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 |