1 Results of FY 08 COG/TPB Travel Forecasting Research Presentation to Travel Forecasting Subcommittee September 19, 2008 Rich Roisman, AICP, Senior Transportation Planner Hongtu “Maggie” Qi, P.E., Transportation Engineer Jose “Joe” Ojeda, Transportation Engineer Vanasse Hangen Brustlin, Inc. Phil Shapiro, P.E., PTOE Shapiro Transportation Consulting, LLC

2 FY 08 TPB Travel Forecasting Research Topics Expanded evaluation of peak spreading Estimating the impact of exurban commuters on travel demand

EXPANDED PEAK SPREADING ANALYSIS 3

FY 2007 Peak Spreading Analysis State of the art and state of the practice review Initial evaluation of traffic count data –Determine data availability Evaluated peak spreading in TPB area –Relationship of peak v/c to # of lanes –Proposed approaches for TPB model 4

Expanded 2008 Analysis Omitted use of V/C ratio Obtained MD SHA historical hourly traffic count data from ATR sites Comparable VDOT and DDOT data not available 5

Compared Ratio of Hourly to Peak Hour Volume by Year 6

Compared Volume Through AM Peak Period by Year 7

Compared Volume Through PM Peak Period by Year 8

Volume per Lane Relationship Peak hour volume per lane divided by ADT volume per lane Radial freeways I-270 I-95 US 50 Combined Beltway in Prince George’s County 9

AM Peak Hour Regression All Radial Facilities 10 r 2 =.726

PM Peak Hour Regression All Radial Facilities 11 r 2 =.598

Potential for Radial Freeways When peak hour capacity reached trend emerges –As ADT/Lane increases peak hour % goes down –Breaks down when ADT/lane > 29,000 Value to TPB and member agencies –Estimate peak % used to determine ADT capacities for model assignment –Project Planning peak hour volumes 12

ADT / Lane and Peak Hour Percent Radial Freeways 13

Regression for Capital Beltway in Prince George’s County Results not as promising as for radial freeways –NB AM Peak Hour: r 2 = –NB PM Peak Hour: r 2 = –SB AM Peak Hour: r 2 = –SB PM Peak Hour: r 2 =

Regression for Capital Beltway Prince George’s County r 2 - not very good fit Off-peak direction regression line slopes up instead of down Probably due to significant available off-peak capacity in Prince George’s County segment Further analysis needed to find useful relationship 15

16 Future steps for TPB Obtain more good hourly count data –Peak Hour and ADT –VA, DC & Other MD Locations –Freeway and major arterials Test Beltway locations with higher peak volumes Test arterial roadways –Radial –Circumferential

ESTIMATING THE IMPACT OF EXURBAN COMMUTERS ON TRAVEL DEMAND 17

18 Estimating the Impact of Exurban Commuters on Travel Demand Purpose: to allow continual evolution of TPB’s forecasting methods Identification of exurban travel patterns to the TPB region Literature review Data review Forecasting external trips using regression equations

19 Background FY 06 research on MPO methods of external forecasts Initial review of data indicates TPB region experiences high level of E-I travel E-I travel from workers outside the region “Extreme commuters” who live more than 100 miles away from the Capitol Impacts on travel forecasting and transportation planning are significant

Long Travel Times Experienced by Area Workers Well-Documented by 2003 ACS Average county commute time rankings –Prince William and Prince George’s counties exceeded only by four outer boroughs of NYC –Fairfax County (21) and District of Columbia (45) also in top 50 nationally for average commute times Among state rankings –Maryland (2); District (4); Virginia (9) 20

2003 ACS Also Highlights “Extreme Commuters” Census defines as traveling 90 minutes or more (one-way) to work Nationally, only 2% of workers face extreme commutes Prince William: 4.5%; Prince George’s: 3.8%; Montgomery: 2.2% Maryland: 3.2%; Virginia: 2.3%; District: 2.2% 21

Consider West Virginia… Eastern panhandle part of new frontier for Washington-area workers Ranked 12 th in commute times nationally Largest increase in average commute times between 1990 and 2000 Jefferson County part of TPB modeled area Berkeley County outside modeled area, growing source of E-I trips Many other nearby jurisdictions like Berkeley County 22

Results of Literature Review: Popular Press Covered extreme commuting heavily following release of ACS data ACS data as jumping-off point to cover more extreme commuters –Northeastern PA as commute shed for NYC –Antelope Valley to Los Angeles Relationship between transportation and housing costs Even a recent documentary film on extreme commuting 23

Results of Literature Review: Professional / Academic Press TTI Urban Mobility Report used as baseline data for popular press articles Transportation and housing costs as metric for regional affordability –Can area’s essential workers can afford to live there? –When answer is “no,” likelihood of extreme commuting higher Pisarski, Commuting in America III 24

Results of Literature Review: Commuting in America III Several key findings –Increases in the proportion of workers traveling > 60 mins and > 90 mins to work –Increases in the percentage of workers leaving before 6 AM –Nationally, about 11% of work trips to the city center arrive from outside the metropolitan area CIA III also asks: will long-distance commuting continue to expand? 25

Data Analysis Review of CTPP and BEA data –Time series comparison Comparable regions (CMSA) –NYC, Atlanta, San Francisco, Los Angeles Forecasting external trips 26

TPB Modeled Region

TPB Modeled Region

TPB Modeled Region

TPB Modeled Region

External Travel

Forecasting External Trips By type of jurisdiction –Central, Inner Suburb, Outer Suburb, etc. Regression equations tested –External trips vs. (employment minus workers) by jurisdiction –Strong relationship for Central jurisdictions –Less strong for outer areas –Less strong for areas with more employment than workers 32

Results – Central Jurisdictions 33

Results – Fredericksburg area and Other Jurisdictions 34

Results for Areas with More Employment than Workers 35

Implications for TPB Modeling Process Conclusions of literature based on “cheap” gasoline –Impact of further price increases? Predictive equations can be used for forecasting external travel in some areas –Further analysis using this data set should be performed 36

Implications for TPB Modeling Process (2) Backcasting using predictive equations as additional test Challenge of data availability E-I market will continue to grow –TPB model must do a reasonable job at capturing these trips 37

38 Questions?