Upload
others
View
0
Download
0
Embed Size (px)
Citation preview
MOVING THINKINGFORWARD
Travel Time Reliability in the SLOCOG Region
October 27, 2014
San Luis Obispo Council of Governments
Transportation Education Series
1
MOVING THINKINGFORWARD
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
2
MOVING THINKINGFORWARD
Presenters
• Jim Damkowitch, Kittelson & Associates, Principal Planner
• Dr. Richard Dowling, Kittelson & Associates, Senior Principal
• Darryl dePencier, Kittelson & Associates, Planner
• Jorge Aguilar, Wallace Group, Principal
3
MOVING THINKINGFORWARD
Host: San Luis Obispo Council of Governments Sponsors: Kittelson & Associates and Wallace Group
4
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
MOVING THINKINGFORWARD
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)
5
MOVING THINKINGFORWARD
US101 Mobility Master Plan
6
MOVING THINKINGFORWARD
Smart Mobility Framework
7
MOVING THINKINGFORWARD
Smart Mobility Framework
8
MOVING THINKINGFORWARD
Smart Mobility Framework
9
MOVING THINKINGFORWARD
US101 Corridor Mobility Master Plan
10
MOVING THINKINGFORWARD
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?
11
MOVING THINKINGFORWARD
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
12
MOVING THINKINGFORWARD
Who are the Customers?
13
MOVING THINKINGFORWARD
Customer Needs - Travelers
14
MOVING THINKINGFORWARD
Customer Needs – Goods Movement
15
MOVING THINKINGFORWARD
What are the Causes of Unreliability?
16
MOVING THINKINGFORWARD
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
17
http://ops.fhwa.dot.gov/publications/tt_reliability/TTR_Report.htm
MOVING THINKINGFORWARD
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
18
MOVING THINKINGFORWARD
Why is Travel Time Reliability Important?
• Paradigms are shifting
– MAP-21 (Federal)
– SB 375 (CA)
– SB 743 (CA)
19
MOVING THINKINGFORWARD
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
20
MOVING THINKINGFORWARD
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.
21
MOVING THINKINGFORWARD
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
22
MOVING THINKINGFORWARD
Travel Time Distribution
23
MOVING THINKINGFORWARD
Characterizing Reliability
24
MOVING THINKINGFORWARD
Buffer Time
25
Buffer Time
MOVING THINKINGFORWARD
The TTI Statistic
26
MOVING THINKINGFORWARD
The Percent < 45 Mph
27
MOVING THINKINGFORWARD
Commonly Applied Reliability Measures
28
MOVING THINKINGFORWARD
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
29
MOVING THINKINGFORWARD
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
30
MOVING THINKINGFORWARD
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
31
MOVING THINKINGFORWARD
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….
32
MOVING THINKINGFORWARD
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.
33
MOVING THINKINGFORWARD
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.
34
MOVING THINKINGFORWARD
1965-2000 HCM Methods
35
MOVING THINKINGFORWARD
HCM 2010+ Reliability Method
36
MOVING THINKINGFORWARD
• 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?
37
MOVING THINKINGFORWARD
• FHWA Primer on Incorporating Reliability into the Congestion Management Process (FHWA-HOP-14-034)
– Available on FHWA website (soon)
References on Reliability
38
MOVING THINKINGFORWARD
• Highway Capacity Manual
• Chapters 36 and 37 (on web)
References on Reliability (2)
39
MOVING THINKINGFORWARD
• 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
40
MOVING THINKINGFORWARD
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
41
MOVING THINKINGFORWARD
US 101 Reliability Measured
42
£¤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
MOVING THINKINGFORWARD
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
43
MOVING THINKINGFORWARD
Roadside Detectors
• Devices placed adjacent to the roadway – mounted to existing structures when possible
• Detection range ~300’
44
MOVING THINKINGFORWARD
BlueMAC Detectors
45
MOVING THINKINGFORWARD
System Components
46
• Detector / Transmitter
– Processor
– A/C or battery powered
– Solar charger / controller
– Connector ports
– Housing
MOVING THINKINGFORWARD
Travel Time Reporting
47
• Southbound – Los Osos Valley Road to Avila Beach Road
– Bi-modal PM peak observed – June 26th, 2014
MOVING THINKINGFORWARD
Travel Characteristics
• Trip Distance
• Expected Travel Time
• Number of Trips
• Average Speed (mph)
• Average Travel Time
• Standard Deviation
• 15th Percentile
• 85th Percentile
• 95th Percentile
48
4.03
202 (3:22)
1214
48.5
299 (4:59)
146.3
200 (3:20)
509 (8:29)
583 (9:43)
MOVING THINKINGFORWARD
PM Travel Time Reliability (4PM to 6PM)
• Sunday
• Monday
• Tuesday
• Wednesday
• Thursday
• Friday
• Saturday
49
(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%
MOVING THINKINGFORWARD
Neighborhood Studies
• Linda Vista Neighborhood – BCS National Championship Game
50
MOVING THINKINGFORWARD
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
51
MOVING THINKINGFORWARD
• 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
52
MOVING THINKINGFORWARD
• 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
53
MOVING THINKINGFORWARD
• 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
54
MOVING THINKINGFORWARD
US 101 Case Study in Reliability
55
MOVING THINKINGFORWARD
US 101 Case Study in Reliability
56
MOVING THINKINGFORWARD
• 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
57
MOVING THINKINGFORWARD
• 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
58
MOVING THINKINGFORWARD
• 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
59
MOVING THINKINGFORWARD
Questions?
60
MOVING THINKINGFORWARD
US 101 Case Study Findings
61
MOVING THINKINGFORWARD
Questions?
62