Using Google s Aggregated and Anonymized Trip Data to Support Freeway Corridor Management Planning in San Francisco

Transcription

1 Using Google s Aggregated and Anonymized Trip Data to Support Freeway Corridor Management Planning in San Francisco Bhargava Sana, Joe Castiglione, Dan Tischler, Drew Cooper SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY January 11, 2017

2 Outline Background on Freeway Corridor Management Study (FCMS) and OD data sources Google s Aggregated and Anonymized Trip (AAT) data AAT application and validation Estimate roadway facility-specific OD matrices Conclusions Limitations and next steps

3 Background: San Francisco Freeway Corridor Management Study (FCMS) US-101 I-280 Corridor Potential strategies TDM, managed lanes Ramp metering OD data could help impute Socio-demographics (age, income, auto ownership etc.) Willingness To Pay (WTP)

4 Background: OD Data Collection Conventional methods License plate surveys Roadside interviews/questionnaires Emerging passive data collection methods Bluetooth detectors Cell phone Call Detail Records (CDR) data

5 Aggregated and Anonymized Trip (AAT) Data Google s Better Cities Program Minimize congestion, improve safety and reduce infrastructure spending Users opt-in to share location GPS, cell-phone towers, Wi-Fi detectors Aggregated and Anonymized Trip (AAT) information from location reports Extract data from moving users Clean data and snap to road network Aggregate OD trip counts Apply differential privacy filters and minimum trips threshold 5

6 AAT Dataset for FCMS 85-district system: combination of Tract and County boundaries via polygons for 4 segments around US-101 and I-280 interchange in San Francisco Hourly AAT data for six months (Apr-Jun and Sep-Nov 2015) 6

7 AAT Application Considerations Flow data provided as relative trips as opposed to absolute counts Trips needed for planning purposes Convert relative flows to trips? 7

8 Caltrans Performance Measurement System (PeMS) Continuously monitors speeds and volumes using detectors Over 39,000 detectors on freeways all across California PeMS counts available for same time and location as AAT Why not compare to Google AAT flows? PeMS vehicle counts -> person trips Observed auto occupancy applied by location and time of day 8

9 Comparison to PeMS Corridor Flows Weekday Hourly Bi-Directional Flows Summary Freeway Google AAT Relative Flows PeMS (Vehs/Hr) Segment Count Mean Min Max Count Mean Min Max North on US-101 2, E E E-02 1,845 9, ,885 South on US-101 2, E E E-02 1,863 9, ,310 North on I-280 2, E E E-03 1,872 3, ,216 South on I-280 2, E E E-03 1,872 6, ,019 9

10 Comparison to SF-CHAMP (ABM) OD Flows Pearson s correlation = Pearson s correlation =

11 Relative Flow Conversion Models AAT relative flows vary by Hour of day: added indicator variables Roadway segment location: separate models estimated Several model forms and transformations explored PT t = K + β f log RF t + β t + ϵ ; 0 t 23 PT t K β f RF t β t ϵ : Person trips on roadway facility for hour-of-day t : Regression constant/intercept : Coefficient of AAT relative flow : AAT relative flow for hour-of-day t : Constant for hour-of-day t : Error term 11

12 Model Validation 12

13 Comparison of Facility-Specific OD Matrices with SF-CHAMP Select Link Correlation Coefficient Time Period Segment County level District level US-101 north of interchange AM Peak US-101 south of interchange I-280 north of interchange I-280 south of interchange US-101 north of interchange PM Peak US-101 south of interchange I-280 north of interchange I-280 south of interchange

14 Summary and Conclusions New passive dataset (AAT) obtained from Google Processed mobile users location reports Reasonable regional flow patterns High correlation with calibrated TDM flows Statistical relationship: AAT & observed counts (PeMS) Appears to vary by hour-of-day and facility Hourly facility-specific OD matrices O & D trips ends better correlated with model select link 14

15 Limitations and Next Steps Limitations Google s AAT data significantly processed to protect privacy Minimum trips threshold for reporting At the time of analysis AAT only available for vehicular traffic Analysis limited to freeway corridors AAT most likely represent person trips and not vehicle trips Auto occupancy assumptions may be needed Next Steps Compare AAT and cellphone CDR OD flows Leverage the longitudinal aspect to perform before-after studies 15

16 Thank You SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

17 Estimation Results US-101 I-280 Coefficient Std. Err. t-stat Coefficient Std. Err. t-stat Intercept log(weight) Hour Hour Hour Hour Hour Hour Hour Hour Hour Hour Hour Hour Hour Hour Hour Hour Hour Hour Hour No. of observations Log-Likelihood R-squared Adj. R-squared