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. Research Question
“How to reduce
single occupancy vehicle
rates for commutes of <10
miles from an employer?”
CalTrain Station
Employment
Center
10miles
10miles
Highway
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
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. Results - Potential Correlation
No significant correlation or with only peak hour points
(bottom two graphs)
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. 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. Deficit of Disaggregated Municipal Shuttle
Ridership data
Importance of Intra Regional Commuting
(OnTheMap)
Explore home-to-stop and connectivity analysis
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