travel time reliability in the slocog...
TRANSCRIPT
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Travel Time Reliability in the SLOCOG Region
October 27, 2014
San Luis Obispo Council of Governments
Transportation Education Series
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Transportation Education Series - Agenda
• Grab Some Free Lunch !!
• Introductions and Presentation Format
• Travel Time Reliability
– US101 Mobility Master Plan
– What is Travel Time Reliability?
– Data Collection for the Travel Time Reliability Metric
– Travel Time Reliability in the SLOCOG Region
• Conclusions and Q/A
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Presenters
• Jim Damkowitch, Kittelson & Associates, Principal Planner
• Dr. Richard Dowling, Kittelson & Associates, Senior Principal
• Darryl dePencier, Kittelson & Associates, Planner
• Jorge Aguilar, Wallace Group, Principal
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Host: San Luis Obispo Council of Governments Sponsors: Kittelson & Associates and Wallace Group
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SLOCOG Member Agencies:
• Paso Robles • Atascadero • San Luis Obispo • Pismo Beach • Arroyo Grande • Grover Beach • County of San Luis Obispo
Regional Partners:
• Caltrans District 5 • San Luis Obispo Regional Transit Authority • San Luis Obispo Air Pollution Control District
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KAI - Transportation Education Series (TES)
• Promote professional development & advancement
• Wide range of topics (what’s of interest)
– Travel Time Reliability (today)
– Multi-modal LOS & Highway Safety Manual (SLO July 17, 2012)
– KAI and Wallace Group plan to co-host another TES in the Feb-March timeframe on the topic of Emerging Trends in Planning and Design
• TES counts for Continuing Education Requirement credits (2.0)
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US101 Mobility Master Plan
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Smart Mobility Framework
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Smart Mobility Framework
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Smart Mobility Framework
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US101 Corridor Mobility Master Plan
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US 101 Corridor Mobility Master Plan & Travel Time Reliability
• Incorporated as part of the freeway operational analysis
• Travel Time Reliability – first application in District 5
• Travel Time Reliability represented by “Buffer Time” – Buffer Time – added increment of time required to ensure you reach
your destination at the desired time 95% of the time.
– How much earlier do you need to leave your home to ensure arriving on time.
– Time you could spend at home (effect is assumed to be similar to when you experience delay while driving your car i.e., travel time delay)
• Buffer time reduction and delay reduction added to yield total monetary time saving benefits of US101 mainline improvement concepts
• Results? But first - what is travel time reliability?
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What is Travel Time Reliability?
• Why is Travel Time Reliability Important?
• Travel Time Reliability Concepts and definitions
• Travel Time Reliability applications for performance measurement
• Travel Time Reliability analysis tools
• Data needs for measuring travel time reliability
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Who are the Customers?
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Customer Needs - Travelers
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Customer Needs – Goods Movement
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What are the Causes of Unreliability?
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Why is Travel Time Reliability Important?
1) Our roadway networks are more frequently operating in a near or above capacity
2) Uncertainties in travel time adversely impact us in multiple ways
3) We don’t have a way to measure the benefit of many of our strategies and investments
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http://ops.fhwa.dot.gov/publications/tt_reliability/TTR_Report.htm
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Why is Travel Time Reliability Important?
• Old days (Capacity-oriented)
– Network build-out and expansion
– Secure funding environment
– Traditional performance metrics
• New way (Reliability-oriented)
– How best to manage the system we have
– Financial, environmental and public perception problems
– Improvements that affect reliability more than capacity
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Why is Travel Time Reliability Important?
• Paradigms are shifting
– MAP-21 (Federal)
– SB 375 (CA)
– SB 743 (CA)
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Why is Travel Time Reliability Important?
• It will be required – MAP-21
– FHWA Notice of Proposed Rule Making – February 2015
• You miss out on many cost-effective solutions unless you consider reliability. – TSM&O – Traffic system management and operations strategies
• Ramp metering, HOV lanes, Express Lanes, signal optimization
– Active Traffic and Demand Management strategies
• Proactive traffic management (anticipating breakdowns).
• Dynamic ramp metering, Dynamic tolls, traffic adaptive controls
• Speed harmonization, advanced queue warning, Traveler Info.
– Getting longer service lives out of expensive capacity improvements
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Definition
• Travel time reliability is:
– The distribution of travel times that a traveler should anticipate if starting a trip at a given point at a given time and day.
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Alternative Definitions
• Reliability is described by the variability in travel times
• Reliability is the amount of time that the system fails to perform adequately (classical system definition)
• Reliability is the predictability of users’ travel time experience
• Reliability is the likelihood of arriving on-time
– All are valid but the underlying theme is that travel times for the same trip are not consistent from day-to-day for a variety of reasons
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Travel Time Distribution
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Characterizing Reliability
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Buffer Time
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Buffer Time
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The TTI Statistic
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The Percent < 45 Mph
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Commonly Applied Reliability Measures
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Is it hard?
• It can be.
– Imagine doing HCM analysis 1,000 times.
• But many tools available or under development that will make it easier (less hard).
– FHWA NPMRDS travel time monitoring data set for NHS
– University of Florida/FDOT spreadsheet
– SHRP2-C11 Model
– HCM 2010 Update
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Tools for Reliability
• Measuring Reliability
– FHWA NPMRDS travel time monitoring data set for NHS
• Predicting or Estimating Reliability
– University of Florida/FDOT spreadsheet
– SHRP2-C11 Method
– HCM 2010 Update
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FHWA NPMRDS
• The Federal Highway (FHWA) National Performance Management Research Data Set (NPMRDS) – A vehicle-probe (cell phone tracking) travel time data base for the
National Highway System (NHS)
• Data collected by HERE (Nokia)
– Average travel times by TMC links for every 5 minutes of every day back to October 2013.
• Passenger cars and trucks (FHWA vehicle classes 7 and 8)
– Its big. Requires GIS expertise and large database tools
• MS Access and Excel not big enough
– Its Accurate – No data smoothing, No interpolation.
– Available to MPO’s and State DOT’s
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University of Florida/FDOT • The UF/FDOT Reliability Spreadsheets
– Freeway
– Arterials
• How it works – Allocate AADT to each hour of year
– Compute recurring congestion using HCM capacities
– For each hour of 24 hour day compute travel times for 24 possible scenarios combining weather, incidents, work zones
– Assign probabilities to each scenario
– Compute reliability statistics
• Lots and lots of assumptions built in – Seasonal traffic variation, incident frequencies, weather
frequencies….
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SHRP2-C11 Method • Developed for estimating freeway reliability
• How it works – Estimate recurring delay based on peak hour speed.
– Estimate incident related delay based on peak hour v/c ratio.
– Compute average annual travel time with incidents.
– Compute 95th percentile travel time.
– Compute percent of trips below 45 mph.
• Sensitivities – Sensitive to regular peak hour congestion
– User must bring in outside data to estimate impacts of ATDM strategies.
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HCM Update • Under Development
• Available in 2 years
• How it works
– Create thousands of scenarios combining weather, incidents, demand, work zones.
– Select capacity adjustments for each scenario.
– Use Monte Carlo to select a few hundred for HCM analysis.
– Run HCM analyses.
– Tally statistics.
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1965-2000 HCM Methods
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HCM 2010+ Reliability Method
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• Nobody really knows.
• US Experience (Exhibit 37-1 HCM)
– 95% TTI on US Urban Freeways ranges from 1.09 to 3.60
• Median is 1.47
– 95% TTI on US Urban Arterials ranges from 1.27 to 1.98
• Median is 1.44
• Recent TRB paper
What is Good Reliability?
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• FHWA Primer on Incorporating Reliability into the Congestion Management Process (FHWA-HOP-14-034)
– Available on FHWA website (soon)
References on Reliability
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• Highway Capacity Manual
• Chapters 36 and 37 (on web)
References on Reliability (2)
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• To Predict or Estimate Reliability – Demand Variability (Seasonality)
– Weather frequencies
• Light, medium, heavy Rain, snow
– Incident and work zone frequencies
• Number of lanes blocked
• Duration
• To Measure Reliability – Hourly speeds and volumes 24/7 for 6 months to a year
– FHWA NPMRDS
Data Needs for Reliability
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Case Study – SLOCOG 101 Mobility Master Plan
• Reliability measured using Bluetooth detection
• Reliability estimated University of Florida method
• Reliability was predicted by applying UF method growth applied to measured baseline
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US 101 Reliability Measured
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£¤101
£¤101
1661
1
58
46
46 41
41
Paso Robles
Atascadero
San Luis Obispo
Arroyo GrandePismo Beach
OceanoGrover Beach
Morro Bay
¯• 10 detectors deployed along US 101
• 9 individual segments
• Data was collected continuously for 2 months
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What is Detected?
• Any “discoverable” Bluetooth device!
– Cell phones, game systems, laptops, some cars themselves
• Unique ID number (MAC Address) for all Bluetooth devices
– Only partial address is stored to protect anonymity
• Any device seen by two or more detectors can be tracked as a trip
– Typically varies from 5 – 15 % of traffic
– Provides travel time / average speed / direction
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Roadside Detectors
• Devices placed adjacent to the roadway – mounted to existing structures when possible
• Detection range ~300’
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BlueMAC Detectors
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System Components
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• Detector / Transmitter
– Processor
– A/C or battery powered
– Solar charger / controller
– Connector ports
– Housing
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Travel Time Reporting
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• Southbound – Los Osos Valley Road to Avila Beach Road
– Bi-modal PM peak observed – June 26th, 2014
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Travel Characteristics
• Trip Distance
• Expected Travel Time
• Number of Trips
• Average Speed (mph)
• Average Travel Time
• Standard Deviation
• 15th Percentile
• 85th Percentile
• 95th Percentile
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4.03
202 (3:22)
1214
48.5
299 (4:59)
146.3
200 (3:20)
509 (8:29)
583 (9:43)
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PM Travel Time Reliability (4PM to 6PM)
• Sunday
• Monday
• Tuesday
• Wednesday
• Thursday
• Friday
• Saturday
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(95th Percentile TT – Avg TT) / Average TT
(237s – 215s) / 215s * 100% = 10%
(247s – 217s) / 217s * 100% = 14%
(250s – 221s) / 221s * 100% = 13%
(244s – 222s) / 222s * 100% = 10%
(271s – 229s) / 229s * 100% = 18%
(320s – 260s) / 260s * 100% = 23%
(251s – 219s) / 219s * 100% = 15%
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Neighborhood Studies
• Linda Vista Neighborhood – BCS National Championship Game
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Cost
• Digiwest unit cost (max - $3,525 / unit) including
– BlueMAC detector with housing and power hookups
– Initial installation (if mounting locations are accessible via ladder)
– 1 Year Cellular services for data transmission (can also communicate via Fiber Optic or other existing communications network)
– 1 Year Cloud services for data storage
– BlueMAC website for data analytics and reporting
– Leasing and project specific costing available by request
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• Objective – Incorporate Travel time reliability as a metric used in the B/C analysis – Buffer time (during typical weekday: Tuesday – Thursday)
• Approach Existing Conditions – Vehicle Operating Conditions
• Published Traffic Volumes and Counts
• Bluetooth Data (4 months)
– Incidents • Incident Inventory in PeMS (12 months)
• CHP Reports (12 months)
– Weather • Paso Robles Airport (12 months)
• San Luis Obispo Airport (12 months)
– Work zones • Caltrans Website
• Caltrans PeMS (12 months)
US 101 Case Study in Reliability
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• Approach Future Conditions – Travel Demand
• SLOCOG Travel Demand Model (NCHRP-255 Adjustments for Daily Volumes) • Caltrans K & D Factors for full 24-hour Distribution • FDOT Procedure
– Expanded procedure for bi-directional results – Validated to baseline TTI
– Incidents (same as baseline – allowed FDOT Procedure to adjust based on volume) – Weather (same as baseline – allowed FDOT Procedure to adjust based on volume) – Work zones (same as baseline – allowed FDOT Procedure to adjust based on volume)
• FDOT Procedure (recap) – Compute recurring congestion using HCM capacities – For each hour of 24 hour day compute travel times for 24 possible scenarios combining
weather, incidents, work zones – Assign probabilities to each scenario – Compute reliability statistics (buffer time, BTI, travel time, TTI etc.)
• Delta Method: (FDOT Future – FDOT Baseline) + Empirical Baseline
US 101 Case Study in Reliability
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• Annualize 24-hour Distribution Effects on Capacity – Weather
– Incidents
– Work zones
– Recurring congestion
• Annualize 24-hour Distribution Effects on Speeds – Weather
– Incidents
– Work zones
– Recurring congestion
US 101 Case Study in Reliability
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US 101 Case Study in Reliability
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US 101 Case Study in Reliability
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• Travel Time Reliability on US 101
– Generally good reliability corridor-wide
• < 8 minutes Southbound
• < 4 minutes Northbound
– Anticipated to not dramatically change in the future
• Weather not a significant factor
• Work zones not a significant factor
• Collision rates generally at or below statewide average for like facilities
US 101 Case Study Findings
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• Travel Time Reliability on US 101
– Where do reliability issues occur:
• Five-City Area and City of San Luis Obispo
– Southbound Direction
– Northbound Direction (Five-City Area)
• Correlates to where the greatest congestion is projected to occur
• Supports US101 Mobility Master Plan – Buffer Time Increased B/C of HOV Improvement in Segment 1 by 8%
– Buffer Time Increased B/C of HOV Improvement in Segment 2 by 4%
US 101 Case Study Findings
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• Travel Time Reliability Metric
– We did it
• Wasn’t too painful
• Learned from our experience
– More than 4-months of data (April – August)
– Would have been nice to test other reliability tools
– Expand analysis to weekend
• Consistent with MAP-21 – provides greater support US101 Mobility Master Plan Recommendations
• Supports PSR-PDS Release by SLOCOG for examining operational improvement on SB US 101 Five-City Area
US 101 Case Study Findings
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Questions?
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US 101 Case Study Findings
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Questions?
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