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.

1. Data Analytics and
Transportation Planning
Managers Mobility Partnership
March 23rd
, 2017
Max O’Krepki, ‘18
Dan Sakaguchi, ‘18

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

4. Spatial Analysis of Commuters in the MMP

5. Methodology
Data Extraction and Cleaning
Survey
Cluster Into Groups Map and Analyze in Excel and
ArcMap

6. Large Employers In The Region Have Substantial Impact on Mobility
With Large Potential To Lead Change

7. Who and Where are the Commuters in the
MMP partner cities?

8. Stanford’s Surveyed Commuters in 2016 Live All Across the Bay

9. Stanford’s Surveyed Commuters in 2016 Live All Across the Bay

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

14. Distribution Of Cyclist In MMP Partner Cities
● Cyclists
● Transit Riders
● Carpools
● SOVs

15. Distribution Of Transit Riders In MMP Partner Cities
● Cyclists
● Transit Riders
● Carpools
● SOVs

16. Distribution Of Carpools 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

22. Feasibility of an Express Shuttle

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

27. Census Tracts With Greatest Demand For Express Shuttles

28. Location Isn’t Everything: A Variety Of Factors Explains People’s Mode
Choice
● Cyclists
● Transit Riders
● Carpools
● SOVs

29. Trying to understand commuter mode choice
-
who are Stanford’s Commuters?

30. Most of Stanford’s Commuters use SOV
SOV
Transit
Biking
Carpool

31. Employees each have different commuting behaviors
Other Teaching
CCT
Graduate TGR
Graduate
Postdoc
Professoriate
Hospital SH
Hospital LP
Staff

32. How can we determine the influence of
people’s resources on their mode choices?

33. Answer: A model

34. Suppose we model a commuter deciding
how to get to campus...

35. There are a variety of factors that will
influence this decision...
Owns home...
High income...
Has children....

36. But not all will have the same
impact on their decisions...
Owns home...
High income...
Has
children....

37. Based on these factors, they will rank
their options with a particular utility...
Owns home...
High income...
Has
children....
12
7
14
3

38. Which will give a probability
of taking each mode...
Owns home...
High income...
Has
children....
33%
20%
40%
7%

39. Of which, we assume they
will take the highest
Owns home...
High income...
Has
children....
33%
20%
40%
7%

40. The Multinomial Logit Model

41. The Multinomial Logit Model
Utility score for a given
mode
Probability of a mode
Weights for influences

42. The Data Workflow

43. EPA Smart Location
Database
Stanford P&TS
Commuter Survey
Google Maps API
Merged Dataset
commute_club_status ethn
emp_cat acad_level
emp_cat.collapsed hh_income
home_lat hh_occ_children
home_long hh_occ_other
weight hh_adults_working
work_loc other_adults_sov
live_on_campus home_type
primary_commute_mode res_ownership
commute_freq housing_cost
prim_commute_freq travel_dist
arr_time pct_0_car_hh
dep_time pct_1_car_hh
mode_influence pct_2p_car_hh
mode_shift_factor pct_low_wage
pref_commute_mode pop_density
age road_net_density
gender local_jobs_by_auto

44. commute_club_status ethn
emp_cat acad_level
emp_cat.collapsed hh_income
home_lat hh_occ_children
home_long hh_occ_other
weight hh_adults_working
work_loc other_adults_sov
live_on_campus home_type
primary_commute_mode res_ownership
commute_freq housing_cost
prim_commute_freq travel_dist
arr_time pct_0_car_hh
dep_time pct_1_car_hh
mode_influence pct_2p_car_hh
mode_shift_factor pct_low_wage
pref_commute_mode pop_density
age road_net_density
gender local_jobs_by_auto
The Merged Dataset
Residence-based
features
Personal
Demographics
Self-listed commuting
behaviors /
preferences
Employment Type +
Other

45. Data Processing with R Informative Results
Merged Dataset

46. Results

47. Biking probability is highest closest to campus

48. Biking probability is highest closest to campus
Stanford
University

49. Biking probability is highest closest to campus

50. Carpooling has low probability, mostly far from campus

51. Transit is more likely near high density areas with easy access

52. But how can we tell who exactly are ideal
commute switch candidates?

53. Commute mode is strongly influenced by distance

54. Commute mode is strongly influenced by distance
SOV
Transit
Biking
Carpool

55. People close to campus are more likely to switch to biking
0 -10 miles
Ideal candidates
to switch from:
SOV-> Biking

56. People far from campus are more likely to switch to transit
> 10 miles
Ideal candidates
to switch from:
SOV-> Transit
[*see report]

57. Let’s take a closer look at the SOV
commuters close to campus

58. Finding Clusters of Commuters

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

60. 4 Types of SOV Commuters Close to Campus

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

70. Similar techniques can be applied to employers across the region