Correlation or causality between the built environment and travel behavior? Evidence from Northern California

Handy, Cao, and Mokhatarian, 2005, in Transportation Research Part D: Transport and Environment

doi:10.1016/j.trd.2005.05.002
Location Northern California - Santa Rosa, Sacramento, Silicon Valley area, Modesto
Population General
Sample size 1682
Factor analysis type exploratory factor analysis, none rotation
Stepwise regression nan
Removal of insignificant variables nan
Reviewed by LCM

Abstract

The sprawling patterns of land development common to metropolitan areas of the US have been blamed for high levels of automobile travel, and thus for air quality problems. In response, smart growth programs—designed to counter sprawl—have gained popularity in the US. Studies show that, all else equal, residents of neighborhoods with higher levels of density, land-use mix, transit accessibility, and pedestrian friendliness drive less than residents of neighborhoods with lower levels of these characteristics. These studies have shed little light, however, on the underlying direction of causality—in particular, whether neighborhood design influences travel behavior or whether travel preferences influence the choice of neighborhood. The evidence thus leaves a key question largely unanswered: if cities use land use policies to bring residents closer to destinations and provide viable alternatives to driving, will people drive less and thereby reduce emissions? Here a quasi-longitudinal design is used to investigate the relationship between neighborhood characteristics and travel behavior while taking into account the role of travel preferences and neighborhood preferences in explaining this relationship. A multivariate analysis of crosssectional data shows that differences in travel behavior between suburban and traditional neighborhoods are largely explained by attitudes. However, a quasi-longitudinal analysis of changes in travel behavior and changes in the built environment shows significant associations, even when attitudes have been accounted for, providing support for a causal relationship.

Factors

Models

Dependent variable ln(VMD)
Model type least squares regression
Sample size 1466.0
R2 0.16
Adjusted R2
Pseudo R2 nan
AIC nan
BIC nan
Variable Coefficient p-value
Constant 3.646 0
Female -0.282 0
Working 0.298 0
Age -0.006 0.001
Driver's license 1.05 0
Cars per adult 0.17 0.004
Pro-bike/walk (travel preference) -0.055 0.049
Pro-transit (travel preference) -0.048 0.075
Safety of car (travel preference) 0.06 0.024
Car dependent (travel preference) 0.271 0
Preference for outdoor spaciousness (neighborhood characteristic/preference) 0.054 0.035
Dependent variable Change in driving
Model type ordered probit
Sample size 1490.0
R2 nan
Adjusted R2
Pseudo R2 0.214
AIC nan
BIC nan
Variable Coefficient p-value
Constant 1.508 0
Current age -0.006 0.014
Currently working 0.155 0.065
Current # kids < 18 years 0.07 0.051
Limits on driving -0.678 0
Change in income 0.0 0
# groceries within 1600 m -0.014 0.048
# pharmacies within 1600 m -0.028 0.041
# theaters within 400 m -0.703 0.055
Change in perceived accessibility (neighborhood characteristic/preference) -0.269 0
Change in perceived safety (neighborhood characteristic/preference) -0.088 0
Car dependent (travel preference) 0.115 0
Pro-bike/walk (travel preference) -0.07 0.02
Threshold parameter -1 0.543 0
Threshold parameter -2 2.142 0
Threshold parameter -3 2.589 0

The Attitudes and Travel Database is produced with support from the Center for Teaching Old Models New Tricks at Arizona State University, a University Transportation Center sponsored by the US Department of Transportation through Grant No. 69A3551747116.

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