TRANSPORTATION RESEARCH BOARD Spatial Modeling for Highway Performance Monitoring System Data: Part 1 Tuesday, February 27, 2018 2:00-4:00 PM ET
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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 1
2 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
HIGHWAY PERFORMANCE MONITORING SYSTEM (HPMS) 3 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]
4 A PRODUCT OF THE STATEWIDE DATA COLLECTION PROGRAM 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 HPMS Environmental Analysis Engineering Applications [Adapted from FHWA - CPI Manual 2001]
5 HPMS CONTINUOUS PROCESS IMPROVEMENT Measure Outputs Identify At- Risk Areas Select Review Type Follow-up Prepare Implementat ion Plan HPMS as a Key Support Process - Reauthorization, Appropriations, etc. - Performance Planning - Congestion Modeling & Safety Analyze Processes Review Guidelines Make Recommend ations Outline Current Process [Adapted from FHWA - CPI Manual 2001]
6 SUGGESTED STATE HPMS PROCESSING CYCLE [HPMS Field Manual, 2016]
7 GENERALIZED HPMS PROCESS Inventory Data Collection Data Compilation Information Derivatives Information Presentation
8 HI HPMS FED. AID ROUTES - 2016 Island NHS Miles
HPMS DATA ITEMS 9 Route Linear Reference Systems (LRS) Inventory Signs, etc. Geometric Curve, Grades Pavement Distress, Transportation Performance Management Traffic Counts, Travel Time (new)
HPMS DATA ITEMS 10 [FHWA HPMS Manual 2016]
HPMS DATA ITEMS 11 [FHWA HPMS Manual 2016]
SAMPLE PANEL DATA ITEMS 12 [FHWA HPMS Manual 2016]
LINEAR REFERENCE SYSTEM (LRS) 13 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 = mile point MP 0 MP 3.5 MP 5 MP 6.5 MP 10 MP 15
LINEAR REFERENCE SYSTEM (LRS) 14 Segmented : Geometry Intersection based Route-level : Spatial Topology Combined: Link-Node Network
LINEAR REFERENCE SYSTEM (LRS) SPECIAL CASES 15 Roadway Gaps / Dog Legs Sta. 0+0 Sta. 1+50 Sta. 2+50 Sta. 5+0
LINEAR REFERENCE SYSTEM (LRS) SPECIAL CASES 16 Roadway Realignment / By-Pass Sta. 1+100 Sta. 1+150 Sta. 0+0 Sta. 1+50 Sta. 1+75 Sta. 1+200 Back = 1+75 Ahead Sta. 3+0
OAHU HPMS WITH NON-NHS & LOCAL ROUTES 17
18 INVENTORY DATA
INVENTORY DATA: PAVEMENT STRUCTURE HISTORY 19
INVENTORY DATA: PAVEMENT STRUCTURE HISTORY 20
HI DOT PAVEMENT THICKNESS 21
HI DOT PAVEMENT THICKNESS 22
MAINTENANCE INVENTORY DATA 23
DERIVED DATA MEDIAN WIDTH 24
INVENTORY DATA PROCESS: TRAVEL MONITORING 25
INVENTORY DATA PROCESS: TRAVEL MONITORING 26 TMG Requirements Travel Monitoring Plan Data Provider Annual Data Collection Station AADT, K, D, Peak HPMS Section AADT, K, D, Peak
TRAVEL MONITORING: INVENTORY DATA SAMPLING HPMS TABLE OF POTENTIAL SAMPLES (TOPS) 27 Functional Class Urban Code Facility Type Through Lanes AADT
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 33 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
AADT Obtained from Continuous Count Stations (24/7) 34
35 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.
MONTHLY FACTORS 36 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 245,000 240,000 235,000 230,000 225,000 220,000 215,000 210,000 ADT Factor 1.04 1.02 1 0.98 0.96 0.94 0.92
AADT Sample (2015 data): Station C7L Number of Counts 315 AADV = 231365 AADT = 231065 DHV (30 th Highest Hrly. Volume) = 11948 K = DHV/AADT * 100 = 5% Dmax = 53.6% 37
VEHICLE CLASSIFICATION 38 Source TMG 2016 pg. 3-37
AADT CLASS FACTORS 39 Single Station Class Factors for Station C7L 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
40 CLASSIFICATION DATA COMPUTATION (TO BE UPDATED WITH REAL DATA) Weekday Class Factors for Station C7L 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
AADT Short Duration Counts Factored using Continuous Count Stations 41 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
HPMS LINK TRAVEL DATA ASSIGNMENT 42
TRAVEL MONITORING: THE IDEAL ENVIRONMENT 43
TRAVEL MONITORING: THE IDEAL ENVIRONMENT 24/7 continuous counters 44
45 TRAVEL MONITORING: AN EVEN BETTER ENVIRONMENT Autonomous Self-Reporting Vehicles
TRAVEL MONITORING: THE REAL ENVIRONMENT 24/7 continuous counters Short Term Counters? No Counters 46?????
SOURCES OF ERROR Count coverage (continuous vs. short-term counters) 47 Equipment malfunction Vehicle classification Seasonal variations Daily variations
48 HPMS LINK TRAVEL DATA ASSIGNMENT Station Link Assignments Spatial Proximity with TOPS attribute filters (functional class, urban code, etc.) Cluster analysis
STATION LINK ASSIGNMENTS DEFINING ROADWAY SEGMENTS 49 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 [Ref. FHWA Traffic Monitoring Guide 2016]
DEFINING ROADWAY SEGMENTS 24/7 continuous counters Short Term Counters? No Counters 50 Segment Boundaries?????
DEFINING ROADWAY SEGMENTS 51???????
CONSIDERATIONS FOR ASSIGNING DATA FROM MONITORED TO UNMONITORED SEGMENTS 52 Functional Classification Urban vs. Rural boundaries Proximity
FUNCTIONAL CLASSIFICATION 53 All Roads Arterial Non- Arterial Principal Minor Collector Local Full Control Partial/ Uncontrolled Major Minor Interstate Other Freeways & Expressways Other Principal Arterial [Ref. FHWA and CDM Smith]
FUNCTIONAL CLASSIFICATION 54 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]
ESTIMATION PROCEDURE FOR UNMONITORED LINKS 55 Interstate, Principal Other Freeways & Expressways, Arterial Crosses Urban/Rural Boundary? Walk each route and assign average AADTs from closest links with counts Functional Class Minor Arterial, Major Collector, Minor Collector, Local Identify routes in each urban/rural boundary by FC For each link in each route, find closest route with similar FC and assign
WHAT IFS 56 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
A GEOGRAPHICALLY CLOSED SYSTEM 57 https://en.wikipedia.org/wiki/closed_system#/media/file:di agram_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? AAAAAATT ffffffffffffffff = AAAAAATT llllllllll_llllllllll_ffffffffffffffffffff? nn ii= 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 2016 Short Term Counts Continuous Counter
SPATIAL MODELING FOR HIGHWAY PERFORMANCE MONITORING SYSTEM DATA PART 2 ITEMS Step-by-step network AADT estimation process using spatial modeling Validation and reporting Ramp balancing Probe data opportunities and challenges 63
REFERENCES 64 FHWA HPMS Website and Field Manual 2016 Traffic Monitoring Guide Highway Functional Classification Concepts, Criteria and Procedures, 2013 Edition Continuous Process Improvement, 2001, FHWA
65 CONTACT INFORMATION Maaza Christos Mekuria, PhD, PE, PTOE Hawaii Department of Transportation maaza.c.mekuria@hawaii.gov Dan P. Seedah, PhD Asst. Research Scientist, Texas A&M Transportation Institute d-seedah@tti.tamu.edu
Today s Participants Jennifer Campbell, Oregon Department of Transportation, jennifer.k.campbell@odot.state.or.us Maaza Mekuria, Hawaii Department of Transportation, maaza.c.mekuria@hawaii.gov Daniel Seedah, Texas A&M Transportation Institute, d-seedah@tti.tamu.edu
Panelists Presentations http://onlinepubs.trb.org/onlinepubs/webinars/180227.pdf After the webinar, you will receive a follow-up email containing a link to the recording
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