Student Travel: Evidence from 13 Diverse Metro Regions of the United States Guang Tian and Reid Ewing Department of City & Metropolitan Planning, University of Utah Guang Tian City and Metropolitan Planning University of Utah Presented by:

Department of City & Metropolitan Planning, University of Utah Introduction % 46% 48% 12% Less and less students walk and bike to school. Travel to school

Department of City & Metropolitan Planning, University of Utah Why are there less and less students choosing walk or bike to school?

Department of City & Metropolitan Planning, University of Utah (Black et al., 2001; Bringolf-Isler et al., 2008; Emond and Handy, 2010; Ewing et al., 2004; Frank et al., 2007; Larsen et al., 2011; McDonald, 2007; Mitra and Buliung, 2012; Müller et al., 2008; Schlossberg et al., 2006; Stewart, 2011; Timperio et al., 2006; Yarlagadda and Srinivasan, 2008). Distance is reported as a primary factor that impacts children’s walking or biking to school. Some built environments are associated with walking and biking to school. ().

Department of City & Metropolitan Planning, University of Utah However, the relationship is not generally found (Ewing et al., 2004; Yarlagadda and Srinivasan, 2008; Wong et al., 2011). Both the positive and negative relationships between walking and biking to school and built environment are reported (Boarnet et al., 2005; Frank et al., 2007; Giles-Corti et al., 2011; Larsen et al., 2011; Marique et al., 2013; Mitra and Buliung, 2012; Panter et al., 2008; Schlossberg et al., 2006Larsen et al., 2011; Timperio et al., 2006). The evidence of how built environment impact student’s travel-to-school choice is not consistent.

Research Question Department of City & Metropolitan Planning, University of Utah How do students travel to and from school? What is the relationship between built environment around schools and homes and student’s travel choices?

Research Design Department of City & Metropolitan Planning, University of Utah Local characteristics -Density -Diversity -Design -Distance to transit -Destination accessibility Traveler characteristics -Household income -Household size -Vehicle ownership -Driver license -Gender Mode choice -Walk -Bike -Transit -school bus -Auto External factors -Weather -Social/culture norm -Safety -etc. Regional characteristics -Region Size -Compactness index -Gas price

Methodology Department of City & Metropolitan Planning, University of Utah Household Travel Surveys Data Collection Works/Transportation~Master~Plan/Complex%20Travel2.png

Department of City & Metropolitan Planning, University of Utah Built environment: Other GIS layers Parcel level land use Population and employment Street network (buffers) Intersections Transit stops Travel time skims (TAZs)

Department of City & Metropolitan Planning, University of Utah

Department of City & Metropolitan Planning, University of Utah Analysis method multilevel binomial logistic regressions Boston … Houston … … Level 1: student Level 2: region

Department of City & Metropolitan Planning, University of Utah Mode share of all K-12 school trips Results

Department of City & Metropolitan Planning, University of Utah Walk coefficient standard error t-ratiop-value Constant <0.001 Travel time <0.001 Household income <0.001 Vehicles per capita <0.001 Driver license <0.001 female <0.001 Transit stop density within 0.25 mile buffer Activity density within 0.5 mile buffer <0.001 Job-population balance within 1 mile buffer <0.001 Intersection density within 1 mile buffer <0.001 % of 4-way intersection within 1 mile buffer Employment can be reached within 30 mins by auto Employment can be reached within 30 mins by transit <0.001 Compactness of metro area <0.001 sample size: 5017 walk trips of 39,880 trips pseudo-R2: 0.88 ( = 1 - variance of fit model / null model)

Department of City & Metropolitan Planning, University of Utah Bike coefficient standard error t-ratiop-value Constant Travel time Vehicles per capita <0.001 Driver license female <0.001 Job-population balance within 1 mile buffer Intersection density within 1 mile buffer <0.001 Gas price of metro area sample size: 628 bike trips of 39,880 trips pseudo-R2: 0.37 ( = 1 - variance of fit model / null model)

Limitations Department of City & Metropolitan Planning, University of Utah o Sample of Regions (the more regions, the more powerful of the regression) o Missing variables (weather, SRTS program etc.) o Self-selection – attitudes and preferences o Street network assumptions

Conclusions Department of City & Metropolitan Planning, University of Utah o Sociodemographic characteristics have strong influences on student travel choice. o Students travel differs from metro to metro.  Students from compact metro areas have higher probability of walking and biking to school.  There are statistically significant positive relationships between built environment (D variables) and students’ walking and biking choice. o Built environment matters.  With the increase of D variables, the probability of students’ walking and biking increase.  With the increase of gas price, the probability of students’ biking to school increase.

Department of City & Metropolitan Planning, University of Utah Thank you ! Guang Tian University of Utah