Transforming Governance and Institutions for Global Sustainability. Key Insights from the Earth
Transforming Transit through Insights in Motion Insights in Motion... · 2018-03-12 ·...
Transcript of Transforming Transit through Insights in Motion Insights in Motion... · 2018-03-12 ·...
Transforming Transit through Insights in MotionTransforming Transit through Insights in Motion
Milind NaphadeSenior Manager, Smarter Cities Solutions Research IBM T. J. Watson Research Center, Yorktown Heights, New York
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Insights in Motion – Understanding Movement and Optimizing ServicesInsights in Motion – Understanding Movement and Optimizing Services
Network Data (millions of events/day)
Transit System & GIS Data
Census & Demographics Data
Analytics & Models
Smart Fare Card Data (millions of events/day)
Information Sources Business Services Outcomes
Time of Day Density Maps
Origin-Destination
Traffic Flow
Deep Analysis
Planning Large Scale Events, Emergency Response
Store Location Siting
Transit Planning
Location-based Services, Traffic Alerts, Promotions
Reduce Congestion
Reduce Journey Time
Reduce Carbon
footprint
Improve Store Traffic of
Customers
Improve Revenue
Reduce Operating Expenses
Reduce Emergency Response
Time
Insights in Motion Mobility ModelInsights 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 TransitImpact of Route changes on Jule TransitIncrease in Length of the trip ¬ designing to action areas
Decrease in Ridership
Bigger head ways Less
Reliability
Increase in operating
costs
Less Fare Box
Less FrequencyLess Frequency Negative PerceptionNegative
PerceptionFew funds to
improve systemFew funds to
improve system
Reduction in Federal Funds
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Process to Improve Jule TransitProcess to Improve Jule Transit
Optimize Transit Routes
Optimize Stop Placement
Contrast Supply vsDemand
Optimize Operations
Measure unmet demand
Suggest new bus routes
Time of Day
Activity Based
New Service area & Demand
Census Data
Traditional Surveys
Online surveys
Data gathering using
technology
X
X
XDesign new
routes
Redesign services by
time of day and activity
Create new marketing plan
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Process to Improve Jule TransitProcess to Improve Jule TransitInsights in Motion
Optimize Transit Routes
Optimize Stop Placement
Contrast Supply vsDemand
Optimize Operations
Measure unmet demand
Suggest new bus routes
Time of Day
Activity Based
New Service area & Demand
Census Data
Traditional Surveys
Online surveys
Data gathering using
technology
X
XDesign new
routes
Redesign services by
time of day and activity
Create new marketing plan
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Data C
leaning
Smartphone(GPS)
Census
Points of Interest
Meaningful LocationDetection
O/D Estimation
Trip Segregation
Supply Model
Transit& GIS
DemandModel
Clean SheetRoute Optimization
GapAnalysis
Telco Network Data
Duration of Stay Estimation
Trip Purpose Estimation
Smart FareCard (RFID)
Trip Mode Estimation
Airsage Proprietary
Analysis of Telco Network Data
OptimalRoutes
Insights in Motion
Process to fix itProcess to fix it
Phase 1: Volunteers for DevicesPhase 1: Volunteers for Devices
Phase 2: Data Collection & AnalysisPhase 2: Data Collection & Analysis
Phase 3: Route Optimization & Implementation
Phase 3: Route Optimization & Implementation
Phase 4: System CalibrationPhase 4: System Calibration
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Phase 1
Phase 2
Phase 3
Insights in Motion
Phase I:DevicesPhase I:DevicesThe 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
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Insights in Motion
Phase I:Recruitment ProcessPhase I:Recruitment Process
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Smart PhoneInformed Consent Documentation + $10 study participation incentiveRecruitment Methods:
• Point of Sale partnership with local cellular agents• Employer and Campus Events• General/Community Events
RFIDFree rides on Jule Transit during study periodRecruitment Methods:
• Community outreach events, press releases, and email marketing • On bus outreach to existing transit users
Insights in Motion
Phase I:Data from Smart PhonesPhase I:Data from Smart Phones
Acceleration
Speed
Backend– Setting up cloud based GPS
data gathering– Receive Shape file data from
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 backend10
Insights in Motion
Phase I:Data from RFIDsPhase I:Data from RFIDs
Acceleration
Speed
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Insights in Motion
Phase I:Data from Cell Tower DataPhase I:Data from Cell Tower Data
Acceleration
Speed
• Cell tower data properties
• TAZ zone based
• Include both call, 3G data and roaming records
• People flows between 7-9 AM using cell phone call data
• Regions represented by centroids
• Volume represented by line thickness
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Insights in Motion
Mode of Transportation and purposeMode of Transportation and purpose
High Low
High Vehiclebus VehiclebusOr
Vehiclecar
Median Bicycle
Low Walk
Zero Static
Acceleration
Speed
Trip Purpose
Definition
HBWork The trips from home locations to office locations.
NHBWork The trips from locations other than home to office locations.
HBSchool The trips from home locations to school locations.
NHBSchool The trips from locations other than home to school locations.
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.
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Insights in Motion
Phase II: Trip purpose distribution check Phase II: Trip purpose distribution check
Acceleration
Speed
Start Time End Time Purpose
7:27 7:39 HBW
8:35 8:47 NHBO
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,
public buildings, etc. 14
Insights in Motion
Phase II: Data from Radio Frequency Identification Device (RFID) Phase II: Data from Radio Frequency Identification Device (RFID)
Acceleration
Speed
The data is collected from August 2011 –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.
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Insights in Motion
Phase II: Smart Phone Analytics ValidationPhase 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
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* 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
Insights in Motion
Phase II: Trip Statistics based on Smart Phone O/DPhase II: Trip Statistics based on Smart Phone O/D
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Sample trip distribution and O-D statistics for Smart Phone data.
Insights in Motion
Trip Statistics based Cell-based O/DTrip Statistics based Cell-based O/D
Sample trip distribution and O-D statistics for cell tower data.
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Cell-based O/D
Insights in Motion
Phase IIIPhase III
Route Optimization & Implementation
)constraint routes of numbers maximum(
)constraintlength (trip
)constraint size(fleet
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Insights in Motion
Phase III: Route Optimization with Smart Phone DataPhase III: Route Optimization with Smart Phone Data
Acceleration
Speed
Current Routes
Clean sheet optimization to minimize opex, unmet demand and travel timeConstraints include fleet size, max transfers, duration, etc.
Clean Sheet Optimal Routes
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Current Routes
Insights in Motion
Phase III: Route Optimization with Telco DataPhase III: Route Optimization with Telco Data
Acceleration
Speed
Current Routes
Clean sheet optimization to minimize opex, unmet demand and travel timeConstraints include fleet size, max transfers, duration, etc.Optimal routes can
• reduce OPEX cost up to 40%• reduce unmet demand by 37%• reduce avg. travel time from 37 minute average to 10-22 minute average
Clean Sheet Optimal Routes
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Insights in Motion
Phase III: Pilot RoutesPhase III: Pilot Routes
Details of Nightrider•Focused on unmet evening ridership and college students.•The route was designed based on random survey of students.•Adjustment to the route will be based on the Smart phone data provided by student population.•Further this route will be adjusted based on final O/D data.
Details of Midtown Loop•Focused on existing fixed routes•The route is designed to reduce headways.•The route is in process of getting implemented.•This route will be adjusted in future by based on final O/D analytics.
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Insights in Motion
Phase IV: System CalibrationPhase IV: System Calibration
Boarding Data
Boarding data >= Target ridership
Smart Card rider
Smart Card reader Ranger
Wireless provider
Backend Server
User Data
Time of loading
Smart Card Loading location
Smart Card Reloading location
Location Data
User ID
Usage
Accounting
Analysis to determine potential ridership
Age
Income Vehicle ownership
Location of Bus stop
TAZ
Marketing
NOContinue Marketing
YES
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Did the ridership increase after change in marketing
Adjust the route
YES
NO
Istanbul in MotionIstanbul in MotionObjectives
• Create people movement models with anonymous telco data• Utilize anonymous mobile phone location information
• Build demand side models
• Work with ULASIM AS to evaluate applications of models• Evaluate existing multimodal transit system use against overall demand
• Explore opportunities to optimize multimodal transit coordination based on gaps
• Deliverables• Trip Frequency Tables• Trip Distribution Tables (Origin Destination Matrices)• Snapshots of zonal occupancy• Analysis of multimodal transit use against the backdrop of overall movement demand• Preliminary results on feeder routes for M4 line for all stations
• Outcomes• First rich large scale movement model level understanding of how Istanbul moves
• Deliverables being used by ULASIM Istanbul to plan feeder bus routes for all stations
• Deliverables will be used by all Istanbul municipal agencies in planning beyond ULASIM Istanbul.
Results – Population Density and Traffic SnapshotResults – Population Density and Traffic Snapshot
Results – Origin DestinationResults – Origin Destination
Trip Analytics: Identifying Meaningful LocationsTrip Analytics: Identifying Meaningful Locations
Where People Live Where People Work
Istanbul Movement Analysis w. Vodafone network data
• 4.7 million phones w. 3B+ events/week
• Accurate detection of home, work & meaningful locations
Trip Analytics: Understanding home-to-work commute patternsTrip Analytics: Understanding home-to-work commute patterns
How far people travel from home zones to work How far people travel to come to work zones
Results – Commuter Pain IndexResults – Commuter Pain Index