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

2

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 &not 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

4

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

5

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

6

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

7

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

8

Insights in Motion

Phase I:Recruitment ProcessPhase I:Recruitment Process

9

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

11

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

12

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.

13

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.

15

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

16

* 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

17

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.

18

Cell-based O/D

Insights in Motion

Phase IIIPhase III

Route Optimization & Implementation

)constraint routes of numbers maximum(

)constraintlength (trip

)constraint size(fleet

)constraintfactor (load*

)constrainty feasibilit(headway s.t.

*

***

*min

max

maxmin

11

max

max

maxmin

3

12

1

RM

RrDDD

RrWhT

N

RrLP

hQL

Rrhhh

dddC

hT

OCC

tdtdCz

mr

m

M

m r

rM

mr

mrr

r

mr

Ni Nj TRtr

trij

Ni Nj DRr

rij

Ni Njij

M

m r

rvv

Ni Nj TRtr

trij

rij

Ni Nj DRr

rij

rij

m

m

m

m

mm

m

m

ijijm

m

m

m

ij

m

ijm

mm

19

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

20

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

21

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.

22

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

23

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