A micro-analysis of land use and travel in five neighborhoods in the San Francisco Bay Area

Kitamura, Mokhtarian, Laidet, 1997, in Transportation

doi:10.1023/A:1017959825565
Location San Francisco Bay Area, California
Population General
Sample size 640
Factor analysis type Unknown, Unknown rotation
Stepwise regression no
Removal of insignificant variables no
Reviewed by MWC

Abstract

This study examined the effects of land use and attitudinal characteristics on travel behavior for five diverse San Francisco Bay Area neighborhoods. First, socio-economic and neighborhood characteris

Factors

Variable Loading
Driving allows me to get more done (Convenience) +
Driving allows me freedom (Mode-liking) +
I would rather drive an electric vehicle than give up driving (Mode-liking) +
Variable Loading
Environmental protection costs too much (Environmentalism) -
Environmental protection is good for California's economy (Environmentalism) +
Environmentalism hurts minority and small businesses (Environmentalism) -
People and jobs are more important than the environment (Environmentalism) -
Stricter vehicle smog control laws should be introduced and enforced (Transport policy) +
We should raise the price of gasoline to reduce congestion and air pollution (Environmentalism) +
Vehicle emissions increase the need for health care (Environmentalism) +
Using tax dollars to pay for public transportation is a good investment (Transport policy) +
We should provide incentives for people who use electric . . . vehicles (Transport policy) +
Whoever causes environmental damage should repair the damage (Environmentalism) +
Variable Loading
Buses and trains are pleasant to travel in (Mode-liking) +
I can read and do other things when I use public transportation (Convenience) +
Public transportation is unrelible (Convenience) -
Ridesharing saves money (Cost) +
I am not comfortable riding with strangers (Safety) -
The rideshare car or van is often late (Convenience) -
I like someone else to do the driving (Mode-liking) +
Too many people drive alone (Transport policy) +
It costs more to use public transportation than . . . to drive a car (Cost) -
Variable Loading
I need to have space between me and my neighbors (Res preference/perception) +
I would only live in a multiple family unit as a last resort (Res preference/perception) +
It's important for children to have a large backyard for playing (Res preference/perception) +
High density residential development should be encouraged (Res preference/perception) -
Variable Loading
I would be willing to pay a toll to drive on an uncongested road (Convenience) +
More lanes should be set aside for carpools and buses (Transport policy) +
We need to build more roads to help decrease congestion (Transport policy) +
Variable Loading
Getting stuck in traffic doesn't bother me too much (Convenience) -
I would like to have more time for leisure (Lifestyle) +
I feel that I am wasting time when I have to wait (Convenience) +
Traffic congestion will take care of itself because other people will adjust (Environmentalism) -
Variable Loading
Having shops and services within walking distance . . . would be important (Res preference/perception) +
Too much valuable agricultural land is consumed to supply housing (Environmentalism) +
I use public transportation when I cannot afford to drive (Cost) +
Variable Loading
I like to spend most of my time working (Lifestyle) +
When . . . busy at work, I get more done by cutting back on personal time (Lifestyle) +
... I would be willing to give up a day's pay to get a day off work (Lifestyle) -

Models

Dependent variable Fraction of trips by auto
Model type linear regression
Sample size 640.0
R2 0.135
Adjusted R2
Pseudo R2 nan
AIC nan
BIC nan
Variable Coefficient p-value
Vehicles per person 0.551 0.002
Driver's license 2.275 0.0
High education 0.118 0.442
Parking spaces available 0.104 0.0
Distance to nearest bus stop 1.137 0.001
Distance to nearest park 0.259 0.009
Intercept -2.169 nan
Dependent variable Fraction of trips by auto
Model type linear regression
Sample size 640.0
R2 0.1818
Adjusted R2
Pseudo R2 nan
AIC nan
BIC nan
Variable Coefficient p-value
Vehicles per person 0.453 0.008
Driver's license 2.004 0.0
High education 0.156 0.308
Pro-environment -0.217 0.003
Pro-transit/ridesharing -0.23 0.001
Suburbanite 0.157 0.027
Automotive mobility 0.472 0.0
Time pressure -0.146 0.039
Urban villager -0.145 0.048
TCM -0.008 0.912
Workaholic 0.13 0.074
Intercept -1.005 nan
Dependent variable Fraction of trips by auto
Model type linear regression
Sample size 640.0
R2 0.1818
Adjusted R2
Pseudo R2 nan
AIC nan
BIC nan
Variable Coefficient p-value
Parking spaces available 0.111 0.0
Distance to nearest bus stop 0.823 0.017
Distance to nearest park 0.193 0.053
Pro-environment -0.166 0.024
Pro-transit/ridesharing -0.235 0.001
Suburbanite 0.062 0.396
Automotive mobility 0.519 0.0
Time pressure -0.115 0.106
Urban villager -0.163 0.03
TCM 0.021 0.772
Workaholic 0.112 0.129
Intercept 0.726 nan
Dependent variable Fraction of trips by auto
Model type linear regression
Sample size 640.0
R2 0.1818
Adjusted R2
Pseudo R2 nan
AIC nan
BIC nan
Variable Coefficient p-value
Vehicles per person 0.387 0.024
Driver's license 2.005 0.0
High education 0.138 0.363
Parking spaces available 0.098 0.001
Distance to nearest bus stop 0.765 0.023
Distance to nearest park 0.224 0.021
Pro-environment -0.148 0.041
Pro-transit/ridesharing -0.222 0.001
Suburbanite 0.075 0.299
Automotive mobility 0.445 0.0
Time pressure -0.138 0.048
Urban villager -0.12 0.099
TCM 0.027 0.704
Workaholic 0.12 0.095
Intercept -1.611 nan

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.

sha256:a08d9e369743bf7e6d1c40d27347318209b40a7fb1543813fdcf31b898918815