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Data Analytics and Transportation Planning

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CEE 224Y Winter 2017
Peninsula Last Mile Mobility Project
Dan Sakaguchi and Max O'Krepki

Published in: Engineering
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Data Analytics and Transportation Planning

  1. 1. Data Analytics and Transportation Planning Managers Mobility Partnership March 23rd , 2017 Max O’Krepki, ‘18 Dan Sakaguchi, ‘18
  2. 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. 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. 4. Spatial Analysis of Commuters in the MMP
  5. 5. Methodology Data Extraction and Cleaning Survey Cluster Into Groups Map and Analyze in Excel and ArcMap
  6. 6. Large Employers In The Region Have Substantial Impact on Mobility With Large Potential To Lead Change
  7. 7. Who and Where are the Commuters in the MMP partner cities?
  8. 8. Stanford’s Surveyed Commuters in 2016 Live All Across the Bay
  9. 9. Stanford’s Surveyed Commuters in 2016 Live All Across the Bay
  10. 10. Commuting In The Partner Cities Dominated By Cycling And SOVS ● Carpools ● Cyclists ● SOVs ● Transit Riders
  11. 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. 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. 13. Distribution Of Surveyed Commuters In MMP Partner Cities ● Cyclists ● Transit Riders ● Carpools ● SOVs
  14. 14. Distribution Of Cyclist In MMP Partner Cities ● Cyclists ● Transit Riders ● Carpools ● SOVs
  15. 15. Distribution Of Transit Riders In MMP Partner Cities ● Cyclists ● Transit Riders ● Carpools ● SOVs
  16. 16. Distribution Of Carpools In MMP Partner Cities ● Cyclists ● Transit Riders ● Carpools ● SOVs
  17. 17. Distribution Of SOVs In MMP Partner Cities ● Cyclists ● Transit Riders ● Carpools ● SOVs
  18. 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. 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. 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. 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. 22. Feasibility of an Express Shuttle
  23. 23. Residents In The Partner Cities Indicate A Wide Variety Of Reasons For Commuting Alone
  24. 24. A Variety Of Approaches Will Be Required To Shift Commuters From Driving Alone
  25. 25. Shuttle Demand Concentrated In Areas Where Transit Is Not As Competitive As Driving Alone And Biking Not A Feasible Option
  26. 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. 27. Census Tracts With Greatest Demand For Express Shuttles
  28. 28. Location Isn’t Everything: A Variety Of Factors Explains People’s Mode Choice ● Cyclists ● Transit Riders ● Carpools ● SOVs
  29. 29. Trying to understand commuter mode choice - who are Stanford’s Commuters?
  30. 30. Most of Stanford’s Commuters use SOV SOV Transit Biking Carpool
  31. 31. Employees each have different commuting behaviors Other Teaching CCT Graduate TGR Graduate Postdoc Professoriate Hospital SH Hospital LP Staff
  32. 32. How can we determine the influence of people’s resources on their mode choices?
  33. 33. Answer: A model
  34. 34. Suppose we model a commuter deciding how to get to campus...
  35. 35. There are a variety of factors that will influence this decision... Owns home... High income... Has children....
  36. 36. But not all will have the same impact on their decisions... Owns home... High income... Has children....
  37. 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. 38. Which will give a probability of taking each mode... Owns home... High income... Has children.... 33% 20% 40% 7%
  39. 39. Of which, we assume they will take the highest Owns home... High income... Has children.... 33% 20% 40% 7%
  40. 40. The Multinomial Logit Model
  41. 41. The Multinomial Logit Model Utility score for a given mode Probability of a mode Weights for influences
  42. 42. The Data Workflow
  43. 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. 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. 45. Data Processing with R Informative Results Merged Dataset
  46. 46. Results
  47. 47. Biking probability is highest closest to campus
  48. 48. Biking probability is highest closest to campus Stanford University
  49. 49. Biking probability is highest closest to campus
  50. 50. Carpooling has low probability, mostly far from campus
  51. 51. Transit is more likely near high density areas with easy access
  52. 52. But how can we tell who exactly are ideal commute switch candidates?
  53. 53. Commute mode is strongly influenced by distance
  54. 54. Commute mode is strongly influenced by distance SOV Transit Biking Carpool
  55. 55. People close to campus are more likely to switch to biking 0 -10 miles Ideal candidates to switch from: SOV-> Biking
  56. 56. People far from campus are more likely to switch to transit > 10 miles Ideal candidates to switch from: SOV-> Transit [*see report]
  57. 57. Let’s take a closer look at the SOV commuters close to campus
  58. 58. Finding Clusters of Commuters
  59. 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. 60. 4 Types of SOV Commuters Close to Campus
  61. 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. 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. 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. 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. 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. 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. 67. And then we can find the biking group that is most similar to a given SOV group
  68. 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. 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. 70. Similar techniques can be applied to employers across the region

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