Transforming Transit through Transforming Transit through Insights in Motion Insights in Motion Milind Naphade Senior Manager, Smarter Cities Solutions Research IBM T. J. Watson Research Center, Yorktown Heights, New York
Insights in Motion – Understanding Movement and Optimizing Services Insights in Motion – Understanding Movement and Optimizing Services Analytics & Models Business Services Information Sources Outcomes Planning Large Scale Events, Time of Day Emergency Response Network Data (millions of Density Maps events/day) Reduce Congestion Reduce Journey Time Smart Fare Card Data (millions of events/day) Traffic Flow Transit Planning Reduce Carbon footprint Improve Store Transit System & Traffic of GIS Data Customers Improve Origin-Destination Store Location Siting Revenue Reduce Operating Expenses Census & Demographics Data Deep Analysis Reduce Emergency Location-based Services, Response Traffic Alerts, Promotions Time 2
Insights in Motion Mobility Model Insights in Motion Mobility Model Individual and Group Mobility Model • Location and movement pattern (space, time) • Meaningful location detection • Meaningful location classification • Trip purpose • Estimated Duration of stay • Estimated Duration of travel • Mode of travel • Calling patterns • Detecting tourist patterns • Detecting student patterns • Estimated demographic profile of user of phone • Anomalies in regular patterns • Supply Demand Gap Analysis • Bus Route Optimization for Small and Medium sized Cities • Feeder Route Optimization for Multimodal Transit •
Impact of Route changes on Jule Transit Impact of Route changes on Jule Transit Increase in Length of the trip & not designing to action areas Increase in Bigger operating Less head ways costs Reliability Less Frequency Negative Few funds to Less Frequency Negative Few funds to Perception improve system Perception improve system Reduction in Decrease in Less Fare Box Federal Funds Ridership 4
Process to Improve Jule Transit Process to Improve Jule Transit Contrast X Supply vs Census Data Demand Time of Day Redesign Optimize services by Transit Routes X time of day and Traditional activity Optimize Stop Surveys Placement Activity Based X Optimize Design new Online surveys Operations routes Measure unmet Data gathering Create new demand New Service using marketing plan area & Demand technology Suggest new bus routes 5
Insights in Motion Process to Improve Jule Transit Process to Improve Jule Transit Contrast Supply vs Census Data Demand Time of Day Redesign Optimize services by Transit Routes X time of day and Traditional activity Optimize Stop Surveys Placement Activity Based X Optimize Design new Online surveys Operations routes Measure unmet Data gathering Create new demand New Service using marketing plan area & Demand technology Suggest new bus routes 6
Insights in Motion Process to fix it Process to fix it Phase 2 Smartphone Data Cleaning (GPS) Telco Phase 1: Volunteers Phase 1: Volunteers Meaningful Location Network Data Detection for Devices for Devices Smart Fare Card (RFID) Phase 1 Trip Segregation Airsage Proprietary Phase 2: Data Phase 2: Data Analysis of Telco Duration of Stay Network Data Collection & Analysis Collection & Analysis Estimation Trip Mode Points of Trip Purpose O/D Estimation Estimation Interest Estimation Phase 3: Route Phase 3: Route Optimization & Optimization & Demand Census Implementation Model Implementation Phase 3 Transit Supply Gap & GIS Model Analysis Phase 4: System Phase 4: System Calibration Calibration Clean Sheet Optimal Routes Route Optimization 7
Insights in Motion Phase I:Devices Phase I:Devices The goal is to eliminate active user input, and automatically identify travel mode and trip purpose by using mobile devices and information techniques Smart phones (Androids & Blackberries) are used to provide location, acceleration and route used by time of day. Sample size : 1,000 Volunteers Radio Frequency Identification Device (RFID) are used to capture transit trips. Sample size : 500 Volunteers Cell Tower Data has been acquired from Airsage. Sample size : 15,000+ phones for 3 months 8
Insights in Motion Phase I:Recruitment Process Phase I:Recruitment Process Smart Phone Informed Consent Documentation + $10 study participation incentive Recruitment Methods: • Point of Sale partnership with local cellular agents • Employer and Campus Events • General/Community Events RFID Free rides on Jule Transit during study period Recruitment Methods: • Community outreach events, press releases, and email marketing • On bus outreach to existing transit users 9
Insights in Motion Phase I:Data from Smart Phones Phase I:Data from Smart Phones Backend Acceleration – Setting up cloud based GPS data gathering – Receive Shape file data from Speed city – Receive link for dynamic alerts to be provided to consumers – Hosting of application (for OTA installs) Application – Blackberry platform – Android platform Pull-based interaction – Application anonymously uploads location data – Battery-optimized sampling – Alerts and messages pulled by application from backend 10
Insights in Motion Phase I:Data from RFIDs Phase I:Data from RFIDs Acceleration Speed 11
Insights in Motion Phase I:Data from Cell Tower Data Phase I:Data from Cell Tower Data • Cell tower data properties Acceleration • TAZ zone based • Include both call, 3G data and Speed roaming records • People flows between 7-9 AM using cell phone call data • Regions represented by centroids • Volume represented by line thickness 12
Insights in Motion Mode of Transportation and purpose Mode of Transportation and purpose Trip Definition Acceleration Purpose High Low HBWork The trips from home locations to office locations. Speed NHBWork The trips from locations other than home to office locations. High Vehicle bus Vehicle bus HBSchool The trips from home locations to Or Median Bicycle school locations. Vehicle car NHBSchool The trips from locations other Low Walk than home to school locations. Zero Static HBShop The trips from home locations to shopping areas. NHBShop The trips from locations other than home to shopping areas. HBOther The trips from home locations to other locations. NHBOther The trips from locations other than home to other locations. 13
Insights in Motion Phase II: Trip purpose distribution check Phase II: Trip purpose distribution check Acceleration Start Time End Time Purpose 7:27 7:39 HBW 8:35 8:47 NHBO Speed 9:30 9:55 NHBW 10:56 11:15 NHBO 11:23 11:31 NHBW 12:22 12:33 NHBO 12:53 13:09 HBW 14:00 14:04 NHBO 14:29 14:48 NHBW 19:28 19:47 NHBO Points of interest: Businesses, retail, hospitals, schools, 14 public buildings, etc.
Insights in Motion Phase II: Data from Radio Frequency Identification Phase II: Data from Radio Frequency Identification Device (RFID) Device (RFID) Acceleration The data is collected from August 2011 – Speed April 2012. There are 43,025 RFID traces with 5,019 RFID traces with duration less than 5 minutes. Moreover, there are 3,002 RFID traces with duration exactly equal to 60 minutes and 35,004 RFID traces with duration >=5 minutes and < 60 minutes; 468 unique RFID tags. 15
Insights in Motion Phase II: Smart Phone Analytics Validation Phase II: Smart Phone Analytics Validation •#Volunteers maintaining detailed diaries: 7 •Duration of diaries: 7 or more consecutive days •Accuracy of detecting meaningful location visited: 93% •Average distance between detected vs. actual home: 0.06 miles •Average distance between detected vs. actual work: 0.25 miles* •Accuracy of trip detection: 96% •Larger number of trips in diaries occur: In the afternoon * Most of this error is due to the mismatch between “GPS coordinates of the postal address of work versus actual location of entry vs. exit 16
Insights in Motion Phase II: Trip Statistics based on Smart Phone O/D Phase II: Trip Statistics based on Smart Phone O/D Sample trip distribution and O-D statistics for Smart Phone data. 17
Insights in Motion Trip Statistics based Cell-based O/D Trip Statistics based Cell-based O/D Sample trip distribution and O-D statistics for cell tower data. Cell-based O/D 18
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