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To comply with AB 32 and SB 375, California local and regional governments are working to reduce vehicle miles traveled (VMT). To develop targeted policies with scarce resources, policymakers need guidance as to which policies will be most effective in their jurisdictions. This research uses empirical analysis of travel survey data to quantify how much Californians will change the amount that they drive in response to changes in land use and transport system variables. Our study improves upon past research in three key ways. First, we assemble and use a dataset that consists of merged information from five California-based household travel surveys that were conducted between 2000 and 2009. Second, we develop and employ a novel approach to control for residential self-selection, categorizing neighborhoods into types and using these as the alternatives in a predictive model of neighborhood type choice. Third, we focus on understanding heterogeneity in effects of variables on VMT across two important dimensions – neighborhood type and trip type. We find that the effects of some land use and transport system characteristics do depend on neighborhood type, in ways that are intuitive but had not previously been empirically verified. Results of this research are embedded in the VMT Impact spreadsheet tool, which allows users to easily see the implications of this work for any census tract, city, or region in California. |
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To comply with AB 32 and SB 375, California local and regional governments are working to reduce vehicle miles traveled (VMT). To develop targeted policies with scarce resources, policymakers need guidance as to which policies will be most effective in their jurisdictions. This research uses empirical analysis of travel survey data to quantify how much Californians will change the amount that they drive in response to changes in land use and transport system variables. Our study improves upon past research in three key ways. First, we assemble and use a dataset that consists of merged information from five California-based household travel surveys that were conducted between 2000 and 2009. Second, we develop and employ a novel approach to control for residential self-selection, categorizing neighborhoods into types and using these as the alternatives in a predictive model of neighborhood type choice. Third, we focus on understanding heterogeneity in effects of variables on VMT across two important dimensions – neighborhood type and trip type. We find that the effects of some land use and transport system characteristics do depend on neighborhood type, in ways that are intuitive but had not previously been empirically verified. Results of this research are embedded in the VMT Impact spreadsheet tool, which allows users to easily see the implications of this work for any census tract, city, or region in California. |
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Fehr and Peers, City of San Diego |
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5000 |
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<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>The research goal of this project was to explore heterogeneity in how much Californians will change the amount that they drive in response to changes in land use and transport system characteristics. We explore heterogeneity across two important dimensions – neighborhood type and trip type – and use statistical analysis of travel survey and land use data to quantify these relationships. We control for key household and individual demographic characteristics and characteristics of the surveys themselves. We also control for household selection of residential neighborhood type. </SPAN></P><P><SPAN /></P><P><SPAN>This project used data from numerous sources and required the use of multiple statistical methods to estimate the effect of land use and transport system variables on VMT, differentiated by local context. To create the dataset, we merged observations from five household travel surveys conducted in California between 2000 and 2009, calculated the distance for each trip taken in a vehicle, and added variables to represent the built environment in the census tract where each household lived. These variables were derived from census data as well as calculated using GIS and MapQuest’s Application Programming Interface (API). </SPAN></P><P><SPAN /></P><P><SPAN>Our final household estimation sample included complete observations for 52,975 weekday travel diaries from 45,624 households, some of which reported their travel on two days. This sample is much larger than any that we are aware of in the related existing literature. Our large sample is important because it allows us to obtain robust estimates of the effects we are interested in for each of seven distinct neighborhood types. </SPAN></P><P><SPAN /></P><P><SPAN>Our main analysis consisted of three steps. First, we used quantitative methods to classify census tracts into seven neighborhood types. Second, we estimated a multinomial logit model (MNL) of household choice of which neighborhood type to live in. Finally, we estimated tobit models of household weekday VMT, commute VMT for adult workers, and nonwork VMT for all adults for each neighborhood type. The tobit analyses are linked to the MNL model of neighborhood type choice as a means to control for residential self-selection. Tobit models are similar to linear regression analysis techniques, but more appropriately account for the significant percentage of zero VMT observations in our data. These models are the basis for calculation of the marginal effects and elasticities that are the main results of this project. </SPAN></P><P><SPAN /></P><P><SPAN /></P><P><SPAN>Results </SPAN></P><P><SPAN /></P><P><SPAN>The contribution of this work is to estimate both average VMT and the effects of land use and transport system characteristics on VMT for different types of neighborhoods and for different types of trips. At the most basic level, we find that there are surprisingly large differences in average VMT across neighborhood types: the highest-VMT neighborhood type has three times the average VMT as the lowest. </SPAN></P><P><SPAN /></P><P><SPAN>We also find that the effects of some land use and transport system characteristics do depend on neighborhood type, in ways that are intuitive but had not previously been empirically verified. For instance, the effect of a change in gasoline price on VMT is effectively zero in both “Central City” and “Rural” neighborhoods. This likely reflects the fact that residents who drive in these neighborhoods do not have flexibility to choose to drive less when gas prices are high – they are already minimizing the amount that they drive. In all other neighborhood types, the VMT effect of pricing is uniformly large and statistically significant. The effect on VMT of improving job access is highly variable across neighborhood types, with the largest absolute effect of local jobs seen in the “Rural” and “Suburb, Single Family Homes” neighborhood types. As would be expected, changing road density is an important determinant of VMT only in neighborhoods with relatively lower road densities. Understanding the differences in effectiveness of policies on VMT will help to prioritize local actions to reduce VMT to comply with AB 32 and SB 375. </SPAN></P><P><SPAN /></P><P><SPAN>A rough scenario analysis indicates that marginal infrastructure changes within neighborhood types will yield reductions in VMT and greenhouse gas emissions on the order of 5 percent. </SPAN></P><P><SPAN /></P><P><SPAN>Conclusions </SPAN></P><P><SPAN /></P><P><SPAN>This research has shown clearly that there is considerable heterogeneity in both Californians’ VMT and their estimated VMT response to changes in land use and transport system characteristics. These differences can be explained by categorizing neighborhoods. Looking forward, we suggest that studies of current policy “natural” experiments with before-after data collection be conducted, as these would provide a more direct link between on-the-ground actions and their VMT results. </SPAN></P><P><SPAN /></P><P><SPAN /></P><P><SPAN /></P></DIV></DIV></DIV> |
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<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>There are no access and use limitations for this item.</SPAN></P></DIV></DIV></DIV> |
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neighborhood_type |
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["San Diego County","VMT","vehicle miles traveled"] |
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en-US |
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150000000 |
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