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Peninsula Mobility CEE224X Final Presentation


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Students: Katherine Phan, Shawn Lee, John Zhao
Mentor: Glenn Katz
CEE 224X, Fall 2016, Sustainable Urban Systems, Stanford University

Published in: Engineering
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Peninsula Mobility CEE224X Final Presentation

  1. 1. Peninsula Mobility The Traffic Annoyance Effect And Other Predictors for Shuttle Ridership
  2. 2. Team Katherine Phan Computer Science John Zhao Civil / Environmental Engineering Shawn Lee Materials Science Management Science and Engineering
  3. 3. Motivation ● Midterm presentation, expressed interest in the overlap between traffic data and bus/shuttle ○ Tackling congestion on local roads ● Team refined focus even more: ○ Focusing on commute trips ○ SOV trips <10 mi of big employer Traffic Shuttle / Bus
  4. 4. Research Question “How to reduce single occupancy vehicle rates for commutes of <10 miles from an employer?” CalTrain Station Employment Center 10miles 10miles Highway
  5. 5. Methodology Palo Alto Stanford Bus traffic information Shuttle traffic information Arrival and departure analysis
  6. 6. Methodology (Analysis) Marguerite Shuttle Data Palo Alto Crosstown Shuttle Data Arrival times per stop (time and date) Routes and stops Ridership (boarding) information Relationship between traffic and shuttle ridership
  7. 7. Methodology (Analysis) Marguerite Shuttle Data Palo Alto Crosstown Shuttle Data Real-time traffic through stops and routes Time estimates inside and outside traffic
  8. 8. Sample Output Palo Alto Crosstown shuttle stop locations Palo Alto Crosstown shuttle arrival and departure times Travel time (without traffic) Correlation between traffic delay and shuttle ridership Palo Alto Crosstown shuttle boarding count per stop Travel time (with traffic)
  9. 9. Results - Visualization Sketch Route Visualization Traffic along route Passengers per stop
  10. 10. Results - Travel Time (minutes) Route Scheduled Shuttle Travel Time Total Car Travel Time No Traffic Non-Peak Traffic Peak Traffic Marguerite AE-F 19.00 12.47 13.73 14.04 Marguerite U 9.00 5.23 6.13 6.51 Crosstown 50 32.77 36.28 38.77
  11. 11. Results - Potential Correlation No significant correlation or with only peak hour points (bottom two graphs)
  12. 12. Results - Potential Significance Slope P Value All points Delay Time -0.0068 0.87 Percent Delay -0.0040 0.80 Rush hour only Delay Time 0.0020 0.97 Percent Delay 0.0079 0.73 No significant correlation, but correlation may improve with more data
  13. 13. What does this mean? No significant correlation yet between traffic delay and shuttle ridership This pattern holds with all routes and just peak hour routes We will have a better sense of the relationship with more peak hour ridership data
  14. 14. Deficit of Disaggregated Municipal Shuttle Ridership data Importance of Intra Regional Commuting (OnTheMap) Explore home-to-stop and connectivity analysis
  15. 15. OnTheMap: Redwood City 10.6%
  16. 16. OnTheMap: Mountain View 13.2%
  17. 17. OnTheMap: Stanford 16%
  18. 18. OnTheMap: Menlo Park 17.3%
  19. 19. OnTheMap: Palo Alto 19.3%
  20. 20. Future Work 1. More granular data from all partners 2. More predictors a. Distance from stop to work b. Distance from home to stop c. Connections (also connectivity analysis) d. Inconsistency of travel time e. Frequency of shuttles 3. Primary surveys on transit systems (IRB) 4. Make a case for potential policy interventions
  21. 21. Thank you! Questions?
  22. 22. Managers Mobility Partnership ● Agreement to collaborate on transportation issues regionally Redwood City Palo Alto Mountain View Stanford Menlo Park ● Data sharing commitment ● Cross jurisdictional collaboration potential