Peninsula Mobility CEE224X Final Presentation

Peninsula MobilityThe Traffic Annoyance Effect

And Other Predictors for Shuttle Ridership

Team

Katherine PhanComputer Science

John ZhaoCivil / Environmental

Engineering

Shawn LeeMaterials Science

Management Science and Engineering

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

TrafficShuttle /

Bus

Research Question

“How to reduce single occupancy vehicle rates for commutes of <10 miles from an employer?”

CalTrain Station

Employment Center

10 mile

s10

mile

s

Highway

Methodology

Palo AltoStanford

Bus traffic information

Shuttle traffic information

Arrival and departure analysis

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

Methodology (Analysis)

Marguerite Shuttle Data

Palo Alto Crosstown Shuttle Data

Real-time traffic through stops and

routes

Time estimates inside and outside

traffic

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)

Results - Visualization Sketch

Route Visualization

Traffic along route

Passengers per stop

Results - Travel Time (minutes)

RouteScheduled 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

Results - Potential Correlation

No significant correlation or with only peak hour points (bottom two graphs)

Results - Potential Significance

Slope P Value

All pointsDelay Time -0.0068 0.87

Percent Delay -0.0040 0.80

Rush hour onlyDelay Time 0.0020 0.97

Percent Delay 0.0079 0.73

No significant correlation, but correlation may improve with more data

What does this mean?

No significant correlation yet

between traffic delay and shuttle

ridership

This pattern holds with all routes

and just peak hour routesWe will have a better sense of the

relationship with more peak hour

ridership data

Deficit of Disaggregated Municipal Shuttle Ridership data

Importance of Intra Regional Commuting(OnTheMap)

Explore home-to-stop and connectivity analysis

OnTheMap: Redwood City 10.6%

OnTheMap: Mountain View 13.2%

OnTheMap: Stanford 16%

OnTheMap: Menlo Park 17.3%

OnTheMap: Palo Alto 19.3%

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

Thank you!Questions?

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