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1 Influence of Neighborhood Types on Trip Distances A Case Study of Central Ohio Gulsah Akar, PhD (Corresponding Author) Assistant Professor City and Regional Planning Knowlton School of Architecture The Ohio State University West Woodruff Av. Columbus, OH 0 -- akar.@osu.edu Na Chen Doctoral Student City and Regional Planning Knowlton School of Architecture The Ohio State University West Woodruff Av. Columbus, OH 0 chen.@buckey .osu.edu Steven I. Gordon, PhD Professor Interim Co-Executive Director Ohio Supercomputer Center Kinnear Road Columbus, Ohio - sgordon@osc.edu November, 0 Word count = words + tables + figures (0 words each) = 0 words Paper submitted for presentation at the nd Annual Meeting of the Transportation Research Board, January, 0, Washington, D.C.

2 0 ABSTRACT This study examines the relationships between land use, transportation infrastructure, household and individual characteristics, and the resulting average trip distances using data from the Mid-Ohio Area Household Travel Survey. A multiple regression model is developed to analyze the determinants of average trip distances at the person level. First, new neighborhood categories are created using K-means cluster analysis and several land use and built environment variables. Then, these new neighborhood categories (such as central city residential areas, medium density suburbs, newly built low density suburbs, etc. ) are used as independent variables to explain the resulting average trip distances, while controlling for socio-demographics and access to transit and bicycle facilities. The results indicate that the residential location characteristics are a significant factor in explaining trip distances, people who live in areas which are away from urban centers and with a low mix of employment and population travel longer distances. For the Central Ohio Region, residing in lowest density suburbs may add up to 0% to average trip distances.

3 Akar, Chen and Gordon INTRODUCTION Links between travel demand, transportation system characteristics, urban form and distribution of population and employment have been the focus of several studies in the literature (-0). These have been viewed as the sources of several challenges related to energy consumption, global warming, environmental quality, and economic viability. Increasing mobility, primarily in terms of vehicle miles traveled (VMT), has been one key contributor to these challenges, particularly in terms of traffic congestion, greenhouse gas (GHG) emissions, air pollution and fuel consumption (,,, ). Deterioration of central urban areas and traditional downtowns along with urban sprawl, and the increased use of motorized modes -particularly private vehicles- have changed people s lifestyles. In addition, changing demographic and economic conditions and rising energy costs are affecting people s lifestyles and daily travel patterns. Therefore, planning for new land use and transportation policies requires a deeper understanding of the relationships between all these factors. The purpose of this study is to derive a model to explain the detailed relationships between land use, transportation infrastructure, household and individual characteristics, and the resulting travel patterns by studying individual travel behavior in Central Ohio. According to the population projections, over 00,000 people are expected to move into the region in the next years, bringing about dramatic changes (). It is crucial to derive a model which explains individual travel behavior in this region in terms of socio-demographic characteristics and spatial factors that can be used for decision making process concerning land use controls and transportation infrastructure. In this study, we first generate new neighborhood categories using several land use and built environment variables, then develop a multiple regression model to explain individuals trip distances using data from the Mid-Ohio Area Household Travel Survey. The remainder of this paper is organized as follows. Section reviews the existing research. Section describes the datasets and methodology used in this study. Model results and relevant discussions are presented in Section. The last section concludes this study and points out the limitations and future research directions.. BACKGROUND Household travel accounts for the vast majority (over 0%) of miles traveled on our nation s roadways and three-quarters of the CO emissions from mobile sources (). Negative externalities from increased VMT are straightforward. Increased driving results in congestion, high fuel consumption, dependence on foreign oil, decrease in air quality, and also reduces the efficiency of public resources due to more construction and maintenance. Therefore, reducing individuals trip distances implies potential benefits from decreasing these negative externalities. The carbon footprint of daily travel for an individual household is based on the types of vehicles that the household owns, their fuel efficiency, and the number of miles traveled. Several studies examined the effects of individual/household characteristics and different land use patterns on the resulting trip distances (,,,, 0,, ). For instance, Ewing et al. () studied the impacts of mixed-use development (MXDs) on trip distances and probabilities of taking alternative modes while considering certain demographic characteristics. Six regions (Atlanta, Boston, Houston, Portland, Sacramento, and Seattle) were selected based on the availability of parcel-level land-use data. Four models were estimated using a hierarchical modeling approach by trip purpose. For the trip distance by automobile, semi-logarithmic (linear-log) models were developed. The results show that both household size and vehicle ownership increase auto trip distances while the job-population balance within the MXD and the share of regional jobs reachable within 0 or 0 minutes by

4 Akar, Chen and Gordon automobile decrease these distances. They also found that the land area of the MXD and intersection density affect trip distances. Larger MXDs lead to longer auto trip distances for home-based trips. Higher intersection density is associated with shorter auto trip distances for non-home based trips. These results are consistent with expectations. Some researchers have examined the travel patterns of particular population segments that face special mobility challenges. In 00, Mercado and Páez () focused on the mean trip distances of the elderly using data from the Hamilton CMA in Canada. They used multilevel models for their modeling approach. The results reveal that being elderly and female are negatively associated with trip distances while increasing household size, having a driver s license and vehicle ownership are positively associated with trip distances. Two years later, Morency et al. () used geocoded micro-data from three major Canadian urban areas, Montreal, Toronto and Hamilton, to analyze factors influencing distance traveled for three population groups: the elderly, low-income households and single-parent households. Multivariate linear regression models and spatial expansion models were applied to estimate trip distances. The authors found that individuals in these three groups travel shorter distances in general. This result is consistent with earlier studies (,, -). The findings related to income, employment status and educational level are also consistent with similar studies (,,,, -0). Interestingly, the results for proximity to transit nodes and population density are mixed across cities. For Hamilton, only the effect of population density is significant and negative. In Toronto, both of these two variables associate positively with distance traveled, while they show negative influence in Montreal. Most studies look at the combined effects of socio-economics and land use variables together. For instance, Boarnet and Sarmiento used travel diary data of Southern California residents to explain non-work automobile trips and non-work miles traveled by car. They examined whether land use characteristics near a person's place of residence really affect behavior. They found that most land use variables are not statistically significant, except for population density and grid share (percentage of the street grid within a quarter mile radius of the person's residence that is characterized by four-way intersections). However, the influences of these two variables are quite weak. Such weak relationships between land use variables and resulting travel patterns correspond with other existing studies (, 0, ). To summarize empirical results on associations between built environment and travel, Ewing and Cervero (, 0) conducted two meta-analyses for these associations in 00 and 00 respectively. The latter was developed through computing elasticities for variables pooled from several related studies. They extended the original three "Ds" (density, diversity and design) () to seven "Ds"-destination accessibility, distance to transit, demand management and demographics (). Although some measures, like destination accessibility and street network design variables, were found to strongly influence VMT, the effects of most built environment variables, such as population density, job density, land use mix, were found to be inelastic. This conclusion is similar to Boarnet and Sarmiento's findings about the weak effects of land use variables on travel behavior. The studies mentioned above analyze the relationships between land use characteristics and the resulting travel behavior using the land use characteristics separately in their models. However, the ambiguous effects of land use variables may indicate that examining these variables in different forms may result in more interesting findings. For instance, Cervero et al. () made use of the Public Use Microdata Sample (PUMS) to study how working families in seven major metropolitan areas of US tradeoff their housing and commuting costs. The critical innovation of this research was characterizing neighborhoods and communities into five Public Use Microdata Area (PUMA) types in terms of four characteristics: average residential density, average employment density, median housing stock age, and average distance from primary central. Similarly, Clifton et al. () used the

5 Akar, Chen and Gordon Puget Sound Regional Council Household Travel Survey of 00 to develop choice models (auto versus non-auto) based on discrete urban contexts and other socio-demographic and trip characteristics. Using seven built environment variables (total population, total housing units, percent single family housing units, median block perimeter, intersection density, total employment, and retail employment) they defined eight urban contexts using K-means cluster analysis. The modeling results indicate that people living in areas which are close to central business districts, major corridors and urban core are more likely to choose alternative modes. These areas are generally characterized by high population, employment, and intersection densities, and low mix of single-family housing units. Urban contexts which are characterized by low densities and high mix of single-family housing units are negatively associated with the propensity of choosing alternative modes. Within these considerations, this paper adds to the existing literature by examining the determinants of trip distances, mainly socio-demographics, access to transportation infrastructure and land use characteristics, by using the discrete urban and rural categories created through K-means cluster analysis.. DATA & METHODS The study area for this research is the Mid-Ohio region, which is served by the Mid-Ohio Regional Planning Commission as the federally-designated Metropolitan Planning Organization (MPO). This area includes Delaware and Franklin counties, and portions of Fairfield and Licking counties in Ohio (See Figure ). Datasets from different sources were assembled for this study. The first one is the Mid-Ohio Area Household Travel Survey, covering the full counties of Franklin, Licking and Delaware and selected townships in Fairfield, Pickaway, Madison and Union counties. In this survey,, households completed travel diaries, representing, persons, 0, vehicles and,00 trips. The location of each household s residence as well as the origins and destinations of each trip are geo-coded, which enables the researchers to calculate network travel distances, travel times, and several land use characteristics. In addition to the household travel surveys, several land use and transportation system related variables are calculated based on the data acquired from the Central Ohio Transit Authority (COTA), Mid-Ohio Regional Planning Commission (MORPC), Ohio Department of Transportation (ODOT), and 000 Census.. Descriptive Analysis This section presents the descriptive statistics of the Mid-Ohio Area Household Travel Survey. Table provides the summary of these descriptive statistics. Around 0% of the individuals in the survey sample have bachelor s degrees, over 0% are white, and around % are employed. The mean age of the survey respondents is., and the majority of the households have two workers. The indicators related to mobility show that more than 0% of sampled individuals possess a valid driver s license, and the majority of households have two vehicles. In general, each driver in the household has access to at least one vehicle. The average household income is above the median income level in Ohio, which is about $0, ().. Methods In this study, inspired by the earlier work of Cervero et al. () and Clifton et al. (), we have clustered the TAZs across the Mid-Ohio region into different categories based on their land use and built environment characteristics. K-means cluster analysis is used to create new neighborhood categories. The analysis resulted in neighborhood types such as: downtown cores with very few residential areas, central city areas with a balance of employment and

6 Akar, Chen and Gordon 0 residential uses, inner city suburbs with some employment and dense residential uses, etc. Then, an Ordinary Least Squares (OLS) regression model is estimated to understand the links between these neighborhoods, and the resulting trip distances while controlling for sociodemographic characteristics. TABLE Descriptive Statistics Sample Sample Percentage percentage Gender Number of workers in hh Male.% 0.% Female.0%.% Has driver s license.% Yes 0.%.% No.% or more 0.% Educational level Number of vehicles in hh Has bachelor s degree.% 0.% Below bachelor s degree.%.% Race.% White.%.% Nonwhite.% or more.% Employment Not employed.0% Employed.% Sample mean Age. Household Income($),. Number of vehicles per. household driver *These statistics are based on the sample. They do not represent the weighted values.

7 Akar, Chen and Gordon FIGURE. Study Area - Mid-Ohio

8 Akar, Chen and Gordon Built Environment and New Neighborhood Types This section presents the variables used for creating new neighborhood types, how they were calculated, and descriptive statistics of these variables for the resulting clusters. In classifying neighborhoods of the region, the first decision was to determine what spatial unit would define neighborhoods. Census tracts are determined to be the best practical proxy for neighborhoods due to the wealth of data available for that geography, although there is an extensive literature about neighborhood definition in geography and other fields (). In this study, the unit of analysis is chosen as the TAZ level. Most of the TAZs are smaller in size, which allows for capturing variations in land-use and built environment characteristics better. The TAZs (N=0) in the Mid-Ohio region were classified into categories using K-means cluster analysis based on their land use and built environment characteristics: residential density, employment density, median age of the housing stock, percentage of detached single-family housing, and intersection density. Although these variables are similar to those that were used by Clifton et al. (), the resulting clusters characteristics are different due to differences in spatial characteristics between their study region (Puget Sound Regional Council Area) and Mid-Ohio. Employment and population numbers at each TAZ were readily available through the Ohio Department of Transportation. Intersection Density is calculated by dividing the number of intersection nodes in each TAZ by the TAZ s area. Data on street network junctions from Census Tiger files are used for this step. For median age of housing stock and percentage of single detached housing, the available data were at the census tract level. Therefore values at the TAZ level are calculated based on the values at the tract level. First, using ArcGIS, both census tract and TAZ shapefiles of the State of Ohio are converted into raster files. All the tracts are divided into 00 foot square cells which are assigned the value for that tract. We then overlay the boundary of the TAZ and calculate the mean value of the cells that fall within the TAZ boundary to estimate the value for the TAZ. K-means cluster analysis is used for classifying the Mid-Ohio TAZs into different neighborhood types. Cluster analysis is a heuristic technique for classifying data into groups, where the number and characteristics of the groups are derived from the data and are not usually known before the analysis (). As cluster analysis is highly empirical and analysis with different samples may lead to different groupings (), to check the validity of the resulting clusters, we performed the analysis on randomly drawn samples from the data, and found out that the resulting clusters do not change significantly. The number of clusters is chosen as. Some other options (such as k =,, and 0) have been tried and the results with -groups provided the best results for the study area. The following eight neighborhood groups are formed based on the cluster analysis results: Cluster : High density & mixed-use central neighborhoods Cluster : Medium density & mixed use central neighborhoods Cluster : Central Business District Cluster : High employment urban neighborhoods Cluster : New dense residential neighborhoods Cluster : Medium density suburban neighborhoods Cluster : Older low density single family neighborhoods Cluster : Newer low density single family neighborhoods The spatial distribution of these neighborhood groups is shown in Figure. Table shows the results of the K-means cluster analysis, with corresponding statistics related to the five built environment variables. The clusters are numbered in descending order of population density which is in persons per square mile.

9 Akar, Chen and Gordon 0 0 Cluster has the highest population density while cluster has the lowest. Cluster is characterized by the highest population density and second highest employment and intersection densities. These TAZs surround the central business district, consisting of higher density mixed use buildings which accommodate retail, offices and apartments (low percentage of single detached housing), and a dense street network (high intersection density). Having high population density and medium employment density, TAZs assigned to Cluster are urban zones with mixed use but more on the residential side. Apparently, the two TAZs classified to Cluster form the central business district in Columbus, featured by the highest employment and intersection densities, and the lowest percentage of single detached housing stock. Furthermore, the buildings in these areas are newer than the ones in other clusters. TAZs categorized as Cluster are close to sub-commercial urban forms which are the secondary commercial areas and dominated by higher levels of employment. TAZs in Cluster have lower population and employment, with high single detached housing stock. Around % of TAZs in Mid-Ohio fall into this group. Clusters and represent the suburban zones which are shaped by low density residential areas, low employment densities, large blocks and high proportion of single detached housing units. Most TAZs in this region are grouped into Cluster which has the lowest employment and residential densities, highest single detached housing percentage. Among the three clusters which are designated as suburban, housing stock in Cluster is the oldest. Generally in these zones, land is dominated by large blocks and sparsely settled single detached houses. Around.% TAZs in Mid- Ohio are assigned to Clusters, and, implying this region is dominated by suburban characteristics. Table presents the number of households and individuals residing at these clusters, and their respective mean trip distances. Most of the sampled individuals live in clusters,, and. As expected, trip distances vary among these clusters. It is reasonable to see that those who live in areas which are away from dense central urban areas have longer trip distances.

10 Akar, Chen and Gordon 0 Figure Mid-Ohio Clusters (TAZ)

11 Akar, Chen and Gordon TABLE New Clusters and Built Environment Variables Clusters Variables Total Population Density (persons/sq. mi) Employment Density (persons/sq. mi) Intersection Density Median Age of Structures Percent Single Detached House Mean Std. Dev Mean Std. Dev Mean Std. Dev Mean Std. Dev Mean Std. Dev Number of TAZs 0 0 TABLE Households, Individuals and Trip Lengths Clusters Frequency (Household) Percentage (Household) Frequency (Person) Percentage (Person) Mean Trip distance (miles) St. Dev. Cluster.%.%..0 Cluster.% 0.0%..0 Cluster 0.0% 0.0%.. Cluster.%.%.. Cluster.00% 0.%.0. Cluster.%.%.. Cluster.%.% Cluster.0%.%.0.

12 Ordinary Least Squares (OLS) Regression In this study, OLS regression is used to analyze the effects of urban form, socio-demographics, access to transit and bicycle networks on the resulting trip distances. The functional form for this regression model used in this study is: Log (Y) = α+ β X + β X + + β n X n + ε, where the dependent variable Y represents the mean trip distance per person, α is a constant and X, X, to X n are the independent variables which have affect travel distances, β, β, to β n are the coefficients that describe the size of the effects of these variables, and ε is the error term. The variables of interest are discussed below... Variables of Interest The selection of variables used in this study is based on the existing literature and several empirical experiments. Table provides a summary of these variables, and their means based on the estimation sample. The dependent variable is the mean trip distance at the person level. Trips which were less than 0. miles and more than 0 miles were excluded in our analysis. Although several studies use land use and built environment characteristics as separate independent variables in their models, in this study we use the new neighborhood categories (clusters) we created based on these variables. After excluding all the missing data, the total number of observations reduces to,0. As the model is estimated at the person level, this corresponds to,0 individuals. The variables included in regression models for trip distance estimation usually include gender, age, race/ethnicity, educational level, license status, employment status, household income, and vehicle ownership. While a considerable number of studies have incorporated land use variables, such as population density, employment density and intersection density separately into their models, in this study we utilize the clusters which were described in the previous section. These clusters are characterized by five built environment variables: population, employment and intersection densities, median age of housing stock, and percentage of single detached housing units. In addition, proximity to transit stops and bicycle trails/paths are also included as independent variables in this study. Finally, an index which measures the job-population balance for each TAZ is also included in the model.

13 Akar, Chen and Gordon TABLE. Variables of Interest Variable Definitions Mean Std. Dev. Mean trip distance Mean trip distance per person for all non-auto travel distances and auto travel distances from 0. miles to 0 miles.. Female Dummy variable. (, if the individual is female) Age Age of individual respondent.. Age_squared Age squared. 0. Income Household income (in 0k).. Income_squared Household income squared (in 0k) License Dummy variable (, if has a valid driver license) Vehicles per driver Number of vehicles per driver at the household level. 0. Education Dummy variable. (, if the individual has bachelor s or higher degree; 0, otherwise) Race Dummy variable. (, if the individual is non-white) Employee Dummy variable. (, if employed) Proximity to bicycle paths/ trails Number of bus stops JOB_POP * Cluster indicators Dummy variable. (, if there is at least one bicycle path/ trail within 0. mile of home location) Number of bus stops within 0. mile of home location 0.. Index that measures the employment and population balance at the TAZ level These variables are all dummy variables. (, if the TAZ of the residential location is in a given cluster; 0, otherwise) Cluster Cluster Cluster 0 0 Cluster Cluster Cluster Cluster Cluster Number of observations = 0 * JOB_POP = - [ABS (employment - 0. * population) / (employment + 0. * population)]; ABS is the absolute value of the expression in parentheses. The calculation of this index refers to the equation in Ewing et al. s paper (). But based on the empirical facts in the study area, the value 0. (which was used by Ewing et al. ()), representing a balance of employment and population, was adjusted to 0. in this study.

14 Akar, Chen and Gordon RESULTS & DISCUSSIONS The ordinary least squares regression (OLS) model, presented in Table, uses personal, household, and traffic analysis zone characteristics to explain average trip distances for individuals living in Mid-Ohio region. Two quadratic variables were used in the model (agesquared and income-squared) to account for the non-linear relationships between these variables and the resulting trip distances. Cluster is taken as the base case, and Cluster is excluded from the analysis due to very low number of individuals residing there. Table also presents the elasticity effects of these variables. For continuous variables (such as household income, age) the elasticity effect is calculated at the sample means. For dummy variables, we report the percent change in the dependent variable due to a discrete change in the dummy variable. Consistent with the existing literature (, ), being female is associated with shorter trip distances. All else being equal, being female decreases the average trip distance by.%. As the average trip distance is. miles, this indicates a 0. mile reduction in average trip distances. Both age and income are positively associated with travel distances, but at a decreasing rate. The quadratic variable for age captures the observed declining trend in distance traveled as age advances, after the peak distance traveled by the middle age group. This result validates current knowledge regarding the nonlinear relationship between trip distance and age (,,, ). It also confirms the general pattern in reality that people in the middle age group have higher probability of traveling longer, in terms of both physical conditions and socioeconomic status. Travel distances also increase with increasing income. The results indicate that the mean trip distance for employed individuals is longer as compared to the individuals who do not work. This is consistent with existing studies (-). Being employed increases the average trip distance by.0%. As expected, people who have valid driver licenses and own more vehicles take longer trips. The same trend is observed for the education level- having a bachelor s degree or higher is positively related to trip distance. One possible explanation for this effect is that people who have higher education get more opportunities to travel to different places and also are more willing to experience different environments. One interesting finding is that individuals who are non-white travel longer distances. This result differs from some other studies regarding ethnicity. For instance, using the US Nationwide Personal Transportation Survey (NPTS) data, Giuliano () found that Hispanics and African Americans have shorter daily travel distances as compared to whites. Instead of looking at the total travel distances, some studies present contradictory results for the effects of race/ethnicity on commute distances. For example, Leonard () noted that African Americans have longer commute times, whereas Coombes and Raybould () found lower average commutes are generally associated with minorities. Consistent with expectations, being in close proximity to transit stops and bicycling facilities decreases the propensity of driving, which is associated with longer trip distances particularly in Central Ohio. Existing studies use different methods to define access to transit and bicycle facilities, such as distance to transit stops, transit route density, distance between transit stops, or the number of stations per unit area. The general results are consistent. Having transit stops or bicycling paths nearby may stimulate walking, biking and taking transit (0). It is reasonable to expect that people may not prefer to use non-motorized modes for long distances. Although the use of public transportation may be preferred for long distances in certain locations, particularly in areas which are served by rapid modes, this is not the case in Central Ohio, where a well-connected transit system does not exist.

15 Akar, Chen and Gordon 0 As discussed in the background section, land-use variables have quite ambiguous influences on travel distance and no consistent conclusions were drawn on their impacts on travel behavior (0). Most studies analyzed the impacts of these variables separately. In this study we reformulate these variables using cluster analysis and create new neighborhood categories. As mentioned in the descriptive analysis for clusters, the cluster assignment of TAZs in the study area is based on five land use characteristics. Combining these characteristics, the findings suggest that people who are living in TAZs which are located in suburban areas or areas away from the urban zones are more likely to travel farther. Increase in average trip distances are observed as one moves from cluster to cluster. Residing in clusters and add the highest percentage of miles (0% and %, respectively), while clusters, and also add significant amounts to average trip distance (.%, 0.% and.% respectively). These areas are characterized by low densities in population, employment and intersection, and higher percentage of single detached housing units. The results indicate that higher job-population balance can reduce trip distances; although this variable s t-value shows slight insignificance (the t value,., is slightly smaller than. which is the critical value of t at the 0.0 level).

16 Akar, Chen and Gordon TABLE Model Results Dependent variable: Log (mean trip distance at person level) Variables Coef. t stat** Elasticity Constant Socio-Economic and Demographic Characteristics Female Age Age Household income Household income License status Vehicles per driver Education Nonwhite Employed Accessibility of Alternative Travel Modes Bus stops Bicycle lanes/trails Clusters (Cluster is the base case)* Cluster Cluster Cluster Cluster Cluster Cluster JOBPOP R-squared 0. Number of observations 0 * Cluster is excluded due to low number of observations **At value of. is significant at the 0.0 level

17 Akar, Chen and Gordon CONCLUSIONS This study examines the links between land use, transportation infrastructure, household and individual characteristics and trip distances using data from the Mid-Ohio Area Household Travel Survey. The results and related analysis can be summarized as follows. Most results regarding socio-economic characteristics are consistent with earlier studies. For example, being male, employed, having a bachelor s degree or higher, driver s license, and vehicle ownership increase trip distances significantly. The results show that individuals who are non-white travel longer distances. Being in close proximity to bicycle facilities and transit stops decrease average trip distances. The importance of locational context, usually as measured by land use variables, has been the focus of various studies. However, lack of consistent findings regarding land-use variables has required further exploration. Our study explored these relationships by looking at the effects of the discrete neighborhood categories which were created through K-means cluster analysis. This study suggests that people who live further away from central urban areas and less dense areas travel farther, and a higher mix of employment and population are associated with shorter distances. As we explained above, most studies have shown weak influence of land use attributes on travel behavior by studying these variables separately. One cause for this weak relationship is that travel behavior for households or individuals may not be directly related to the specific built environment attributes that are surrounding the individual, but rest with the general urban context within a certain range. The primary advantage of the K-mean approach is the capture of combined built environment and land use characteristics. This implies that land use policies should be flexible and analyze the arrangements among different components, instead of aiming at land use and built environment characteristics separately. Acknowledgements This study is partly funded by Ohio Department of Transportation s OPREP grant. The authors would like to particularly thank the project manager Ms. Rebekah Anderson of ODOT for her time and efforts for providing the datasets. REFERENCES. Ewing, R., et al., Traffic Generated by Mixed-Use Developments-Six-Region Study Using Consistent Built Environmental Measures. Journal of Urban Planning and Development, 0. (): p. -.. Boarnet, M. and R. Crane, The influence of land use on travel behavior: a specification and estimation strategies. Transportation Research Part A: Policy and Practice, 00. (): p. -.. Badoe, D.A. and E.J. Miller, Transportation-land-use interaction: empirical findings in North America, and their implications for modeling. Transportation Research Part D Transport and Environment, 000. (): p. -.. Cervero, R. and K. Kockelman, Travel Demand and the Ds: Density, Diversity, and Design. Transportation Research Part D: Transport and Environment,. (): p. -.. Boarnet, M.G. and S. Sarmiento, Can Land Use Policy Really Affect Travel Behavior? A Study of the Link between Non-Work Travel and Land Use Characteristics. Urban Studies,. : p. -.

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19 Akar, Chen and Gordon. Leonard, J., The interaction of residential segregation and employment discrimination. Journal of Urban Economics,. (): p. -.. Coombes, M. and S. Raybould, Commuting in England and Wales: people and place factors, in PITFIELD D. (Ed.). Transport Planning, Logistics and Spatial Mismatch., 00. (): p. -.