Status Report: Ongoing review of O-D cellular data for the TPB modeled area

Similar documents
TPB Version 2.3 Travel Forecasting Model for the 3,722 Zone Area System: Calibration Report Draft Report

Calibration Report for the TPB Travel Forecasting Model, Version 2.3, on the 3,722 Zone Area System Final Report

2015 Grand Forks East Grand Forks TDM

Developing and Validating Regional Travel Forecasting Models with CTPP Data: MAG Experience

Improving the Model s Sensitivity to Land Use Policies and Nonmotorized Travel

City of Hermosa Beach Beach Access and Parking Study. Submitted by. 600 Wilshire Blvd., Suite 1050 Los Angeles, CA

3.0 ANALYSIS OF FUTURE TRANSPORTATION NEEDS

2014 Certification Review Regional Data & Modeling

ITEM 11 Information June 20, Visualize 2045: Update to the Equity Emphasis Areas. None

Analyzing the Market Share of Commuter Rail Stations using LEHD Data

Encapsulating Urban Traffic Rhythms into Road Networks

Now That You ve Downloaded Some StreetLight Data, What Should You Do First? Data Representativeness and Expansion Considerations

Trip Generation Model Development for Albany

Appendix BAL Baltimore, Maryland 2003 Annual Report on Freeway Mobility and Reliability

Spatial and Socioeconomic Analysis of Commuting Patterns in Southern California Using LODES, CTPP, and ACS PUMS

2040 MTP and CTP Socioeconomic Data

Prepared for: San Diego Association Of Governments 401 B Street, Suite 800 San Diego, California 92101

Trip and Parking Generation Study of Orem Fitness Center-Abstract

FHWA Planning Data Resources: Census Data Planning Products (CTPP) HEPGIS Interactive Mapping Portal

Morgantown, West Virginia. Adaptive Control Evaluation, Deployment, & Management. Andrew P. Nichols, PhD, PE

November 16, Metropolitan Washington Council of Governments National Capital Region Transportation Planning Board

Changes in the Spatial Distribution of Mobile Source Emissions due to the Interactions between Land-use and Regional Transportation Systems

Assessing spatial distribution and variability of destinations in inner-city Sydney from travel diary and smartphone location data

Census Transportation Planning Products (CTPP)

Spatiotemporal Analysis of Commuting Patterns in Southern California Using ACS PUMS, CTPP and LODES

Transit Modeling Update. Trip Distribution Review and Recommended Model Development Guidance

Forecasts for the Reston/Dulles Rail Corridor and Route 28 Corridor 2010 to 2050

Technical Memorandum #2 Future Conditions

Land Use Modeling at ABAG. Mike Reilly October 3, 2011

FHWA Peer Exchange Meeting on Transportation Systems Management during Inclement Weather

URBAN TRANSPORTATION SYSTEM (ASSIGNMENT)

HORIZON 2030: Land Use & Transportation November 2005

Tier 2 Final Environmental Assessment I-66 Transportation Technical Report. Appendix E. Travel Demand Forecasting Model Validation Memorandum

ADDRESSING TITLE VI AND ENVIRONMENTAL JUSTICE IN LONG-RANGE TRANSPORTATION PLANS

Sketch Transit Modeling Based on 2000 Census Data. Norm Marshall and Brian Grady. Norm Marshall

Douglas County/Carson City Travel Demand Model

WOODRUFF ROAD CORRIDOR ORIGIN-DESTINATION ANALYSIS

Trip Generation Calculations

Spatiotemporal Analysis of Commuting Patterns: Using ArcGIS and Big Data

Transportation Statistical Data Development Report OKALOOSA-WALTON OUTLOOK 2035 LONG RANGE TRANSPORTATION PLAN

Speakers: Jeff Price, Federal Transit Administration Linda Young, Center for Neighborhood Technology Sofia Becker, Center for Neighborhood Technology

Bus Landscapes: Analyzing Commuting Pattern using Bus Smart Card Data in Beijing

Extracting mobility behavior from cell phone data DATA SIM Summer School 2013

Estimating Large Scale Population Movement ML Dublin Meetup

2012 Household Travel Survey Preliminary Highlights

Exploring Human Mobility with Multi-Source Data at Extremely Large Metropolitan Scales. ACM MobiCom 2014, Maui, HI

The prediction of passenger flow under transport disturbance using accumulated passenger data

StanCOG Transportation Model Program. General Summary

Detecting Origin-Destination Mobility Flows From Geotagged Tweets in Greater Los Angeles Area

Impact of Metropolitan-level Built Environment on Travel Behavior

Exploring the Impact of Ambient Population Measures on Crime Hotspots

March 31, diversity. density. 4 D Model Development. submitted to: design. submitted by: destination

Using Innovative Data in Transportation Planning and Modeling

Trip Generation Characteristics of Super Convenience Market Gasoline Pump Stores

CONGESTION REPORT 1 st Quarter 2016

Regional Growth Strategy Work Session Growth Management Policy Board

SBCAG Travel Model Upgrade Project 3rd Model TAC Meeting. Jim Lam, Stewart Berry, Srini Sundaram, Caliper Corporation December.

APPENDIX C-3 Equitable Target Areas (ETA) Technical Analysis Methodology

FINAL REPORT THE FLORIDA DEPARTMENT OF TRANSPORTATION RESEARCH OFFICE. on Project

Regional Transit Development Plan Strategic Corridors Analysis. Employment Access and Commuting Patterns Analysis. (Draft)

Palmerston North Area Traffic Model

TRAVEL DEMAND MODEL. Chapter 6

Assessing the impact of seasonal population fluctuation on regional flood risk management

River North Multi-Modal Transit Analysis

Mapping Accessibility Over Time

FLORIDA STATEWIDE TOURISM TRAVEL DEMAND MODEL: DEVELOPMENT OF A BEHAVIOR-BASED FRAMEWORK

GIS Analysis of Crenshaw/LAX Line

Geospatial Analysis of Job-Housing Mismatch Using ArcGIS and Python

CIV3703 Transport Engineering. Module 2 Transport Modelling

APPENDIX IV MODELLING

Trip Generation Study: A 7-Eleven Gas Station with a Convenience Store Land Use Code: 945

Figure 8.2a Variation of suburban character, transit access and pedestrian accessibility by TAZ label in the study area

California Urban Infill Trip Generation Study. Jim Daisa, P.E.

PATREC PERSPECTIVES Sensing Technology Innovations for Tracking Congestion

A Simplified Travel Demand Modeling Framework: in the Context of a Developing Country City

Study Overview. the nassau hub study. The Nassau Hub

Appendix B. Land Use and Traffic Modeling Documentation

Non-Motorized Traffic Exploratory Analysis

April 18, Accessibility and Smart Scale: Using Access Scores to Prioritize Projects

Regional Performance Measures

NATHAN HALE HIGH SCHOOL PARKING AND TRAFFIC ANALYSIS. Table of Contents

IRTS 2008 Compilation Guide: an overview

QUANTIFICATION OF THE NATURAL VARIATION IN TRAFFIC FLOW ON SELECTED NATIONAL ROADS IN SOUTH AFRICA

BESPOKEWeather Services Tuesday Morning Update: SLIGHTLY BEARISH

MOBILITIES AND LONG TERM LOCATION CHOICES IN BELGIUM MOBLOC

MPOs SB 375 LAFCOs SCAG Practices/Experiences And Future Collaborations with LAFCOs

Metrolinx Transit Accessibility/Connectivity Toolkit

Use of US Census Data for Transportation Modeling and Planning

September 22, Metropolitan Washington Council of Governments National Capital Region Transportation Planning Board

Visualization of Commuter Flow Using CTPP Data and GIS

Integrating Origin and Destination (OD) Study into GIS in Support of LIRR Services and Network Improvements

Paine Field Airport Existing and Future Environmental Assessment, Initiation of Commercial Service Noise Analysis

Kitsap County 2016 Comprehensive Plan Update. Appendix A: Growth Estimates

The Spatial Structure of Cities: International Examples of the Interaction of Government, Topography and Markets

Regional Performance Measures

Data Collection. Lecture Notes in Transportation Systems Engineering. Prof. Tom V. Mathew. 1 Overview 1

* Abstract. Keywords: Smart Card Data, Public Transportation, Land Use, Non-negative Matrix Factorization.

2010 KMPO Base Calibration Travel Demand Model Update

Wasatch Front Region Small Area Socioeconomic Forecasts: Technical Report #49

Visualization of Origin- Destination Commuter Flow Using CTPP Data and ArcGIS

Transcription:

Item #4 Status Report: Ongoing review of O-D cellular data for the TPB modeled area Presentation to the Travel Forecasting Subcommittee September 19, 2014 Ronald Milone, COG/TPB staff National Capital Region Transportation Planning Board (TPB) Metropolitan Washington Council of Governments (COG) Item4_tfs140718_AirSage_v2.pptx

Recap: June TPB staff purchased O-D data from AirSage (AS) Intention: To use AS data as a basis for updating TPB forecasts of external, through and visitor/tourist trips Opportunity: Data mined from wireless signal tracking is potentially a cost-effective data solution for analyzing exogenous markets in aggregate Challenge: To assess this novel product 2

Recap: July-September Staff presented early results to the TFS in July Reviewed AirSage s approach for processing mobile device data into trips Showed early findings of the analysis, relating O/Ds to land activity and ground counts Our analysis has continued since July to date AirSage transmitted an updated O/D file to COG in August External and through trips were refined Analysis has been more focused on comparing the updated AirSage data with land activity and modeled outputs 3

What are we looking for in the analysis? An informed understanding of the data Are AS trips consistent with land activity? How do AS trips compare to modeled trips? Are AS trip patterns complete and reasonable? What are the potential biases or limitations of the data? 4

For today Brief recap of the AirSage data product Review of the data and TPB s current analysis: Comparison of AirSage trips & land activity Comparison of AirSage trips & modeled trips Comparison of recent AirSage trips and traffic counts 5

AirSage Technology Collection: Wireless carrier (Verizon, Sprint) signals are securely captured Analytics: Multiple stages of statistical analysis are performed on several weeks of data to assess the location and temporal profiles of mobile devices O/D samples: Trip purposes and traveler subclasses of O/Ds are inferred based on daytime and nighttime location clustering The analysis results in a highly sampled trip file of aggregate O/D movements that may be assigned to any predefined level of geography 6

Key AS trip record attributes Origin, destination (O/D) identifier Subscriber classes (6) Trip purpose codes (9) Time of day codes (5) Weighted trips Thus, AirSage essentially provides: - 54 daily trip tables (6 subscriber classes by 9 trip purposes) - 270 time period trip tables (54 trip tables by 5 time periods) 7

O/D matrix specifications Parameter Specification Notes / Details O/D geography 3722 TAZs Includes external station O/D groups Year/month timeframe 1 April 2014 Day of week timeframe Average weekdays Tuesday, Wednesday, Thursday only Time of Day Periods Early AM / 12mid. -6AM AM Peak/ 6-9AM Midday/ 9AM- 3PM PM Peak / 3PM-7PM Night/ 7PM-12mid. Time periods consistent with those used in the existing 2.3 traffic assignment process 8

O/D matrix specifications, cont. AirSage s trip purpose classifications (9): Purpose: 1 Home-to-Work (H-W) 2 Home-to-Other (H-O) 3 Home-to-Home (H-H) 4 Work-to-Home (W-H) 5 Work-to-Other (W-O) 6 Work-Work (W-W) 7 Other-to-Home (O-H) 8 Other-Work (O-W) 9 Other-Other (O-O) - The home, work and other designations are inferred based on locational and temporal clustering of device locations - AS purposes are based on directional movements (i.e., not in P/A format) 9

O/D matrix specifications, cont AirSage s traveler subscriber classes (6): Subclass Description 1 Resident Worker Resident, daytime & nighttime location clusters are different & inside study area 2 Home Worker Resident, daytime & nighttime location clusters are similar 3 Inbound Commuter Non-resident, daytime cluster is inside the study area 4 Outbound Commuter Resident, daytime cluster is outside of the study area 5 Short Term Visitor Non-resident, in the study area two weeks or less 6 Long Term Visitor Non-resident, in the study area more than two weeks 10

AirSage trips vs. surveyed trips Trip attributes AirSage HTS Trips Trip detection Based on aggregate device movements Reported by HH member Trip linking Unknown Reported by HH member Trip maker information Unknown Reported by HH member Trip purpose Inferred by TOD /location clustering Reported by HH member Trip mode Unknown Reported by HH Member Auto occupancy Unknown Reported by HH member Trip Weighting Based on Census Population-based expansion at tract level Based on Census expansion that considers jurisdictional, household, and person level dimensions 11

Trip table summary of weighted AirSage purpose and subclass codes Weighted Daily AirSage Trip Summary (April 2014 / Avg. Weekday) Trips by AirSage Purpose and Subclass Subclass Purpose RES_WORKER HOME_WORKER INB_COMMUTER OUTB_COMMUTER SHRTTM_VISITR LONGTM_VISITR TOTAL H_H: 1,994,758 2,058,895 3,393 137,643 8,890 287 4,203,866 H_O: 1,476,206 1,567,904 33,007 107,379 87,937 3,672 3,276,105 H_W: 3,075,407 0 103,607 64,614 4,941 0 3,248,570 W_W: 470,913 0 22,886 602 608 0 495,009 W_H: 2,586,034 0 85,150 56,091 5,232 0 2,732,507 W_O: 807,156 0 31,363 11,838 7,568 0 857,925 O_O: 786,883 588,627 28,250 83,036 237,994 891,602 2,616,392 O_H: 1,859,124 1,530,171 39,077 125,735 84,288 3,550 3,641,946 O_W: 363,648 0 13,990 6,023 5,187 0 388,849 Sum: 13,420,130 5,745,598 360,723 592,961 442,647 899,111 21,461,170 - Above table reflects all (internal, external and through) movements - Trip anomalies do exist, e.g.: - an internal inbound commuter trip with a Home origin - an internal outbound commuter trip with Work origin 12 TPB staff is communicating with AirSage about these types of occurrences

Conversion of AirSage trips into modeled purpose trip tables 54 daily trip tables are too many to analyze! Staff thought about how best to transform AS data into a form that could be compared to modeled trips AirSage trip tables were consolidated by modeled purpose (as closely as possible) to expedite analysis 54 AirSage trip tables were collapsed into 5 trip tables: HBW, HBNW, NHW, NHO and Non-Resident trips O/D TAZs of to-home AirSage trips were transposed to arrive at trip tables in P/A format 13

Equivalence of modeled purposes to AirSage purposes/subclasses AirSage Subscriber Class--> AirSage Resident Home Outbnd. Inbnd. Short Term Long Term Purpose Worker Worker Commuter Commuter Visitor Visitor H-H * H-O Home-Based Non-Work (HBNW) O-H * H-W Home-Based-Work (HBW) W-H * Non-Resident W-W W-O Non-Home-Based Work-Related (NHW) O-W O-O Non-Home-Based Non-Work (NHO) * - indicates transposition of O/D codes to reflect P/A format 14

HBW TAD Origins HBW TAZ Origins Correlation between AirSage HBW productions and Round 8.3 land activity 20,000 18,000 16,000 14,000 AirSage HBW Trip Productions vs. HHs at TAZ Level y = 1.542x + 445.33 R² = 0.6261 Zone level comparison Substantial scatter 12,000 10,000 8,000 6,000 4,000 2,000 0 80,000 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000 Households (Rnd8.3) AirSage HBW Productions vs. HHs at TAD Level District level comparison Much reduced scatter 70,000 60,000 50,000 40,000 30,000 20,000 y = 2.0714x + 589.88 R² = 0.9127 10,000 15 0 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 Households (Rnd8.3)

Work TAD Origins Work TAZ Origins Correlation between AirSage HBW attractions and Round 8.3 land activity 35,000 30,000 25,000 20,000 AirSage HBW Trip Attractions vs. Jobs at TAZ Level y = 0.8125x + 637.6 R² = 0.5367 15,000 Zone level comparison Considerable scatter District level comparison 10,000 5,000 0 120,000 100,000 80,000 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 y = 1.1852x + 1893.2 R² = 0.8781 Jobs (Rnd8.3) AirSage HBW Attractions vs. Jobs at TAD Level Much reduced scatter, but outliers still prevalent 60,000 40,000 20,000 16 0 0 20,000 40,000 60,000 80,000 100,000 120,000 Jobs (Rnd8.3)

Comparison of internal (I-I) trips by purpose: 2014 AirSage vs. 2015 TPB Model AirSage vs. TPB Model Internal-Resident Trip Comparison (A) (B) ( C) TPB Modeled Trips Motorized Motorized & Purpose AirSage Person Non-Motr Psn (A) / (B) (A) / ( C) HBW 5,656,000 3,991,300 4,148,700 1.42 1.36 HBNW 10,641,700 10,333,500 11,592,600 1.03 0.92 NHW 1,619,700 1,664,900 2,150,900 0.97 0.75 NHO 1,413,200 3,332,300 3,709,600 0.42 0.38 ALL 19,330,600 19,322,100 21,601,700 1.00 0.89 Findings: - AS trips match modeled motorized person trips well, overall - AS trips are less than motorized and non-motorized person trips by 11% - AS HBW trips are substantially higher than modeled motorized HBW trips - AS NHO trip are substantially less than modeled motorized NHO trips Modeled trips are from a 2015 simulation (R8.3, Version, 2.3.57 Model, 2014 CLRP) AirSage trips reflect weekday average for April 2014 17

Comparison of internal (I-I) trips generation rates by purpose: 2014 AirSage vs. 2015 TPB Model Daily Person Trips Trips/HH Trips/Job Purpose AirSage TPB Model AirSage TPB Model AirSage TPB Model HBW 5,656,000 4,148,700 2.17 1.59 1.39 1.02 HBNW 10,641,700 11,592,600 4.08 4.45 2.61 2.84 NHW 1,619,700 2,150,900 0.62 0.83 0.40 0.53 NHO 1,413,200 3,709,600 0.54 1.42 0.35 0.91 ALL 19,330,600 21,601,700 7.42 8.29 4.74 5.30 - Trips shown are internal only (I-I) - TPB trips shown above include motorized & non-motorized trips - Trip rates shown are based on 2014 Rnd. 8.3 land activity 18

Comparison of internal (I-I) trip lengths by purpose: 2014 AirSage vs. 2015 TPB Model Daily Person Trips Avg. Time (min) Distance (mi) Purpose AirSage TPB Model AirSage TPB Model Ratio (A/T) AirSage TPB Model Ratio (A/T) HBW 5,656,000 3,991,300 40 49 0.82 13 15 0.87 HBNW 10,641,700 10,333,500 18 16 1.13 8 7 1.14 NHW 1,619,700 1,664,900 21 19 1.11 10 8 1.25 NHO 1,413,200 3,332,300 21 14 1.50 10 5 2.00 ALL 19,330,600 19,322,100 24 22 1.09 9 8 1.13 Findings: - Total time and distances lengths are in reasonable agreement - HBW and NHO average trip times are more notably different - HBNW and NHW average trip times are within +/- 2 minutes TPB trips shown are internal, resident motorized person trips Modeled trips are from a 2015 simulation (R8.3, Version, 2.3.57 Model, 2014 CLRP) AirSage trips reflect weekday average for April 2014 19

AirSage (2014) Person Trips (in thousands) Total I-I trips at the jurisdictional level agree well, except for trips within some of the large counties 3,000 2,500 2,000 1,500 1,000 AirSage and TPB Modeled Internal Motorized Trips at Jurisdiction Level Purpose: Total Intra-Montgomery County Intra-Fairfax County 500 0 0 500 1,000 1,500 2,000 2,500 3,000 TPB Modeled (2015) Motorized Person Trips (in thousands) 20

21

Trips per HH Zonal trip production rates Scatter plot of ZONAL household trip rates using AirSage resident trips and Round 8.3 land activity 45.00 40.00 35.00 30.00 25.00 20.00 15.00 AirSage Daily Trips/HH Zonal Level of Analysis 10.00 DAILY total purpose trips per HH 5.00 0.00 1 1001 2001 3001 TAZ No. (1-3675) Daily HBW trips per HH 14.00 12.00 10.00 8.00 AirSage HBW Trips/HH Zonal Level of Analysis Expected Range 6.00 22 Apparent inconsistency between zone level trips and land activity yields unstable rates 4.00 2.00 0.00 1 1001 2001 3001 TAZ No. (1-3675)

District trip production rates Scatter plot of DISTRICT level household trip rates using AirSage resident trips and Round 8.3 land activity DAILY total purpose trips per HH Daily HBW trips per HH 23 Much more stable and reasonable, with a few outliers.

Comparison of 2014 AirSage External/Through trip-ends with 2012 AAWDT Counts at External Stations (a) (b) Apr-14 Observed 2012 External AirSage Trips Vehicle counts Difference Ratio Station(s) Facility (or Facilities) (a - b) (a / b) 3676 VA 3 26,088 5,000 21,088 5.22 3677 US 301 18,084 11,000 7,084 1.64 3678-3681 US 17, VA 2, I-95, and US 1 100,949 104,600-3,651 0.97 3682 VA 208/606 34,150 3,500 30,650 9.76 3683 VA 612 12,338 3,500 8,838 3.53 3684 VA 3 60,630 23,400 37,230 2.59 3685 US 15/29 51,257 26,600 24,657 1.93 3686 US 211 26,265 16,000 10,265 1.64 3687-3688 I-66 and VA 55 46,075 38,500 7,575 1.20 3689-3690 US 340 and US 17/50 41,826 28,400 13,426 1.47 3691 VA 7 50,278 26,500 23,778 1.90 3692 WVA 51 1,486 8,500-7,014 0.17 3693-3694 WVA 9 and WVA 45 130 26,800-26,670 0.00 3695 WVA 480 (MD 34) 9,972 5,800 4,172 1.72 3696-3699 US 40 (Alt), I-70, US 40, and MD 77 1,629 85,400-83,771 0.02 3700 MD 550 19,010 2,000 17,010 9.51 3701 PA 16/MD 140 12,671 8,700 3,971 1.46 3702-3704 US 15, MD 194, and MD 97 45,490 35,100 10,390 1.30 3705-3706 MD 30 and MD 86 50,066 18,800 31,266 2.66 3707 MD 88/833 13,227 4,400 8,827 3.01 3708-3710 MD 30, MD 140 /91, and MD 26 132,814 95,000 37,814 1.40 3711-3712 I-70 East and US 40 East/MD144 94,541 157,300-62,759 0.60 3713-3714 I-95 and US 1/I-195 129,107 274,600-145,493 0.47 3715-3716 MD-295 /BWPkwy and MD 170 73,888 112,800-38,912 0.66 3717-3721 MD 648, MD 3/I-97, MD 2, and MD 710 43,066 263,700-220,634 0.16 3722 US 50/301 50,670 73,400-22,730 0.69 TOTAL: 1,145,708 1,459,300-313,592 0.79 24

wwwcounts 25

Staff is reviewing external trip maps and will present more at the next meeting. 26

Conclusions Staff has been working with a refined set of trip tables from AirSage AirSage trips have been compared to land activity and TPB modeled trips by purpose Substantial noise exists at the TAZ level Noise is reduced at the TAD level; not eliminated completely Differences are noted in comparing AS & TPB motorized trips by purpose: HBNW and NHW trips and trip lengths match reasonably HBW and NHO and trip rates and trip lengths are different 27

Conclusions, cont. The match between AS external, through trips and counts at external stations has improved; AS trips are nonetheless still less than counts by about 20%. A pronounced underestimation exists for stations in the Baltimore area Staff will investigate locations where inconsistencies between AirSage trips and land activity exist For modeling purposes, AS data appears to be more stable at the district level than at the zone level Work will continue 28