spatial modeling for highway performance monitoring system...
TRANSCRIPT
Spatial Modeling for Highway Performance Monitoring System Data: Part 1
Tuesday, February 27, 2018
2:00-4:00 PM ET
TRANSPORTATION RESEARCH BOARD
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requirements of the Registered Continuing Education Providers Program.
Credit earned on completion of this program will be reported to RCEP. A
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may be deemed or construed to be an approval or endorsement by RCEP.
Purpose Discuss how to incorporate spatial modeling and statistical tools to enhance the quality and productivity of travel monitoring data.
Learning Objectives At the end of this webinar, you will be able to: • Identify Highway Performance Monitoring System (HPMS) traffic data
needs and requirements • Describe the linear referencing system and its use in HPMS processing • Describe cardinal and spatial joins through attribute, connectivity,
proximity, and similarity
TRB WEBINAR: SPATIAL MODELING FOR HIGHWAY PERFORMANCE MONITORING SYSTEM DATA PART 1 - FEBRUARY 27, 2018
Maaza Christos Mekuria, PhD, PE, PTOE Dan P. Seedah, PhD
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WEBINAR OUTLINE
Overview of HPMS
Description of HPMS Travel Data Items
Relationship between HPMS Data items
Linear Referencing System
Sample Panels and HPMS items
Leveraging Spatial Analysis
Q & A session
2
HIGHWAY PERFORMANCE MONITORING SYSTEM (HPMS)
A national level highway information system that provides data on the extent, condition, performance, use and operating characteristics of the nation's highways.
[Ref. FHWA]
3
A PRODUCT OF THE STATEWIDE DATA COLLECTION PROGRAM
[Adapted from FHWA - CPI Manual 2001]
Planning Process Statewide Planning
Project Prioritization and Funding Local/Regional Planning
Corridor Studies, ITS Strategies Freight Planning
Data Collection Programs Management Systems
Inventory, Condition, Travel
Environmental Analysis Engineering Applications
HPMS
4
HPMS CONTINUOUS PROCESS IMPROVEMENT
[Adapted from FHWA - CPI Manual 2001]
Identify At-Risk Areas Select
Review Type
Analyze Processes
Review Guidelines
Outline Current Process
Make Recommend
ations
Prepare Implementat
ion Plan
Follow-up
Measure Outputs
HPMS as a
Key Support Process
- Reauthorization, Appropriations, etc. - Performance Planning
- Congestion Modeling & Safety
5
SUGGESTED STATE HPMS PROCESSING CYCLE
[HPMS Field Manual, 2016]
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GENERALIZED HPMS PROCESS
Inventory Data Collection
Data Compilation
Information Derivatives
Information Presentation
7
HI HPMS FED. AID ROUTES - 2016
Island NHS Miles
8
HPMS DATA ITEMS
Route – Linear Reference Systems (LRS)
Inventory – Signs, etc.
Geometric – Curve, Grades
Pavement – Distress, Transportation Performance Management
Traffic – Counts, Travel Time (new)
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HPMS DATA ITEMS
[FHWA HPMS Manual 2016]
10
HPMS DATA ITEMS
[FHWA HPMS Manual 2016]
11
SAMPLE PANEL DATA ITEMS
[FHWA HPMS Manual 2016]
12
LINEAR REFERENCE SYSTEM (LRS)
A system where features (points or segments) are localized by a measure along a linear element.
[Introducing the Linear Reference System in GRASS, 2004]
MP 0 MP 5 MP 10 MP 15
MP = mile point
MP 3.5 MP 6.5
13
LINEAR REFERENCE SYSTEM (LRS)
Segmented : Geometry – Intersection based
Route-level : Spatial Topology
Combined: Link-Node Network
14
LINEAR REFERENCE SYSTEM (LRS) – SPECIAL CASES
Roadway Gaps / Dog Legs
Sta. 0+0
Sta. 1+50
Sta. 2+50
Sta. 5+0
15
LINEAR REFERENCE SYSTEM (LRS) – SPECIAL CASES
Roadway Realignment / By-Pass
Sta. 0+0
Sta. 1+50
Sta. 1+75
Sta. 3+0
Sta. 1+100
Sta. 1+150
Sta. 1+200 Back = 1+75 Ahead
16
OAHU HPMS WITH NON-NHS & LOCAL ROUTES 17
INVENTORY DATA 18
INVENTORY DATA: PAVEMENT STRUCTURE HISTORY 19
INVENTORY DATA: PAVEMENT STRUCTURE HISTORY 20
HI DOT PAVEMENT THICKNESS 21
22
HI DOT PAVEMENT THICKNESS
MAINTENANCE INVENTORY DATA 23
DERIVED DATA – MEDIAN WIDTH 24
INVENTORY DATA PROCESS: TRAVEL MONITORING
25
INVENTORY DATA PROCESS: TRAVEL MONITORING
Travel Monitoring Plan
Annual Data Collection
Station AADT, K, D, Peak
HPMS Section AADT, K, D, Peak
TMG Requirements
Data Provider
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TRAVEL MONITORING: INVENTORY DATA SAMPLING
Functional Class
Urban Code
Facility Type
Through Lanes
AADT
HPMS TABLE OF POTENTIAL SAMPLES (TOPS)
27
HPMS TRAVEL INVENTORY ITEMS 28
DERIVED HPMS TRAVEL ITEMS 29
ANNUAL TRAVEL MONITORING DERIVED DATA 30
RAW AND DERIVED DATA 31
RAW AND DERIVED DATA 32
TRAVEL MONITORING QA/QC CHECKS
1. Historical 24hr volume count consistency
2. Historical 24hr directional count consistency
3. Compare with nearby Permanent Station
4. Check Historical AM/PM Peak by Direction
33
AADT Obtained from
Continuous Count Stations – (24/7)
34
K-factor - The design hour volume (30 th largest hourly volume for a given calendar year) as a percentage of AADT. Computed using continuous count sites by ranking the observed hourly volumes.
Directional Factor (D) - The percent of design hour volume (30th largest hourly volume for a given calendar year) flowing in the higher volume direction.
35
MONTHLY FACTORS
ISLAND STATION Yr Oahu C7L 2015
Month ADT Factor January 223716 1.03 February 230187 1.00 March 231319 1.00 April 233336 0.99 May 227484 1.02 June 231540 1.00 July 236930 0.98 August 229611 1.01 September 230538 1.00 October 232752 0.99 November 227738 1.01 December 241228 0.96 AADT 231065
0.92
0.94
0.96
0.98
1
1.02
1.04
210,000
215,000
220,000
225,000
230,000
235,000
240,000
245,000
ADT Factor
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AADT
Sample (2015 data): Station – C7L Number of Counts – 315 AADV = 231365 AADT = 231065 DHV (30th Highest Hrly. Volume) = 11948 K = DHV/AADT * 100 = 5% Dmax = 53.6%
37
VEHICLE CLASSIFICATION
Source TMG 2016 – pg. 3-37
38
AADT CLASS FACTORS
Single Station Class Factors for Station C7L
39
Class MC PC LTrk Bus SU CU TotalVol MADTvc (Jan) 201 142309 62643 875 12028 1205 219261 AADTVC 207 140617 65987 968 17534 1230 226544 Class Factors 1.03 0.99 1.05 1.11 1.46 1.02
CLASSIFICATION DATA COMPUTATION (TO BE UPDATED WITH REAL DATA)
Class MC PC LTrk Bus SU CU Sunday 1.15 1.24 1.37 3.04 1.34 5.21 Monday 0.97 1.00 0.98 0.87 0.98 0.80 Tuesday 0.94 0.96 0.94 0.80 0.93 0.78
Wednesday 0.93 0.95 0.94 0.81 0.94 0.76 Thursday 0.93 0.96 0.94 0.82 0.96 0.78
Friday 0.94 0.92 0.89 0.79 0.91 0.80 Saturday 1.22 1.03 1.07 1.82 1.03 2.33
Weekday Class Factors for Station C7L
40
AADT Short Duration Counts – Factored using Continuous Count Stations
COUNTDATE CYCLES PC LT_TRKS BUS SU CU 1/22/2015 (Thursday) 220 18627 4662 88 120 104 1/23/2015 (Friday) 206 19343 4892 78 131 131 Factor Jan. 1.03 0.99 1.05 1.11 1.46 1.02 Thursday Factor 0.93 0.96 0.94 0.82 0.96 0.78 Friday Factor 0.94 0.92 0.89 0.79 0.91 0.80 Adjusted Data 210 18318 4514 74 119 83
199 18234 4503 63 123 108 Average 205 18276 4509 69 121 95
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HPMS LINK TRAVEL DATA ASSIGNMENT
42
TRAVEL MONITORING: THE IDEAL ENVIRONMENT 43
TRAVEL MONITORING: THE IDEAL ENVIRONMENT 24/7 continuous counters
44
TRAVEL MONITORING: AN EVEN BETTER ENVIRONMENT
Autonomous Self-Reporting Vehicles
45
TRAVEL MONITORING: THE REAL ENVIRONMENT 24/7 continuous counters Short Term Counters
?
?
?
? ?
? No Counters
46
Count coverage (continuous vs. short-term counters)
Equipment malfunction
Vehicle classification
Seasonal variations
Daily variations
SOURCES OF ERROR 47
Station – Link Assignments
Spatial Proximity with TOPS attribute filters (functional class, urban code, etc.)
Cluster analysis
HPMS LINK TRAVEL DATA ASSIGNMENT 48
Homogeneous traffic volume (± 10%)
For controlled access roadways (e.g. interstate system), in-between interchanges is appropriate.
Urban vs. rural boundaries
Low volume rural roadways
STATION – LINK ASSIGNMENTS DEFINING ROADWAY SEGMENTS
[Ref. FHWA Traffic Monitoring Guide 2016]
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DEFINING ROADWAY SEGMENTS 24/7 continuous counters Short Term Counters
?
?
?
? ?
? No Counters
Segment Boundaries
50
DEFINING ROADWAY SEGMENTS
? ?
?
?
? ?
?
51
CONSIDERATIONS FOR ASSIGNING DATA FROM MONITORED TO UNMONITORED SEGMENTS
Functional Classification
Urban vs. Rural boundaries
Proximity
52
FUNCTIONAL CLASSIFICATION
All Roads
Arterial
Principal
Full Control
Interstate Other
Freeways & Expressways
Partial/ Uncontrolled
Other Principal Arterial
Minor
Non-Arterial
Collector
Major Minor
Local
[Ref. FHWA and CDM Smith]
53
FUNCTIONAL CLASSIFICATION
Urban and Rural
1. Interstate
2. Principal Other Freeways and Expressways
3. Principal Other Arterial
4. Minor Arterial
5. Major Collector
6. Minor Collector
7. Local
[Ref. FHWA]
54
ESTIMATION PROCEDURE FOR UNMONITORED LINKS
Functional Class
Interstate, Principal Other
Freeways & Expressways,
Arterial
Minor Arterial, Major Collector, Minor Collector,
Local
Crosses Urban/Rural Boundary?
Walk each route and assign average AADTs
from closest links with counts
Identify routes in each
urban/rural boundary by
FC
For each link in each route, find closest
route with similar FC and assign
55
WHAT IFS …
What if no functional class within an urban/rural boundary was counted? Use estimates from other urban/rural boundaries
with similar characteristics e.g. population
What if no functional class within the entire state with similar urban/rural characteristics is found? Signifies limitation in statewide data collection
program Explore concept of “geographically closed system”
56
“A GEOGRAPHICALLY CLOSED SYSTEM” 57
https://en.wikipedia.org/wiki/Closed_system#/media/File:Diagram_Systems.png
“A GEOGRAPHICALLY CLOSED SYSTEM” ASSUMPTIONS IN TRAFFIC FLOW ESTIMATION
58
Minor Arterials, Major and Minor Collectors, and Local Roadways account for majority of traffic flow in an urban/rural boundary
Can AADT estimates be derived based on the hierarchical relationship between roadway networks?
𝐴𝐴𝐴𝐴𝐴𝐴𝑇𝑇𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 = ∑ 𝐴𝐴𝐴𝐴𝐴𝐴𝑇𝑇𝑓𝑓𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙_𝑓𝑓𝑙𝑙𝑙𝑙𝑙𝑙𝑓𝑓_𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑙𝑙𝑓𝑓 ?𝑛𝑛𝑓𝑓= 0
OAHU ROADWAYS AND TRAFFIC COUNTS 59
OAHU ROADWAYS AND TRAFFIC COUNTS 60
OAHU ROADWAYS AND TRAFFIC COUNTS 61
Continuous Counter
OAHU ROADWAYS AND TRAFFIC COUNTS 62
Continuous Counter
2016 Short Term Counts
SPATIAL MODELING FOR HIGHWAY PERFORMANCE MONITORING SYSTEM DATA – PART 2 ITEMS
63
Step-by-step network AADT estimation process using spatial modeling
Validation and reporting
Ramp balancing
Probe data opportunities and challenges
REFERENCES
FHWA HPMS Website and Field Manual
2016 Traffic Monitoring Guide
Highway Functional Classification Concepts, Criteria and Procedures, 2013 Edition
Continuous Process Improvement , 2001, FHWA
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CONTACT INFORMATION
Maaza Christos Mekuria, PhD, PE, PTOE Hawaii Department of Transportation [email protected] Dan P. Seedah, PhD Asst. Research Scientist, Texas A&M Transportation Institute [email protected]
65
Today’s Participants
• Jennifer Campbell, Oregon Department of Transportation, [email protected]
• Maaza Mekuria, Hawaii Department of Transportation, [email protected]
• Daniel Seedah, Texas A&M Transportation Institute, [email protected]
Panelists Presentations
http://onlinepubs.trb.org/onlinepubs/webinars/180227.pdf
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containing a link to the recording
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