Abstract

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.