Elizabeth C. McBride

Incorporating Land Use into Methods of Synthetic Population Generation and of Transfer of Behavioral Data

This paper describes a new method of population synthesis that includes land use information. The method is based on an initial identification of suitable land use summaries to build a spatial taxonomy at any spatial scale. This same taxonomy is then used to classify household travel survey records (persons and households) and in parallel geographic subdivisions for the state of California. This land use information is the added dimension in the population synthesis methods for travel demand analysis. Synthetic population generation proceeds by expanding (re-creating) the records of the households responding to the survey and the entire array of travel behavior data reproduced for the synthetic population. The basis for selecting the variables to use in the synthetic population is first testing their significance in simplified specification in models of travel behavior that include land use as an explanatory variable and account for the shape of behavioral data (e.g., observations with no travel). The paper shows differences between synthetic populations with and without land use data to demonstrate the behavioral realism added by this approach.

California Business Establishment Evolution and Transportation Provision

This research filled a gap in empirically supported knowledge linking the survival and economic success of business establishments to locational characteristics including access to transportation facilities. This relationship was studied for the entire State of California while controlling in a statistically robust way for a variety of factors influencing business life cycle events, such as closures, formation/birth, and relocation. Using panel data analysis methods and data spanning two decades, the external (local context, transportation network) and internal (business type, number of employees, age of the establishment) factors that influence the success and failure of business establishments were examined. Using panel analysis, manufacturing and retail business establishments were studied in particular illustrating differences and commonalities in event occurrence and the factors that impact these events.

A Latent Class Pattern Recognition and Data Quality Assessment of Non-Commute Long-Distance Travel in California

This study analyzes 8-week long-distance travel records from the California Household Travel Survey for completeness and identifies general types of non-commute long-distance tours using Latent Class Analysis. Likely due to the difficulty of gathering data of this kind, there has been relatively limited study of non-commute long-distance travel, despite the substantial contribution to many households’ greenhouse gas emissions and travel expenses. The California Household Travel Survey includes a valuable long-distance 8-week travel dataset, but this study identifies several possible shortcomings in the dataset. Of particular importance is a severe underreporting of shorter trips, which may result from a mix of respondent forgetfulness and survey fatigue. Despite the issues with the data, latent class cluster analysis was able to identify five distinct, informative patterns of long-distance travel. This analysis shows that long-distance tours for vacation, business travel, medical, and shopping are substantially distinct in terms of their travel characteristics and correspond to different combinations of other activities in the tour, and they are done by different types of households. The method used here to identify the typology of long-distance travel can be easily expanded to include a variety of other explanatory variables of this type of behavior in more focused data collection settings.

A Spatial Latent Profile Analysis to Classify Land Uses for Population Synthesis Methods in Travel Demand Forecasting

In this paper, a new land use classification method is explored for its utility in explaining travel behavior and as a new dimension in population synthesis for travel demand forecasting. This method is based on latent profile analysis applied to 17 business establishment indicators for each of the more than 20,000 block groups in California. The method reproduces the four types of land use environments (urban, suburban, exurban, and rural) identified in a previous paper, and improves our ability to create a finer-grain geographic classification based on land use. It also offers similar indications about the difference between urban dwellers (that make more trips but travel shorter distances) and rural residents (that make fewer trips but with more vehicle miles traveled).

Long Distance Travel and Destination Attractiveness

Author(s): Goulias, Konstadinos G., PhD; Davis, Adam W.; McBride, Elizabeth C. | Abstract: This report provides a summary of analyses using data of long distance tours by each household from an 8-week California Household Travel Survey travel log. The first analysis, uses Structural Equations Models (SEM) and a simpler variant called Path Analysis on three censored variables (tour miles by air, miles driving, and miles by public transportation) and two categorical variables (main trip tour purpose) and number of overnight stays. The second analysis, uses Latent Class Cluster Analysis (LCCA) to identify five distinct, informative patterns of long-distance travel. This analysis shows that long-distance tours for vacation, business travel, medical, and shopping are substantially distinct in terms of their travel characteristics and correspond to different combinations of other activities in the tour and they are done by different types of households. The methods used here to identify the typology of long distance travel can be easily expanded to include a variety of other explanatory variables of this type of behavior in more focused data collection settings.