( P2)

Presentation Structure Research Objectives Research Process Findings Next Steps 2

Overview of Topics State-of-the-Practice –Carpool preference policy alternatives (slide 6) –Factors considered in setting policies (slide 7) Survey Results –Personal travel patterns (slides , 20, 21) –Managed Lane opinions (slides 23, 24) –Stated preference on mode choice (slides 26, 27, 28) –Survey findings (slides 29, 30) Empirical Analysis –Case study of 91 Express Lanes, CA (slide 31) Impact Analysis –Hypothetical Case Study (slide 32) –Analysis of alternative carpool preference policies (slide 33) Operations (slide 34) Person Throughput (slide 35) Revenue (slide 37) Emissions – CO2 (slide 39) Comparison of Alternative HOV Policy Scenarios (slide 40) Overall Findings (slide 41) Policy Considerations (slide 42) 3

Research Objective: Evaluate the tradeoffs associated with carpool preferences in Managed Lanes Explore causal relationship between HOV pricing incentives and the propensity to carpool Document state-of-the-practice in carpool preferences Identify tradeoffs associated with preferential treatment 4

Survey Implementation Products: Decision Framework Webinar PowerPoint and Brochure Observational Conclusions PMC+ Input State-of-Practice Review Survey Results Impact Analysis Tool Research Process 5

ETL Carpool Preference Combinations HOV 3+ Free 24/7 Free Peak Period Only, Pay All Other Times Discount 24/7 Discount Peak Period Only, Pay All Other Times Pay 24/7 HOV2 Free 24/7 HOV-to-HOT I-15 CA I-394 MN I-25 CO I-15 UT SR 167 WA Free Peak Period Only, Pay All Other Times I-10 Houston Expansion Discount 24/7 Discount Peak Period Only, Pay All Other Times DFW Policy Pay 24/7 SR-91 CA*, I-495 VA, I-95/395 VA Express Toll Lanes 6

State-of-the-Practice Results Limited information about carpool incentives in priced Managed Lanes Current facilities provide free access to HOV3+, many offer HOV2+ free Factors considered in setting policy: –Enforcement of carpools vehicles –Maximizing vehicular throughput –Uniformity and equity considerations Apparent disconnect between regional carpool program objectives and ML policies 7

Survey May to July 2006 Houston and Dallas –Toll and HOV facilities –Primarily web-based Questions Regarding –Personal travel patterns –Managed Lane opinions –Stated preference on mode choice –Demographic information 8

Data Collection Primarily collected on-line English and Spanish Widely advertised and many organizations provided web links Resulted in over 4000 valid responses, but too few from minority and low income respondents 9

Data Collection Learned of Survey From: DallasHouston News Article TV News Report Tollbooth Card Bus / Train Card 0.1 Employer Website Link Family / Friend Other / No Answer

Data Collection Additional responses from selected community centers and DPS offices Required both paper and laptop options 11

Data Collection Survey TypeDallasHoustonTotal Electronic - Web Based1,8522,4054,257 Electronic – On-site Paper – On-site Total2,0362,5754,611 12

Ethnicity Ethnicity Dallas Houston SurveyCensusSurveyCensus Caucasian African-American Hispanic Asian Native American Others

Income Income Dallas Houston SurveyCensusSurveyCensus Less than $25,0007.0%22.0%6.2%26.1% $25,000 to $50, %28.7%16.0%28.7% $50,000 to $100, %31.8%38.3%29.6% $100,000 to $200, %13.7%33.3%12.5% More than $200,0007.0%3.8%6.2%3.1% Total100% 14

The Bottom Line… Our sample: –Under-represented minority and low-income travelers –Over-represented toll road users Weighted our results to better represent Houston and Dallas traveler characteristics by: –4 income groups –4 ethnic groups –Toll versus non-toll road travelers 15

Weighting Factors for Dallas Respondents Who Used Toll Roads IncomeCaucasian African- American HispanicOthers Less than $24, $25,000 to $49, $50,000 to $99, $100,000 or more

Reasons for Mode Choice Why Do People Carpool? Why Don’t People Carpool? Why Do People Use Transit? 17

Reasons for Carpooling FactorNumber of Respondents Mean Score* Relaxation while traveling Access to HOV Lanes Help environment and society Enjoy travel with others Sharing vehicle expenses Travel time savings Reliability of arrival time Drop off kids at school/day care Get work done while traveling Splitting tolls on toll roads Carpool partner matching program Encouraged by program at work Preferred parking at work Other * 5 = very important to 1 = not at all important 18

Reasons for Not Carpooling FactorPercentage of Respondents Location/schedule limitation55 Travel flexibility45 Need a vehicle during the day35 Need to make other stops during the trip28 Appreciate alone time20 No program (to encourage carpooling)14 Like my specific radio station/music7 Potential carpool partners have disagreeable traits6 Other8 19

Types of Carpools 5.6 minutes average formation time 20

Reasons for Using Transit ReasonPercentage Selected Cost savings18 Convenience49 No waiting / short headways7 Travel time (quicker than car)6 No car available9 Other11 21

Interest in Managed Lanes With Managed Lanes a freeway would have two types of lanes as shown below. There would be toll free lanes - but they may be congested. There would also be new Managed Lanes added to the freeway where a toll would be charged but those lanes would not be congested. The toll would be collected electronically so there would be no toll booths. There might also be toll discounts or free travel in the Managed Lanes for carpools and buses. Would you be interested in using Managed Lanes? 22

Data Analysis CategoryPercent Interested in using MLs Toll Road Travelers73.4 Non-Toll Road Travelers67.9 Caucasians71.9 African-Americans69.5 Hispanic70.9 Others55.6 Less than $25, $25,000 to $50, $50,000 to $100, More than $100, Travelers with different trip purposes and modes also showed strong interest in the ML concept. The lowest level was transit riders with 60% interest in MLs. 23

Data Analysis What reasons do travelers give for preferring or not preferring MLs? Top ranked reasons why respondents would use the MLs: 1.Able to travel faster than GPLs 2.Travel time reliability Top ranked reasons why respondents would not use the MLs: 1.Other 2.Do not want to pay the toll Other was dominated by one theme – “My taxes already pay for the roads” 24

Stated Preference Questions Respondents selected between MLs and GPLs Different –Occupancy levels (SOV, HOV2, HOV3+) –Tolls –Travel times Resulted in the models Ginger will be discussing 25

Characteristic Replicate Weights Choose ML (%)p-value Income ($) < 35, ,000 to 49, ,000 to 74, ,000 to 99, > 100, Trip Length (miles) 0 to to to >

Characteristic Replicate Weights Choose ML (%)p-value Trip Purpose Commute Recreational Work Related School Other Age 16 to to to to and older

Characteristic Replicate Weights Choose ML (%)p-value Ethnicity Caucasian African-American Hispanic Other Mode SOV HOV HOV Vanpool, Train, Bus or Motorcycle

Survey Results With the planned ML in Texas, providing preferential treatment to HOVs is a significant issue. The web survey provided a cost-effective survey method, but required follow up for some groups. Overall, a lot of interest in MLs (approximately 70%). 29

Survey Results Little difference in ML interest by city or trip purpose Interest jumped as income >$100,000 Current toll road users were more likely to be interested in using MLs Travel time savings and reliability were highest rated reasons for ML use Tolls and “roads already paid by my taxes” were the main negative aspects 30

Empirical Results Case study of SR 91 Express Lanes –One of the only facilities where effects of price change on carpooling can be measured Findings –Overall percentage of vehicles in traffic stream decreased by small amount when HOV3+ charged –However, this amount represented a significant portion of HOV3+ –True for both scenarios, where preference suspended and resumed –Elimination of preferential treatment decreased use of HOV3+ while increasing revenue 31

Impact Analysis - Modeling Purpose –Develop quantitative values for various measures of effectiveness Modeling tool –UTA’s Toll Pricing Model (TPM) 3.1 Driver decisions –Stated preference survey data used to develop model for predicting mode choice in priced lanes Corridor analyzed –IH-30 under high volumes –Peak hour analysis, no trucks 32

HOV Policy Scenarios ExampleHOV Policy Express Toll Lanes (base case)All HOVs pay 91 Express Lanes CA (private op)HOV3+ 50%, HOV2 pay DFW policyHOV2+ pay 50% I-10 Houston, 91 Express (public op)HOV3+ free, HOV2 pay (No example)HOV3+ free, HOV2 pay 50% Typical HOV-to-HOTAll HOVs free 4 SOV price scenarios Low: $0.10/mile Medium: $0.25/mile High: $0.50/mile Optimized for 60 mph in MLs: $ $0.45 per mile 33

Model Results - Operations 34

Model Results – Person Throughput 35

Person Movement Values for Optimized Toll Rate HOV Scenario ML Vehicles ML People Δ People (ETL) ETL HOV3+ 50% HOV2 pay All HOV pay 50% HOV3+ free HOV2 pay HOV3+ free HOV2 pay 50% All HOVs free

Model Results – Revenue Impacts 37

Model Results - Emissions 38

Model Results - Emissions 39

HOV Policy Scenarios 40

Overall Findings HOV preferences in Managed Lanes can influence carpooling behavior Family member carpools make up the vast majority of carpools –HOV access rates high in “fampool” responses Support for Managed Lanes is high in Texas cities that currently have both toll roads and HOV lanes, and “faster travel” and “travel time reliability” were the most important reasons for support There may be more to gain in person-moving capacity with policies that emphasize HOV preference The determination of the appropriate HOV policy in Managed Lanes depends upon individual project objectives 41

HOV Policy Considerations Existing HOV policies Regional ridesharing objectives Facility performance objectives 42