This document summarizes research analyzing commuting patterns of employees at Stanford University and within the Partnership for Mobility Management (MMP). The research team used survey and spatial data to model commute mode choice and identify groups of single-occupancy vehicle (SOV) commuters who may be open to alternative transportation. Four clusters of SOV commuters near campus were identified based on demographics and location. Biking commuters were also clustered, and some SOV clusters were found to be similar to biking clusters, identifying targets for mode shift programs. The analysis showed distance as a major factor in commute mode, with opportunities to shift nearby SOV drivers to biking and more distant ones to transit.
2. The Research Team
● Dan Sakaguchi
● Stanford ‘18 | M.S. Earth Systems
● Stanford ‘16 | B.S. Physics
● Hometown: Portland, OR
● Max O’Krepki
● Stanford ‘18 | M.S. Civil Engineering
● Virginia Tech ‘16 | B.S. Civil Engineering
● Hometown: Hammond, LA
3. Table of Contents
❏ Spatial Analysis of Commuters in the MMP
❏ Who and Where are the Commuters in the MMP?
❏ Feasibility of Local Express Shuttles in the MMP
❏ Who are Stanford’s Commuters?
❏ Modeling Commute Mode Choice
❏ Data Processing Workflow
❏ Identifying Groups of SOV Commuters
10. Commuting In The Partner Cities Dominated By Cycling And SOVS
● Carpools ● Cyclists ● SOVs ● Transit Riders
11. SOV Commuters In The Partner Cities Makeup ~27% Of All Stanford
SOVs Commuting To Campus
● Redwood City ● Menlo Park ● Palo Alto ● Mountain View ● Others
12. Despite Large SOV Share, SOV Commuters From Partner Cities Account
for ~10% Of Daily VMT By All Stanford SOVs
● Redwood City ● Menlo Park ● Palo Alto ● Mountain View ● Others
13. Distribution Of Surveyed Commuters In MMP Partner Cities
● Cyclists
● Transit Riders
● Carpools
● SOVs
17. Distribution Of SOVs In MMP Partner Cities
● Cyclists
● Transit Riders
● Carpools
● SOVs
18. Commuter Clusters In The Partner Cities: Cyclists
● High Density Clusters
● Medium-High
Density Clusters
● Mean Density
Clusters
● Medium-Low Density
Clusters
● Low Density Clusters
19. Commuter Clusters In The Partner Cities: Transit Riders
● High Density Clusters
● Medium-High
Density Clusters
● Mean Density
Clusters
● Medium-Low Density
Clusters
● Low Density Clusters
20. Commuter Clusters In The Partner Cities: Carpools
● High Density Clusters
● Medium-High
Density Clusters
● Mean Density
Clusters
● Medium-Low Density
Clusters
● Low Density Clusters
21. Commuter Clusters In The Partner Cities: SOVs
● High Density Clusters
● Medium-High
Density Clusters
● Mean Density
Clusters
● Medium-Low Density
Clusters
● Low Density Clusters
23. Residents In The Partner Cities Indicate A Wide Variety Of Reasons For
Commuting Alone
24. A Variety Of Approaches Will Be Required To Shift Commuters From
Driving Alone
25. Shuttle Demand Concentrated In Areas Where Transit Is Not As
Competitive As Driving Alone And Biking Not A Feasible Option
26. Even a Small Fleet of Shuttles or Vans Could Have a Sizeable Impact
● A look into the survey respondents that drive alone residing in the partner cities indicating interest an interest in
a shuttle
○ 19% of all SOVS in the Partner Cities
■ Generating an average 14 VMT/person each day
○ Accounts for ~10,000 daily VMT or 44% of all daily VMT generated by residents in the partner cities
● The Impact of one shuttle or van
○ Each 15 passenger vehicle would
■ Reduce daily VMT by 189 miles
■ Reduce CO2
emissions by 0.08 metric tons each day
59. The Average SOV Commuter Close to Campus
Demographics: 43 years old, 31.3% male, 63% white, some college education,
$147,000 household income, about .7 children, about 50% rent their homes
Where they live: 5 miles from campus, average neighbor has 1.6 cars, medium
population density
61. 4 Types of SOV Commuters Close to Campus
Cluster 1
Demographics: older, more women, higher household
income, more children, nearly all own their homes
Where they live: slightly lower density neighborhood
62. 4 Types of SOV Commuters Close to Campus
Cluster 2
Demographics: younger, more women, lower household
income, fewer children, nearly all rent their homes
Where they live: slightly closer to campus
63. 4 Types of SOV Commuters Close to Campus
Cluster 3
Demographics: younger, more men, much lower household
income, nearly all rent their homes
Where they live: fewer neighbors own cars, higher proportion
of neighbors are low wage workers, much higher population
density
64. 4 Types of SOV Commuters Close to Campus
Cluster 4
Demographics: older, more men, whiter, more educated,
much higher income, more children, most likely own home
Where they live: slightly further from campus, more
neighbors own cars, very low population density
65. The Average Biker Close to Campus
Demographics: 37 years old, 47% male, 68% white, most have college education,
$120,000 household income, about .5 children, about 80% rent their homes
Where they live: 3 miles from campus, average neighbor has 1.5 cars, medium
population density
SOV: 43 years old, 31.3% male, 63% white, some college education,
$147,000 household income, about .7 children, about 50% rent their homes]
SOV: 5 miles from campus, average neighbor has 1.6 cars, medium
population density
66. We can do similar clustering with the bikers and
find how similar the groups are...
“Distance” between SOV
cluster #2 and Biking
cluster #2
67. And then we can find the biking group that is most
similar to a given SOV group
68. And then we can find the biking group that is most
similar to a given SOV group
These are ideal candidates for further research
69. Take-Aways
❏ Modeling can provide broad insight into the relative importance of different
demographics / resources
❏ Clustering techniques can be useful for segmenting a diverse group of commuters
❏ Similar modeling / data analysis can be conducted for other similar institutions to
Stanford