cell phone location data for travel behavior analysis

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

http://www.trb.org/nchrp/nchrp.aspx

http://www.trb.org/Main/Blurbs/177847.aspx

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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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» 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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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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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



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


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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


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


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


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


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


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.


Difficult to infer the location of activities.

A challenge in mixed land use areas.

Travel Purposes Self-reported in survey diaries.


Home and work are inferred.

Poor inference on non-home & nonwork.Joint Travel Self-reported in survey diaries.

Risk of underreporting.


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,

[email protected]

mailto:[email protected]




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cell phone location data for travel behavior analysis

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