Mark E. Hallenbeck Director Washington State Transportation Center (TRAC) University of Washington Seattle, WA USA

TRAC  WSDOT: “Has problems” Has money to solve problems  UW (and WSU) Bright people Need money Like to solve problems

TRAC  Big problem: WSDOT culture = University culture  TRAC’s role Help the two organizations work together (Communication!)

Mark’s Role at TRAC  Connect WSDOT staff and UW faculty/staff Any transportation related subject  Serve as a point of contact and problem solver  Perform research

But I also get to teach (but not this year)  Courses Intelligent Transportation Systems (when budgets allow) Urban Transportation Planning (used to)  Office location 1107 NE 45 th St, Suite #535 (west of the Ave.)

Major Personal Research Areas What can you measure, how do you measure it, and how can you use it to describe/improve system performance or meet policy needs? Congestion monitoring Intelligent transportation systems deployment and use Transportation’s role in growth management Traffic loading for pavement design

Congestion Monitoring The basic need is to monitor facility use and performance (real-time management/planning) Vehicle use Person use Speeds (travel time) Reliability (frequency of congestion)

Congestion Monitoring  These needs must be met in a manner that allows for: collection of adequate information lowest possible cost comparison between modes / strategies tracking of policy decisions

Congestion Monitoring - State Track congestion for Legislative / DOT policy needs Identify size and scope of congestion problem Allow evaluation of congestion relief efforts Allow for comparison / prioritization between locations

Congestion Measures ● V / C can be ●Hourly ●Daily ●Peak period ● How do you measure “C”? ● How do you measure “V”? ● How do you account for variation in “V”?

Congestion Measures Limitations in V / C have led to the adoption of alternative congestion measures, mostly: Travel time, Speed, or Delay

Using Intelligent Transportation Systems ITS provides a variety of “continuous” data collection / surveillance systems that can provide travel time and speed data New traffic control systems Vehicle location systems (the GPS in your phone can turn your car into a probe)

Time PMAM Time PMAM Montlake Blvd. 520 Arboretum 92nd Ave. B’vue. Way 148th Ave. NE 60th St. 84th Ave. Lk. Wash. Uncongested, near speed limit Restricted movement but near speed limit More heavily congested, mph Extremely congested, unstable flow Westbound SR 520 Traffic Profile General Purpose Lanes 1997 Weekday Average Eastbound

SR 520 Performance After Tolling  Daily, then weekly, then monthly reporting  Changes from initial condition  Summary statistics

Geographic ContextColorEqualsStatisticalChange

Volume Variability and Change

Peak- Period Cross-lake Trips 30 Peak-Period Person-Trips Across Lake Washington Post Tolling Peak-Period Person-Trips Across Lake Washington Pre Tolling

31 Increased SR 520 bus service more than 20 percentIncreased SR 520 bus service more than 20 percent o 15 percent ridership increase due to service investment o 10 percent ridership increase since tolling started Vanpools in the SR 520 corridor have increased 18 percentVanpools in the SR 520 corridor have increased 18 percent Park and ride usage is similar to pre-tolling with most lots remaining fullPark and ride usage is similar to pre-tolling with most lots remaining full Improved travel times on SR 520 means improved on-time performanceImproved travel times on SR 520 means improved on-time performance Successful Transit Deployment

Travel Time Variability and Change

Geographic Changes in Congestion NowBefore

Arterial Travel Times

Arterial Performance  So what about arterials? Can we use data collected at signals? Do we need to change how it is recorded/reported? How do we present that data? Can we use other data sources? Blue tooth readers? Vehicle probes Single points? Or actually tracking vehicles?

Arterial Performance  What about bike use?  What about pedestrian use? 

Potential Research questions  How do you collect bike/ped data? On a trail At an intersection Jaywalking behavior Along a corridor  How do you analyze these data? Sample plan? Sampling what? For what?  How do you report performance of bike/ped?

What About Freight (Trucks?)

Zone to Zone travel via GPS

ITS and Congestion Monitoring Data Sources How do we get data from existing control systems? How do we work with control systems to place surveillance in the right places? Do we use crowd sourced data? With what privacy controls? What do we ask for? How do we use it? What do we do with the data once we have it?

Research NW Region Freeway Performance  How do you take dissimilar data and make a cohesive, functional database / decision support system? Roadway performance (volume, speed) Incidents (accidents, disabled vehicles, debris) Special Events (unusual volumes) Weather  Note different geographic / temporal attributes

SHRP2 - LO3: Analytic Procedures for Determining Impacts of Reliability Mitigation Strategies  Determine the causes of congestion  Develop analytical metrics that describe how operational improvements reduce delays  (Results were also used for a WSDOT project looking at the benefits of incident response)

Normal Congestion Incident Condition X Congested Slower than free flow Faster than normal Slower than normal ABCDE Travelers entering at C or D do better under incident conditions Travelers using A to C experience unusually long delay Travelers going from A to E only experience moderate time increases

Major Blocking Crash Occurs 5:30 – 7 AM, ~Milepost 23

Probability of Being in Congestion: Rain Versus No Rain SR 520 Westbound From Bellevue to Seattle

Increase in Mean Travel Times With the Increase in Probability of Congestion Due to Rain I-90 Westbound from Issaquah

Comparison of Mean Travel Times With and Without the Influence of Incidents. I-5 Northbound Through the Seattle Central Business District

Expected Travel Time Under Different Conditions I-90 Issaquah Segment WB Weekdays This study measures the difference between “No Disruption” and “Incident” conditions – and calls it “incident caused”

Findings  Disruptions (crashes, incidents and bad weather) cause 30 percent of all delay on the Seattle metropolitan freeway system Crashes alone cause ~ 11 percent of delay  This equals 5,300,000 veh-hours of delay in 2006 Crashes cause 1,950,000 veh-hrs of delay

Findings  “Incidents” by themselves do not cause congestion The same is true for crashes  Incidents only cause congestion when the disruption they create causes functional capacity to fall below actual demand

Summary Effects of Incidents Assumes that a linear relationship applies (see report): Average Vehicle Delay per Minute of Incident 576 Veh-min / min Average Effect of a Lane Closure 814 veh-min/min

Variability in Incident Delays High Delay Segments CorridorVeh-Min Delay / Min of Incident Veh-Min Delay / Min of Closure I-405 Bellevue SB I-5 North Seattle SB SR 167 Renton NB I-5 South (Fed Way) SB I-405 Kennydale SB SR 520 WB Redmond Low Delay Segments CorridorVeh-Min Delay / Min of Incident Veh-Min Delay / Min of Closure I-90 Issaquah EB 10<1 I-90 Bellevue EB 1539 I-5 Everett SB6361 I-90 Issaquah WB 8553 I-405 North NB95268 SR 167 Auburn NB

There Are Other Important Benefits – Not Included in the Travel Time Benefit  Safety: Crash rates increase under incident conditions In AM peak, crash probability more than doubles on most corridors when an event (crash/incident) has affected travel In PM peak, crash probability increases by ~50% (Midday less stable, but on average it too doubles) At night there aren’t enough crashes to be statistically significant

ITS Can Also Give Information Directly to the Public

WSDOT’s Seattle Congestion Map

Transit Information (OneBusAway) Phone (voice or SMS) iPhoneWeb

Framework for Monitoring Change in VMT  RCW says that we will reduce VMT/Capita by 50% by 2050  Monitor whether the state is on track and why

VMT Reduction Dashboard  Primary Indicators Statewide VMT Statewide VMT / Capita  Programmatic Indicators Statewide annual transit ridership Percentage of people in SOV by major urban corridor (peak periods only) Trips Reduced by CTR program VMT reduced by CTR program Number of employees covered by CTR program Cost per VMT reduced by CTR VMT Reduced by specific projects (construction mitigation efforts by project) Volume of use of key non-motorized facilities (e.g., bike trail on SR 520) Special event vehicle volumes versus attendance CurrentTrend

VMT Reduction Dashboard  Regional or Jurisdiction Level Indicators County VMT County VMT / Capita County transit ridership Could use the number of revenue hours of transit service provided Walkscore (or combined Walk/Transit/Bike Score) per city/county Percent of population living within GTEC (by jurisdiction) Alternative: Percent of population living within an area with a Walkscore/Transit score of 80 (or some other value) Percent of jobs located within a GTEC (by jurisdiction) Alternative: Percent of jobs located within either walkscore, transit score, or bike score of 80 (or some other value) Volume of use of key non-motorized facilities (specific to jurisdiction) Mode split during peak periods to major job centers (or downtown, or…) Percent of planned walk/bike network completed  Indicators of Future Performance Number of cities in compliance with VMT reduction criteria Needs work to identify the criteria to be tracked (see Mark Anderson) Change in land use permits (sq ft?) granted within the last five years in GTECs or designated urban centers versus those granted outside of those areas (by jurisdiction) Census journey to work mode split (update frequency?) Vehicle registrations per capita

Growth Management: The Concept of Concurrency  Developed in support of growth management efforts  Development should not occur unless an adequate transportation system exists (or will exist) to handle that growth

The Initial Problem  How do we determine if “adequate transportation” exists?”  State law says that each jurisdiction defines “adequate” within their jurisdiction State routes aren’t included in the process

Cities Usually Measure Congestion (v/c)  Is roadway congestion the only “transportation facility” that matters?  Does the term “adequate transportation facilities” include other modes of travel?

Results: Other Limitations  Concurrency is applied only to “local” impacts, This skews development decisions Regional impacts soon “trump” local decisions  Many “solutions” require commitments from other jurisdictions No money source exists for these services No local control exists over these services

Regional Trip Making Origin: Trip concentration Destination: Trip distribution Line Haul Portion All three parts of a trip must be shared ride mode friendly if an alternative to the SOV is to be used.

Recommendations  Need both Local and Regional concurrency Local Determine key modes Permit / No permit decision Regional Remove financial incentives to export costs

GTEC Assistance  Cities understand what GTECs are  City politics and land developers do not always agree with the idea of compact, mixed use, walkable development

Measurement of Smart Growth Impacts  Research Questions: Does Smart Growth “work?” Is it simply self selection? Does it reduce VMT? Do people walk/bike more, if… I put in more sidewalks Mix uses

Measurement of Smart Growth Impacts  Current WSDOT projects: RideshareOnline Secondary function: document effectiveness of commute trip reduction efforts Our role: help with documentation Prediction of VMT reduction impacts Framework for monitoring VMT reductions

Destination Taking An Alternative Route  A project done in four-week time span for King County Funded through budget office At the request of the County executive In conjunction with Booze Allen  Also known as “Examination of Regional Tolling”

Initially Tolled Network

Traffic Load Data And Pavement Design Engineers Traffic Load Inputs For the MEPDG (Mechanistic-Empirical Pavement Design Guide)

Tandem Axle Load Distribution – Mixed Truck Loads Maximum Weight in a Given Axle Weight Group (x 1,000 lbs) Fraction of Tandem Axles In Weight Group

ESAL Comparison Lightly Loaded Tandems = (flexible) Moderately Loaded Tandems = Heavily Loaded Tandems = Simple conclusion: Not knowing the loaded/unloaded condition can equal a 3X error in life expectancy

Time of Day Truck Volume Variation

Day of Week Truck Volume Variation

Seasonal Truck Volume Variation