Travel Behavior and the Built Environment: Local and Regional Influences
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The American built environment and the way we travel through it are fundamentally linked, and this research paper will attempt to describe that complex relationship. Our love for and dependence on the automobile has environmental and financial consequences, and understanding how urban spatial structure contributes to vehicle travel can help policy-makers encourage the use of alternative modes of transportation. The travel choices available to us are largely dependent on where we live: a downtown resident usually has some sort of public transportation available or has destinations within walking distance, while a suburb dweller usually has to drive to get to most places. Can planners use this knowledge to design the types of spaces that lead to less vehicle travel? This paper will examine measures of the built environment at both the neighborhood and the regional scale to try to determine which matter most in influencing travel. Past research on this topic has yielded inconsistent results. American travel patterns changed dramatically in the latter half of the twentieth century, when car ownership became the norm and the modern suburb expanded across the country. Studies on how these changes in travel behavior are related to characteristics of our environment have focused on a wide array of travel outcomes and measures of urban form with different methods of analysis. Some have found that the small-scale built world does impact travel choices. Others suggest that the larger metropolitan spatial structure matters more. This study will attempt to clarify the results by adding a measure of urban form—polycentricity—that quantifies the degree of high-density dispersion throughout a metropolitan area. The main data source used for this study is the 2001 National Household Travel Survey, which collects information from a representative sample of households across the United States about their travel over a 24-hour survey day. This was supplemented with information from the U.S. Census in order to enrich the geographic variables available. Dr. Jiawen Yang’s spring 2010 studio used Census data to develop a measure of polycentricity in ArcGIS. In each major city, census tracts were categorized as low density, moderate density, moderate-high density, and high density. Directional distribution ellipses were used to measure the area of each category of tracts and compare their dispersion to the total area of the city. Larger areas of dense tracts equate a higher degree of polycentricity. Once the data were collected, descriptive statistics were computed to identify relationships between some key geographic variables and certain travel outcome measures. The geographic variables examined here were metropolitan area population, block group population density, high-density polycentricity, moderate-high density polycentricity, and overall city density. The travel outcomes measured were average mode split (vehicle vs. non-vehicle trips, which include public transit and non-motorized modes), trip time, and trip distance. First, the distribution of different trip types according to mode, length of time, or distance traveled was examined across different categories of each environmental variable. Then, the individual average trip length (by minutes and miles) was calculated for each built environment measure. Next, a regression model was built in order to measure how the different environmental measures interact to influence travel and to control for a selection of socioeconomic variables, including age, gender, race, level of education, household income, employment status, and household life cycle. The geographic variables included in the model are metropolitan area population, metropolitan land area, block group population density, census tract job density, high-density polycentricity, moderate-high density polycentricity, and overall city density. Four sets of three models each were run. The first looked at all trips. The next two sets examined only vehicle and non-vehicle travel, respectively. The last model measured the share that non-vehicle travel makes up of an individual’s total travel. Each model used three sets of y-variables: daily trip count, total daily miles traveled, and total daily minutes traveled. The results are complex but reveal some statistically significant relationships between different geographic variables and travel patterns. The most significant result is in block group density. As density increases, vehicle travel decreases and travel by other modes increases. Higher census tract job density also occurs with less vehicle travel, but only after a threshold job density is met. People living in highly polycentric cities travel less by vehicle than those in moderately polycentric cities. However, not all of the built environment measures influence these travel outcomes. Overall city density does not have an affect on vehicle travel, nor does city size or population. This study demonstrates that urban form does affect how we travel, and the neighborhood-level built environment measures may have more of an influence than the metropolitan area. It cannot be assumed that living in certain environments causes our travel patterns, and there are other unmeasured factors that affect our transportation choices, but there are still important lessons to be learned from this study. If planners want to try to influence transportation through urban form, they should start by examining the policies that shape the built environment at the local level.