Accessibility plays an important role in behavioral decision making. It influences a variety of behavior patterns in time use, resource allocation, and activity allocation of household members. In spite of the recognition of the importance of accessibility in behavioral modeling, appropriate accessibility measures based on time geography are rarely used in time use and activity participation models. This study aims to include accessibility measures in behavioral models and to assess the relationship between accessibility and time use, especially in the context of interaction within households. Accessibility measures based on time geography account for the temporal fixity and spatial settings around important activity pegs for each individual, and therefore provide a unique opportunity to assess temporal and spatial aspects of human behavior. In the analysis reported in this paper Structural Equation Model (SEM) is used to model the complex correlation between individual accessibility and time use and to explain individual and household heterogeneity. We address how accessibility measures has to be treated in behavioral models with different SEM model settings and report the intra-household interaction patterns in time allocation and the importance of individual accessibility in clarifying the impact of land use on travel behavior.

With the introduction and increasing reliance on the activity based approach in travel demand analysis and forecasting, discrete choice methods are more often in spatial contexts (e.g., residential location, job location, destination/activity choice). A necessity in specifying spatial choice models is the inclusion of the alternatives decision makers consider, and a realistic inclusion in the specification of the attributes of these alternatives, the characteristics of the decision making context, and the relevant characteristics of the decision maker. These details describe differences that exist among choices and individuals making choices. In the case of travel behavior, attributes of the alternatives have traditionally included attributes such as cost, distance, time, level of service and opportunities. Researchers however have recognized the benefit of attitudes in the estimation of choice models, showing improvement in explanatory power with the inclusion of attitudinal attributes of the individual. There is still however a vast expanse of unexplored attitudinal attributes in choice modeling. Particularly lacking in choice modeling is a strong theoretical underpinning of attitude formation and attitude relation to choice. Theorists in geography in the mid to late 1970s recognized and developed one such theory regarding the emotional and attitudinal association that people have with places. This became known as the theory of sense of place, which is the “affective ties with the material environment” (Tuan, 1974). This theory presents great potential in furthering the descriptive power of choice models, particularly destination choice. However, challenges abound, as this theory is rich in development, but poor in computational operationalization. In addition, everyday activity locations have not been adequately explored in sense of place quantification. In this paper, an overview of developments in discrete choice methods is presented, followed by a discussion of sense of place. Current and future work is discussed and benefits to choice modeling are presented.

The overall objective of this project is to develop an optimal resource allocation tool for the entire state of California using Geographic Information Systems and widely available data sources. As this tool evolves it will be used to make investment decisions in transportation infrastructure while accounting for their spatial and social distribution of impacts. Tools of this type do not exist due to lack of suitable planning support tools, lack of efforts in assembling data and information from a variety of sources, and lack of coordination in assembling the data. Suitable planning support tools can be created with analytical experimentation to identify the best methods and the first steps are taken in this project. Assembly of widely available data is also demonstrated in this project. Coordination of fragmented jurisdictions remains an elusive task that is left outside the project. When this project begun we confronted some of these issues and embarked in a path of feasibility demonstration in the form of a pilot project that gave us very encouraging results. In spite of this pilot nature aiming at demonstration of technical feasibility, substantive conclusions and findings are also extracted from each analytical step.

In this project we have two parallel analytical tracks that are a statewide macroanalysis (called the zonal based approach herein) and an individual and household based microanalysis (called the person based approach herein). In the statewide macroanalysis we study efficiency and equity in resource allocation. Resources are intended as infrastructure availability and access to activity participation offered by the combined effect of transportation infrastructure and land use measured by indicators of accessibility. Stochastic frontiers are used to study efficiency and a particular type of inequality measurement called the Theil fractal inequality index is used to study equity in the macroanalysis. The outcome of this analysis are maps identifying places in California that enjoy higher levels of service when compared to the entire state and places which succeeded in allocating resources in a relatively better way than others. In the individual microanalysis we use the accessibility indicators from the macronalysis and expand them by defining a new set of indicators at a second level of spatial (dis)aggregation. Then we use them as explanatory factors of travel behavior with focus on the use of different travel models (e.g., driving alone, use of public transportation and so forth). As expected infrastructure availability and accessibility to activity opportunities has a significant and substantive effect on the use of different modes. Many resource allocation decisions, then, will impact behavior, which in turn influences the optimality and equity conditions. This implies that decisions about where and when to allocate resources in public and private transportation needs to account for changes in behavior in a dynamic fashion, using scenarios of accessibility provision and assessing their impact by studying activity and travel behavior changes.

In this paper land use densities by type of employment and infrastructure supply are used together with social and demographic characteristics to explain non-motorized travel, transit use, and solo driving in California. The land use database, the highway network database, and the travel survey used for the analysis here covers the entire state of Califonia. Land use and infrastructure have a significant, substantial, and very different role for each behavior indicator used here. They alternate in significance and importance depending on the specific behavior analyzed. We also performed experiments to identify the appropriate geographical aggregation by comparing US Census tract vs US Census block group based land use densities and infrastructure densities. Regression models gave us mixed results leading us to suggest the use of a combination between the two geographies. Next steps are also outlined in the paper.