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Comparison of Cell, GPS, and Bluetooth Derived Travel Data Results from the 2014 Tyler, Texas Study Texas A&M Big Data Workshop February 13, 2015 Ed Hard.

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Presentation on theme: "Comparison of Cell, GPS, and Bluetooth Derived Travel Data Results from the 2014 Tyler, Texas Study Texas A&M Big Data Workshop February 13, 2015 Ed Hard."— Presentation transcript:

1 Comparison of Cell, GPS, and Bluetooth Derived Travel Data Results from the 2014 Tyler, Texas Study Texas A&M Big Data Workshop February 13, 2015 Ed Hard Byron Chigoy Praprut Songchitruksa, Ph.D, P.E. Steve Farnsworth Darrell Borchardt, P.E.

2 Comparison of O-D Data by Technology Origin-Destination (O-D) 2 Technology Comparison Cellular GPS Data Stream Bluetooth Data Unitcell ‘sighting’GPS pingMAC address Type of Travel Collectedmovements /flowstrips traces trips between device readers Data Saturation/Penetrationgoodpoorfair Positional Accuracy150–500 meters5–30 meters100 meters Sample Frequencyminutes, hours seconds, minutes seconds Continuous Data Stream?noyes Is it Big Data?yessometimesno

3 Study Area and Overview 3 Tyler MPO, Smith County, Texas Conducted Spring 2014 Focused on: – external trips, E-E, E-I/I-E – average weekday trips Trial external O-D travel survey using cell, GPS, Bluetooth (BT) Tyler

4 Data Collection/Capture Time Periods 4 Bluetooth - 2 weeks, April 1-April 14 Cell - 4 weeks, March 21-April 24 GPS - 3 months, February 24 – May 9

5 Study Area Zones and Capture Areas 5 420 MPO zones aggregated to 307 cell capture zones 18 Exterior cell data capture areas created 10 mile GPS buffer area utilized

6 Bluetooth Detection and E-E Matching 6 Over 170,000 BT observations during study period – 24,500 ave. weekday E-E matches – 4,000 per ave. weekday matches with time constraints BT detection ranged from 4% to 11% Matches expanded to counts

7 Cell Data Processing, Analyses 7 198,000 unique resident devices; 17% residential sampling rate Ave. of 180 device sightings per day Removed trips that did not cross study boundary For E-E: developed trip matrix, counts by station, percent resident vs non-residents by station For E-I/I-E: developed matrix, trip length frequency distributions (TLFD)

8 GPS Data Processing, Analyses 8 Raw GPS data processed to develop O-D trips Analysis incorporated anonymization O-D datasets developed for freight, cars, and apps Developed E-E, E-I/I-E trips and count totals by station Same E-E time constraints for GPS as used for Bluetooth

9 External-to-External Results All Vehicles 9

10 E-E Results 10

11 E-I/I-E Results – Total Trips Saturation/Distribution across Internal TAZs Max Value = 13,500 Max Value = 4,900 Max Value = 3,500 GPS Data 2004 Survey Data Cell Data 11

12 E-I/I-E Trip Length All Stations – All Vehicles 12 K-S Test p-value << 0.01

13 Conclusions Highly Summarized 13 O-D methods/technologies still evolving Combination of technologies providers best approach for external data (currently) Bluetooth remains E-E benchmark, for time being Cell data better suited for larger studies areas Third party GPS appears to be viable option – especially as sample sizes increase – more trials needed

14 For more Information: Ed Hard e-hard@tamu.edu (979)845-8539 Byron Chigoy b-chigoy@ttimail.tamu.edu (512)407-1156 Praprut Songchitruksa, Ph.D., P.E. praprut@tamu.edu (979)862-3559 Steve Farnsworth s-farnsworth44@tamu.edu (979)862-4927 Questions? 14

15 Acknowledgments and Special Thanks! 15 Bill Knowles Janie Temple Charlie Hall Bill King Vijay Sivaraman Rick Schuman Andrew Davies


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