Cell Phone Location Data for Travel Behavior Analysis
Thursday, August 2, 20182:00-4:00 PM ET
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Purpose
Discuss research from the National Cooperative Highway Research Program(NCHRP)’s Research Report 868: Cell Phone Location Data for Travel Behavior Analysis.
Learning Objectives
At the end of this webinar, you will be able to:• Understand the extent to which CDR data, GPS data, and cell phone app-based
data can augment or replace traditional means of data collection• Understand how the lessons learned from traditional data and methods apply
to, and can help, inform new sources of data and emerging analysis methods• Discuss how the different individual components or aspects of regional models
may be supported by each of these new forms of data• Understand whether CDR data, GPS data, and cell phone app-based data can
be a unique data source for these model components
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presented to presented by
TRB Webinar
August 2, 2018
Dan Beagan Shan Jiang Anurag Komanduri Kimon Proussaloglou
NCHRP Project 08-95
NCHRP Research Report 868
Cell Phone Location Data for Travel Behavior Analysis
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NCHRP Research Report 868Cell Phone Location Data
for Travel Behavior Analysis NCHRP Project 08-95
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NCHRP is a State-Driven Program
» Sponsored by individual state DOTs who
Suggest research of national interest
Serve on oversight panels that guide the research
» Administered by TRB in cooperation with the Federal Highway Administration.
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Practical, ready-to-use results
» Applied research aimed at state DOT practitioners
» Often become AASHTO standards, specifications, guides, syntheses
» Can be applied in planning, design, construction, operations, maintenance, safety, environment
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The TeamTRB Staff» Larry Goldstein» Anthony Avery
Peer Review Panel» Rebekah Straub Anderson» Michael Cohen» Tae-Gyu Kim » Guy Rousseau» Erik Sabina» Reginald Souleyrette» Sarah Sun » Kermit Wies» Fang Yuan
CS Project Team
MIT» Prof. Marta Gonzalez» Dr. Shan Jiang
CS» Dan Beagan» Anurag Komanduri» Kimon Proussaloglou
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Today’s Speakers
Shan Jiang, Massachusetts Institute of Technology
Dan Beagan, Cambridge Systematics, Inc.
Anurag Komanduri, Cambridge Systematics, Inc.
Moderated by: Kimon Proussaloglou, Cambridge Systematics, Inc.
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Project Objectives» Potential uses of cell phone data» Key research elements Definition and nature of CDR data Locational accuracy and inferences Cell use and cell technology
» Boston Case Study Multi-level comparisons Data, models, and CDR analysis
» Guidebook for practitioners evaluating cell data Potential as a source of data Planning and modeling support
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Table of Contents3. Travel Behavior from Cell Phone Data4. A Planner’s View of Cell Phone Data
5. Description of Raw Data6. Extraction of Daily Trajectories
7. Measuring Individual Activities
Appendix B. References
8. Trips by Purpose and Time of Day9. Model Comparison: O-D Trips
10. Guidelines for Practitioners
Appendix A. Glossary
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Comparative Analysis
Existing Data Sources CDR Model 1 CDR Model 2 Vendor Data
2010 Massachusetts Survey
1991 Boston Travel Survey
2007 Boston MPO Model
2010 Boston MPO Model
2009 NHTS / ACS
CDR Models versus
Model Outputs, Surveys, and
NHTS/CTPP data
CDR Models 1 and 2 versus Vendor Data
» Spatial and Temporal Variables: Total Travel Trips by Purpose Trips by Time of Day Trips by Geography
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Key Points
» Traces from Call Detail Records (CDR) Anonymized data and passive data collection No identifying or socioeconomic information
» Inference of travel flows from CDR data» Analysis of raw CDR data and vendor dataset» The difficulty of establishing “ground truth”» Questions we ask about data and models» Insights about strengths and weaknesses» Data and modeling uses of CDR data
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Presentation Outline
» Traditional models in the planning process How are models used?
» Translating CDR traces to activities and stays A “peek” into the black box
» Comparing CDR data with surveys and models Some key insights
» Closing thoughts What we learned and the way forward
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Travel Demand Models
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What is a Travel Demand Model?
» Computes implications of the economy on the transportation system Economy represented by Socioeconomic Data by location Demand and performance are volumes and times/speeds
on the transportation system
» Unless a feedback loop exists, models will not forecast how demand and performance might change the economy Transportation performance (e.g., cost) assumed in
economic or land use model should be consistent with outputs of travel demand model
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Why use a Travel Demand Model?
» Transportation systems are expensive Cost of projects, program, policies
» Transportation systems are enduring May take many years to build Once built may be in place for many decades.
» Transportation systems are complex Travelers on the transportation systems are numerous The reasons that they travel are varied The location of travel activities are numerous and varied
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Use of Travel Demand Models
» Demand (e.g., ridership and volumes) Usage of facilities
» Performance Congestion Emissions Energy consumption
» Who is impacted Characteristics of travelers who are impacted Characteristics of those near the transportation systems Type of trip impacted
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What is a Travel Demand Model?
» Network Links are typically grouped into modal networks (e.g., transit, highway)
» Trip tables Table of trips, between an origin I and a destination J Flow units and time period in table need to be consistent with flow
units of network (e.g. convert passengers to vehicles, annual to daily)
» Assignment Very large number of links and zones (i.e. to avoid “lumpy loading”) Number of zones and links makes the “Probability” file very large
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𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 = �𝑙𝑙,𝑗𝑗
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑙𝑙,𝑗𝑗 ∗ 𝑃𝑃𝑇𝑇𝑉𝑉𝑃𝑃𝑙𝑙,𝑗𝑗,𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙
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What is a Travel Demand Model?
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𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 = �𝑙𝑙,𝑗𝑗
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑙𝑙,𝑗𝑗 ∗ 𝑃𝑃𝑇𝑇𝑉𝑉𝑃𝑃𝑙𝑙,𝑗𝑗,𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙
º Surveyº Trip basedº Tour based
(e.g., Activity Based Model; cargo supply chain)
Trip Tables
º Diversionº Free Flowº Equilibriumº Dynamic
Assignment
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Trip Table component of Models
» Characteristics of trips in the table Stops, by time and location Purposes (activities at origin and at destination) Characteristics of traveler (income, age, household
type) Mode (auto driver, auto passenger, commuter rail
rider, bus rider, cyclist, pedestrian, etc.)
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Models to Create Trip Tables
» Trip Based Trip Generation Trip Distribution Mode Choice
» Tour Based Synthesis of tours Location and time of next stop on tour End of tour
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Data needed to estimate models» Surveys/diaries Expensive Logistically complex Traditional methods are harder to do Sparse Age of data
» Big Data Pervasive Continuous For purposes of this NCHRP project, limited to cell
phone CDRs
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Cell Phones as a Data Source
» What do “Call Detail Records, CDRs” include?
» What triggers the recording of locations? Active signals – user makes a call, sends a text, or visits web Passive signals – user receives a calls or a text Passive signals also received from apps accessing the phone
» What is the location information and its accuracy? Cell tower vs. more accurate location via triangulation Frequency of location signal transmission
» How are raw CDR data analyzed and expanded?
» How do we gain individual travel behavior insights?
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Understanding Travel Using CDR Data
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A Closer Look at Cell Phone Data
CDR activities for all sampled user in Boston
in 2 months
in 2 months
A Snapshot of Triangulated Cell Phone Data in One Sample Day in Boston
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A Closer Look at Cell Phone Data
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Extraction of Daily TrajectoriesExtracting Point-based Stay, Pass-by and Potential Stay
(a) Raw cell phone records as input ●
(b) Reduce noisy jumps ● roaming distance : 300 meter cluster spatially close points
(c) Detect “Stay” ● & “Pass-by” areas ○ time duration: 10 minutes
(d) Detect “Potential Stay” areas ▲ extract distinct “Stay” area as destinations; flag pass-by collocating with any of the
destinations as “potential stay”.
Ref: Hariharan, R. and Toyama, K. 2004. Jiang, S., Fiore, G. A., Yang, Y., Ferreira, J., Frazzoli, E., Gonzalez, M.C. 2013.
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Extraction of Daily TrajectoriesStay Points and Stay Regions
Stay Point
s1
s2 s3
Stay Points (s1)phone records made when engaging activities» Roaming distance:
300 meter» Time duration:
10 minutes
Ref: Jiang, S., Fiore, G. A., Yang, Y., Ferreira, J., Frazzoli, E., Gonzalez, M.C. 2013.
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Extraction of Daily TrajectoriesStay Points and Stay Regions
s3
Stay Point
s1
s2 s3r1
Stay Region
Stay Points (s1)phone records made when engaging activities» Roaming distance:
300 meter» Time duration:
10 minutes
Stay Region (r1)to cluster stay points that are close in space but far in time
Ref: Jiang, S., Fiore, G. A., Yang, Y., Ferreira, J., Frazzoli, E., Gonzalez, M.C. 2013.
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Measuring Individual Activities Home, Work, and Other
» A phone user’s “home” is measured as the most frequently visited location during nights of weekdays & days of weekends Night time: a parameter (e.g., from 7 pm to 8 am)
» A phone user’s “work” is measured as Model 1: A stay to which a user travels the maximum total distance from home
max (d x n), n: total number of visits to a given stay during weekday daytime d: straight-line distance between the home and the stay
Model 2: the most frequently visited stay location during working hours of the weekday
» A phone user’s “other” is measured as the rest stay-points
s1s2
s3
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Measuring Individual Activities Home, Work, and Other
Ref: Alexander, L., Jiang, S., Murga, M., and Gonzalez, M.C. 2015.
Expansion Factors for Trip Productions and Attractions
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CDR CTPP
Measuring Individual Activities Home, Work, and Other
Ref: Alexander, L., Jiang, S., Murga, M., and Gonzalez, M.C. 2015.
Comparison of Commuting (Home-to-Work) Flows in Space
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Trips by Purpose and Time of Day
» Trip Purposes » Time-of-Day Home-based work: HWB Home-based other: HBO Non-home based: NHB
AM: 6am - 9am MD: 9am - 3pm PM: 3pm - 7pm RD: 7pm - 6am
A trip departure time is estimated based on observed current arrival time, duration and next arrival time an empirical conditional probability of hourly departureo Model 1: derived from national household travel surveyo Model 2: derived from aggregated CDR temporal activity patterns
» Departure Time Estimation
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Trips by Purpose and Time of DayHBW Trips HBO Trips
NHB Trips All Trips
Ref: Alexander, L., Jiang, S., Murga, M., and Gonzalez, M.C. 2015.
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Trip Estimation ComparisonCDR Estimates
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Raw Cell Phone Data Census Data
Geographic Boundary
Sta
y &
Pas
s-by
E
xtra
ctio
n
Act
ivity
D
etec
tion
Daily Mobility Network (Motif) Detection
Use
r-day
Fi
lterin
gExpansion
Factors
Population, Daily Travel and Motif Expansion, Aggregation and Visualization
to Identify Areas for Improvement
Future ResearchFrom Trip-based to Activity-based EstimationsMore Applications to Improve Mobility and Infrastructure
Ref: Jiang, S., Ferreira, J., and Gonzalez, M.C. 2017.
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Future ResearchFrom Aggregated Trip Models to Disaggregated Individual Mobility Model and Simulation
Ref: Jiang, S., Yang, Y., Gupta, S., Veneziano, D., Athavale, S., & González, M.C. 2016.
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Evaluating CDR Data and Results
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Sampling and Expansion
Source: Cambridge Systematics, Inc.
Variables Traditional Surveys CDR DataSAMPLING RATE Between 0.5% and 2% Between 15% and 35%
SAMPLING STRATA
Unit of Analysis Individual and household Cell phone
Sampling by Geography Can be fine-grained Feasible at aggregate level
Sampling by Market Segment Yes N/A
SOCIOECONOMIC INFORMATION
Respondent Attributes Rich data Location of cell phone
Household Attributes Rich data N/A
SURVEY EXPANSION
Sampling Rate and Size Careful sampling/expansion Robust sample sizes
Household Attributes Used in expansion N/A
Geography Attributes Often at county-level Some expansion feasible
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Recording Travel
Source: Cambridge Systematics, Inc.
Variables Traditional Surveys CDR Data
TOTAL DAILY TRAVELRisk of under-reporting Passive cell signals over days
Unit is “device” trips
TIME OF TRAVELInaccurate and incomplete Accurate time stamps
Need to infer activity
STOPS VS. ACTIVITIESDetailed log of stops and activitiesDetailed travel purposes
Infer stops, activities, segmentsNon-work trips difficult to infer
LOCATION OF ACTIVITIES
Locations reported are geocoded
Difficult to infer activity locationA challenge in mixed land use
TRAVEL PURPOSEDetailed data Home and work are inferred
Poor non-home and non-work
JOINT TRAVEL Recorded in diaries Not feasible to capture
MODE OF TRAVEL Good detail by tour andsegment
Not readily inferred
TOUR GENERATION Analysis of survey data No chains. Only aggregate trips
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Data PropertiesCell Phone Data (CDR)
Personal GPS Derived Data
Smartphone Surveys
Data in Raw Form
Processed Data
Zonal Size and Spatial Resolution
External Zones and Stations
Trip Purposes
Socioeconomics
Technology
Time Periods/Temporal Resolution
Commercial and Passenger Travel
Sample Expansion
Path Traces
Properties by Data Source
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CDR Data Properties » Larger sample sizes» Device as the unit of analysis» Sample attributes are not known» Expansion cannot account for market segments» Signals: Traces, paths, and time stamps» Inference of activity/stop location and duration» Data presented at the trip level versus tour level» Limited inference of “Other” travel purposes» Mode and joint travel are not inferred
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Cell Use and Travel
» Phone versus person locations Phone does not always travel with a person Multiple users may share the same phone Phone may travel with a different person Individuals may carry multiple phones
» Locational accuracy of the data Short “pseudo trips” inferred from activity locations Incorrect / spurious locations (e.g. within office) Lost trips due to dead zones – inconsistent use of phone
» No differentiation by purpose or by market segment
» Less resolution for non-work travel purposes
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Aggregate Analysis
Source: Cambridge Systematics, Inc.
Variables of Interest
Travel Data from Traditional Surveys
Travel Data based onCell Phone Use
SEASONAL VARIATION
A well thought out sampling plan
Continuous data collection by season
CDR data by month of the year
Differences in seasonal travel
VISITOR TRAVEL PATTERNS
Targeted detailed visitor surveys
Airports, train stations, highway restareas, hotels, and popular visitor sites
Home as night-time device location
Differential visitor/residential devices
SPECIAL GENERATORS
Specialized surveys at airports, malls, or special event sites
Supplement to regional surveys
Data on mode, time-of-day, origin of trips, and demographic detail
CDR data for “event days”
Capture of time-of-day and trip origin
Mode inference is weak
Demographic data not available
YEAR TO YEAR VARIATION
Longitudinal/panel or rolling sample data
Measurement of change over time
CDR datasets from different years
Measurement of change in patterns
EXTERNAL TRAVEL License-plate capture at cordon line
Follow up survey of auto owners
Bluetooth data as an option
Definition of external cordon line
Number of devices crossing cordon
Home origin to measure external travel
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Type of ModelCell Phone Data (CDR)
Personal GPS Derived Data
SmartphoneSurveys
Estimation of Regional Models
Validation of Models
Model Updates
Corridor Studies
Microsimulation studies
Special Generator Studies
Visitor Models
Long Distance Travel
Properties by Model Type
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Commuting Flows
Source Home to Work Trips (in millions)
Inter-tract (share) Inter-town (share) Average Trip Length (in miles)
CDR 2.11 94% 68% 9.67
CTPP 2.10 90% 68% 10.72
Source: Analysis of the data sources and models by MIT
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CDR Data for Modeling» ABM model estimation can’t rely on CDR data Sampling and expansion not possible by segment Limited by purpose, mode, tours, and joint travel “Black box” - access to processed versus raw data
» CDR as an additional source of validation data Journey to work Time of day travel patterns Geographic aggregation
» Options to use CDR data to augment models Trends in travel - periodic model updates Special generators and external travel Visitor and long distance markets
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Percent of Trips by Purpose
Source: Analysis of the data sources and models by MIT
Estimation Source HBW HBO NHB TotalNHTS SURVEY (2009) 13% 55% 32% 100%
MTS SURVEY (210-2011) 12% 49% 39% 100%
BHTS SURVEY (1991) 20% 48% 32% 100%
BOSTON MPO MODEL (2010) 23% 55% 22% 100%
BOSTON MPO MODEL (2007) 20% 49% 31% 100%
CDR MODEL 1 (2010) 18% 51% 31% 100%
CDR MODEL 2 (2010) 27% 42% 31% 100%
CDR MODEL 3 (2015) 18% 49% 33% 100%
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Person Trips by Purpose
Source: Analysis of the data sources and models by MIT
Estimation Source HBW(millions)
HBO(millions)
NHB(millions)
Total(millions)
IndividualDaily Avg.
MTS SURVEY (210-2011) 2.14 8.99 7.18 18.31 4.11
BOSTON MPO MODEL (2010) 3.00 7.10 2.82 12.92 2.90
BOSTON MPO MODEL (2007) 2.79 7.03 4.41 14.23 3.20
CDR MODEL 1 (2010) 2.81 7.83 4.72 15.36 3.45
CDR MODEL 2 (2010) 4.27 6.58 4.85 15.70 3.52
CDR MODEL 3 (2015) 3.52 9.53 6.36 19.42 4.36
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Trips by Time of Day
Source: Analysis of the data sources and models by MIT
Estimation SourceAM
Peak(6am-9am)
MD(9am-3pm)
PMPeak
(3pm-7pm)
RD(7pm-6am) Total
NHTS SURVEY (2009) 19% 37% 31% 13% 100%
MTS SURVEY (210-2011) 21% 34% 33% 12% 100%
BHTS SURVEY (1991) 18% 32% 33% 17% 100%
BOSTON MPO MODEL (2010) 11% 51% 21% 17% 100%
BOSTON MPO MODEL (2007) 16% 34% 28% 22% 100%
CDR MODEL 1 (2010) 16% 27% 27% 30% 100%
CDR MODEL 2 (2010) 17% 36% 27% 20% 100%
CDR MODEL 3 (2015) 20% 36% 27% 18% 100%
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Summary of Comparisons
» CDR results versus models, surveys and vendor data
» “Ground truth” tough to establish – results are mixed Differences between models Differences between surveys and models
» CDR results close to journey to work estimates
» Successful definition of home and work locations
» Narrower range for home-based trips – wider for NHB
» More CDR trips in evening and late night hours
» Geographic correlation improves with aggregation
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Evolution of Data and Methods
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The Next Few Years» Locational data» Potential of “aggregate” location data? Support model calibration/validation Replace elements of model estimation? Allow “quick” update of existing models Enhance knowledge of “ignored markets”
» Potential of “disaggregate” location data? Study passenger and freight movements Augment traditional data collection methods Examine different sampling and response bias issues
» The evolution of the traditional survey
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Cell Use and Technology» Patterns of cell phone use by subscribers Cost of cell phone use Increased use of phones for text messaging Frequent use of cell for web access
» Smartphone market penetration Higher share of smartphones A variety of apps record location passively
» Evolving technology Industry standard up to 2010: 1G / 2G Industry standard in 2015: 4G / LTE
» Mix of cell phone providers by market
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GuidebookData and Models
» Emerging sources of locational data
» CDR data vs. GPS vs. Smartphone surveys
» Potential of other locational and transaction data
» Properties for each source of emerging data
» Locational data to support travel behavior models
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One last thought
» Locational data are here to stay» Component of a new wave of data sources» The behavioral paradigm has staying power» Our methods may need to adjust to the new data» We need to recognize uncertainty and
“ground truth”» The future will likely be characterized by: Individual-level analyses of travel behavior A fusion of new and traditional data sources Continued innovation to extract value from data
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Thank You
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Slides for Q&A
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CDR Definition Varies
CDR – Financial
CDR: Calls, Text, and WebActive/outgoing control by userPassive/incoming control by phone
Location info: Cell Tower Locations only
Location frequency: Low
Location accuracy: +/- 1 mile
NCHRP Project
CDR – Financial plusTriangulated handset locations
Operational
CDR plusNetwork pinging the phoneConstant communication with phone
Handset locations triangulated / Sightings / More accurate locations
Phone continuously transmitting a signal– depends on settings and use
Positional accuracy: +/- 100s feet
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Sampling and ExpansionVariables of Interest Traditional Survey Data Cell Phone CDR DataSample Sizes
Sampling Rate Between 0.5% and 2% Between 15% and 35%Sample Size for a Region with:
1,000,000 Households
3,000,000 Population
5,000 to 20,000 Households
15,000 to 60,000 Individuals
150,000 to 350,000 Households
450,000 to 1,050,000 Individuals
Sampling StrataUnit of Analysis Individual and Household Cell phoneSampling Unit Household Cell phoneSampling by Geography As fine-grained as Block Group Level Feasible at aggregate levelSampling by Market Segment Yes N/A
Socioeconomic InformationRespondent Attributes Age / Gender / Worker Status / Student Status /
Occupation / Work hours / Ability to telecommute / Ethnicity
Night-time Location of cell phoneDay-time Location of cell phone
Household Attributes SizeNumber of VehiclesNumber of Workers
IncomeLifecycle
Residential LocationNumber of children
N/A
Survey ExpansionSampling Rate and Size Small sample size requires careful sampling and
expansion. Large dataset – relatively robust sample sizes for
expansion.Household Attributes Used in all household travel survey expansion. No data are available for detailed weighting based
on personal or household attributes.Respondent Attributes Being increasingly used in the weighting of surveys for activity-based models.
Geography Attributes Often carried out at County-level. Smaller geographic levels are possible depending on sample sizes.
Some geography-based expansion is feasible, but not at the individual or household level.
Source: Cambridge Systematics
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Variables of Interest Traditional Survey Data Cell Phone CDR Data
Total Daily Travel Self-reported in survey diaries.
Travel may be underreported.
Prompted recall offers an improvement.
Passive cell signals over days may offer more robust metrics than surveys.
Unit is “device” trips vs. person trips.
Quality depends on CDR data density.
Time of Travel Self-reported in survey diaries.
Times may be inaccurate & incomplete.
Accurate time stamps.
Need to infer activity and link it to the time stamp versus en-route travel.
Stops versus Activities Self-reported in survey diaries.
Detailed log of stops and activities.
Good detail on all travel purposes.
Need to infer stops, activities, segments.
Nonwork purposes are difficult to infer.
Location of Activities Self-reported in survey diaries.
Smart geocoding needed to match.
Prompted recall offers an improvement.
Difficult to infer the location of activities.
A challenge in mixed land use areas.
Travel Purposes Self-reported in survey diaries.
Prompted recall offers an improvement.
Home and work are inferred.
Poor inference on non-home & nonwork.Joint Travel Self-reported in survey diaries.
Risk of underreporting.
Prompted recall offers an improvement.
Not feasible to record or capture.
Mode of Travel Self-reported in survey diaries.
Good detail by tour and segment.
Walk/bike trips may be underreported.
Not readily inferred.
Route Assignment Not usually captured in surveys. Depends on trace data and algorithm.
Tour Generation Self reported in detail in a survey.
Analysis using heuristics and rules.
Data products don’t include chains.
Only aggregate trips are sold.
Recording of Travel Elements
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CDR Estimates, Survey Data and the MPO Model
Trip Estimation Comparison
Home Based Work Home Based Other Non Home Based
Baseline: The MPO Travel Demand Model
Using 2010 raw cell phone dataUsing 2010 raw cell phone dataThird-party cell phone data (2015) processed by data provider
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References» Hariharan, R. and Toyama, K. 2004. Project lachesis: parsing and modeling
location histories. In Geographic Information Science, pp 106–124. Springer.
» Jiang, S., Fiore, G. A., Yang, Y., Ferreira, J., Frazzoli, E., and Gonzalez, M.C. 2013. A Review of Urban Computing for Mobile Phone Traces: Current Methods, Challenges and Opportunities. Proceedings of the ACM SIGKDD International Workshop on Urban Computing. Chicago, IL.
» Alexander, L., Jiang, S., Murga, M., and Gonzalez, M.C. 2015. Origin-destination trips by purpose and time of day inferred from mobile phone data . Transportation Research Part C: Emerging Technologies. 58: 240-250.
» Jiang, S., Ferreira, J., and Gonzalez, M.C. 2017. Activity-based human mobility patterns inferred from mobile phone data: A case study of Singapore. IEEE Transactions on Big Data.
» Jiang, S., Yang, Y., Gupta, S., Veneziano, D., Athavale, S., & Gonzalez, M.C. 2016. The TimeGeo modeling framework for urban mobility without travel surveys. Proceedings of the National Academy of Sciences, 113(37), E5370-E5378.
Today’s Participants
• Kimon Proussaloglou, Cambridge Systematics, [email protected]
• Shan Jiang, Massachusetts Institute of Technology, [email protected]
• Dan Beagan, Cambridge Systematics, [email protected]• Anurag Komanduri, Cambridge Systematics,
Panelists Presentations
http://onlinepubs.trb.org/onlinepubs/webinars/180802.pdf
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