data analytics and transportation planning

Data Analytics and Transportation Planning

Managers Mobility Partnership

March 23rd, 2017

Max O’Krepki, ‘18Dan Sakaguchi, ‘18

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

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

Spatial Analysis of Commuters in the MMP

Methodology

Data Extraction and Cleaning Survey

Cluster Into Groups Map and Analyze in Excel and ArcMap

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

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

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

Commuting In The Partner Cities Dominated By Cycling And SOVS

● Carpools ● Cyclists ● SOVs ● Transit Riders

SOV Commuters In The Partner Cities Makeup ~27% Of All Stanford SOVs Commuting To Campus

● Redwood City ● Menlo Park ● Palo Alto ● Mountain View ● Others

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

Distribution Of Surveyed Commuters In MMP Partner Cities● Cyclists● Transit Riders● Carpools● SOVs

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

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

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

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

Commuter Clusters In The Partner Cities: Cyclists● High Density Clusters● Medium-High

Density Clusters● Mean Density

Clusters● Medium-Low Density

Clusters● Low Density Clusters

Commuter Clusters In The Partner Cities: Transit Riders● High Density Clusters● Medium-High




Commuter Clusters In The Partner Cities: Carpools● High Density Clusters● Medium-High




Commuter Clusters In The Partner Cities: SOVs● High Density Clusters● Medium-High




Feasibility of an Express Shuttle

Residents In The Partner Cities Indicate A Wide Variety Of Reasons For Commuting Alone

A Variety Of Approaches Will Be Required To Shift Commuters From Driving Alone

Shuttle Demand Concentrated In Areas Where Transit Is Not As Competitive As Driving Alone And Biking Not A Feasible Option

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

Census Tracts With Greatest Demand For Express Shuttles

Location Isn’t Everything: A Variety Of Factors Explains People’s Mode Choice

● Cyclists● Transit Riders● Carpools● SOVs

Trying to understand commuter mode choice -

who are Stanford’s Commuters?

Most of Stanford’s Commuters use SOV

SOV

Transit

Biking

Carpool

Employees each have different commuting behaviorsOther Teaching

CCT

Graduate TGR

Graduate

Postdoc

Professoriate

Hospital SH

Hospital LP

Staff

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

Answer: A model

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

There are a variety of factors that will influence this decision...

Owns home...

High income...

Has children....

But not all will have the same impact on their decisions...

Owns home...

High income...

Has children....

Based on these factors, they will rank their options with a particular utility...

Owns home...

High income...

Has children....

12

7

14

3

Which will give a probability of taking each mode...

Owns home...

High income...

Has children....

33%

20%

40%

7%

Of which, we assume they will take the highest

Owns home...

High income...

Has children....

33%

20%

40%

7%

The Multinomial Logit Model

The Multinomial Logit ModelUtility score for a given mode

Probability of a mode

Weights for influences

The Data Workflow

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

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

Data Processing with R Informative Results

Merged Dataset

Results

Biking probability is highest closest to campus


Stanford University

Carpooling has low probability, mostly far from campus

Transit is more likely near high density areas with easy access

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

Commute mode is strongly influenced by distance

Commute mode is strongly influenced by distance

SOV

Transit

Biking

Carpool

People close to campus are more likely to switch to biking

0 -10 miles

Ideal candidates to switch from:

SOV-> Biking

People far from campus are more likely to switch to transit

> 10 milesIdeal candidates to switch from:

SOV-> Transit[*see report]

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

Finding Clusters of Commuters

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

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


Cluster 2

Demographics: younger, more women, lower household income, fewer children, nearly all rent their homes

Where they live: slightly closer 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


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

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

We can do similar clustering with the bikers and find how similar the groups are...

“Distance” between SOV cluster #2 and Biking

cluster #2

And then we can find the biking group that is most similar to a given SOV group

And then we can find the biking group that is most similar to a given SOV group

These are ideal candidates for further research

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

Similar techniques can be applied to employers across the region