causally affects the route choice behavior of drivers. To our knowledge, there is limited literature that consider the effects of various factors on route choice based on causal inference.Yet, collecting data sets that are sensitive to the aforementioned factors are challenging and the existing approaches usually take into account only the general factors motivating drivers route choice behavior. To fill these gaps, we carried out a study using Immersive Virtual Environment (IVE) tools to elicit drivers' route choice behavioral data, covering drivers' network familiarity, educationlevel, financial concern, etc, apart from conventional measurement variables. Having context-aware, high-fidelity properties, IVE data affords the opportunity to incorporate the impacts of human related factors into the route choice causal analysis and advance a more customizable research tool for investigating causal factors on path selection in network routing. This causal analysis provides quantitative evidence to support drivers' diversion decision.
In reviewing the existing research it can be gleaned that transportation researchers have employed two different types of empirical data collection in studying route choice behavior. First, collecting route choice data using observed actual choices and second, collecting route choice data in hypothetical experiments. Researchers have for the majority of cases used utility maximizing theory to explain route choice behavior that is rooted in econometrics @cite_5 .
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This book, which is intended as a graduate level text and a general professional reference, presents the methods of discrete choice analysis and their applications in the modeling of transportation systems. The first seven chapters provide a basic introduction to discrete choice analysis that covers the material needed to apply basic binary and multiple choice models. The chapters are as follows: introduction; review of the statistics of model estimation; theories of individual choice behavior; binary choice models; multinomial choice; aggregate forecasting techniques; and tests and practical issues in developing discrete choice models. The rest of the chapters cover more advanced material and culminate in the development of a complete travel demand model system presented in chapter 11. The advanced chapters are as follows: theory of sampling; aggregation and sampling of alternatives; models of multidimensional choice and the nested logit model; and systems of models. The last chapter (12) presents an overview of current research frontiers.