Draft System Performance Measures Input March 7, 2013 Transportation Research Board (ABE50) Committee on Transportation Demand Management

Performance Measurement Dilemmas  Recognize multiple system objectives and needs  Passenger / freight  Urban / non-urban  Peak / non-peak times  Vehicles / people  Encourage sustainable, multi-modal investments  Measurement requires data  Availability vs relevance - data availability is important, but should not dictate selection of relevant measures  Utilize existing data in short-term / develop additional sources and future technology options

Measurement Considerations

 Congestion delays affect PEOPLE -  Vehicles don’t lose time, people do  Selected measures will influence future system decision-making  Focusing solely on vehicles may lead to only vehicle-oriented solutions that unintentionally may limit investment decisions  Improving person throughput requires cost- effective regional or area programs such as TDM that support corridor improvements Add Person Throughput as a System Performance and CMAQ Measure

Measuring Performance Solely by Vehicle Counts Can Be Misleading After HOT lanes (MNPass) conversion opening  The segment carried 10% MORE VEHICLES 1,605 vs 1,457 vehicles  BUT … 10% FEWER PEOPLE 2,593 vs 2,853 persons

Focus on Weekday Peak Periods  Obvious congestion in most urban areas  Peaks most directly affect the public – peak performance is most easily understood and improvements are visible and appreciated  Easiest time to affect congestion through cost-effective transit and TDM solutions  In line with Human Factors – people more inclined to shift modes for repetitive trips  Most effective period for promising new approaches: ICM, info technology, managed lanes

Extent of Congestion Varies Spatially and Temporally Wednesday, 8:30 AMSaturday, 9:00 PM Source: Google Traffic

Suggested Peak Measures  Corridors and regions – use benchmark measures and track changes over time  Person throughput (“passengership”) in addition to vehicle throughput (roadway / parallel modes, e.g. rail)  Person delay (peak delay - minutes ) due to congestion  Mode mix (same amount of vehicle delay with more SOV not as good as more alt mode)

Vehicles per Traveler Ratio Includes All Modes AVO  Relationship between AVO and vehicle trips is non-linear  Masks understanding of real goal = movement of people Increasing AVO from 1.10 to 1.25 seems “easy” But requires reduction from 90.9 to 80.0 vehicles per 100 travelers - a shift of 11 travelers for every 100 Vehicles per 100 Travelers

Data / Method Considerations

Measures Should Reflect Performance Objectives as well as Data Availability  Perfection of data availability should not determine measure selection  Acceptable sources are currently available as a starting point  Use existing data and reasonable surrogates in the short-term - phase in consistent / rigorous methods over time “Perfect is the enemy of the good” - Voltaire When you lose your keys, look for them where you lost them, not where the light is better

Measuring Person Throughput Requires Different Data  Transit ridership is generally available - NTD / automatic passenger counters  Measurement of CP/VP and overall person throughput requires data on vehicle occupancy  But methods /data are currently in place in various locations to estimate occupancy, including sampling

Many Communities Collect Passenger Data Now

Vehicle Occupancy IS Being Measured Today and More Measurement Options are Coming Existing Sources  Household Travel Surveys (regional and national)  Corridor / cordon counts  State of Commute surveys  Vanpool occupancy via NTD  American Community Survey  Crash data  Managed lane transponders Future Options  Photographic detection (video recognition)  In-vehicle systems (e.g., detecting passengers for proper air bag deployment)  Infrared sensors

Data Sources: Traveler Surveys Best occupancy data are collected using some form of local survey:  Regional household travel surveys  Employer / employee surveys – e.g. WA State Commute Trip Reduction Survey  Public / traveler surveys – e.g., “State of Commute” surveys in numerous metro regions  License plate reader surveys – Identify vehicles at random on roads, then contact the owner for a survey

Secondary Options when Local AVO Data are not Available  American Community Survey  Nationally available at sub-regional level  Commute mode and trip time – reflects most congested period  Annual data / 3-year and 5-year option for smaller geographic areas  Crash data  Toll / managed lane transponder data

Future Options – Coming Soon “ Multi-band infrared is the most promising of the roadside technologies, and the only one that has led to a product that is close to being marketed. It can distinguish human skin under all lighting conditions. Minnesota DOT developed and field-tested a prototype in 2000; it was claimed to be effective at detecting front-seat passengers through the windshield of vehicles driven at 50 mph, with an accuracy equal to that of human visual observation.” Robert Poole, Jr – “ Automating HOT Lanes Enforcement” Reason Foundation 2011

Case Study – San Francisco In 2002, the MTC used vehicle license plate readers and user survey to evaluate carpool characteristics/occupancy for HOV Lane Master Plan update. Data were used to:  Determine ‘Per Person Use of lanes’  Lane productivity (HOV vs Non-HOV lanes)  Travel time savings  Model future patterns & characteristics Data can be used to look at lanes, corridors, and at specific times

One Possible Phased Approach  Use ACS or other nationally-available regional-level data to establish baseline  2 years out, use NHS route-specific collection methods:  Traveler survey with route and time data;  License plate survey; or,  Field collection  In reporting NHS-specific data, incorporate NTD data for corresponding commuter and light rail lines

Summary – 4 Ps  Person throughput is equally important as vehicle throughput  Peak period is the time that is meaningful and most amenable to change  Preserves multi-modal focus  Phase-in as we go - perfect data not necessary – acceptable data available today

Contact  Lori Diggins, Chair, TRB Committee on Transportation Demand Management  Jason Pavluchuk, Government Relations for Association for Commuter Transportation