TRANSPORTATION RESEARCH BOARD. Spatial Modeling for Highway Performance Monitoring System Data: Part 1. Tuesday, February 27, :00-4:00 PM ET

Transcription

1 TRANSPORTATION RESEARCH BOARD Spatial Modeling for Highway Performance Monitoring System Data: Part 1 Tuesday, February 27, :00-4:00 PM ET

2 The Transportation Research Board has met the standards and requirements of the Registered Continuing Education Providers Program. Credit earned on completion of this program will be reported to RCEP. A certificate of completion will be issued to participants that have registered and attended the entire session. As such, it does not include content that may be deemed or construed to be an approval or endorsement by RCEP.

3 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

4 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

5 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

6 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]

7 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]

8 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]

9 6 SUGGESTED STATE HPMS PROCESSING CYCLE [HPMS Field Manual, 2016]

10 7 GENERALIZED HPMS PROCESS Inventory Data Collection Data Compilation Information Derivatives Information Presentation

11 8 HI HPMS FED. AID ROUTES Island NHS Miles

12 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)

13 HPMS DATA ITEMS 10 [FHWA HPMS Manual 2016]

14 HPMS DATA ITEMS 11 [FHWA HPMS Manual 2016]

15 SAMPLE PANEL DATA ITEMS 12 [FHWA HPMS Manual 2016]

16 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

17 LINEAR REFERENCE SYSTEM (LRS) 14 Segmented : Geometry Intersection based Route-level : Spatial Topology Combined: Link-Node Network

18 LINEAR REFERENCE SYSTEM (LRS) SPECIAL CASES 15 Roadway Gaps / Dog Legs Sta. 0+0 Sta Sta Sta. 5+0

19 LINEAR REFERENCE SYSTEM (LRS) SPECIAL CASES 16 Roadway Realignment / By-Pass Sta Sta Sta. 0+0 Sta Sta Sta Back = 1+75 Ahead Sta. 3+0

20 OAHU HPMS WITH NON-NHS & LOCAL ROUTES 17

21 18 INVENTORY DATA

22 INVENTORY DATA: PAVEMENT STRUCTURE HISTORY 19

23 INVENTORY DATA: PAVEMENT STRUCTURE HISTORY 20

24 HI DOT PAVEMENT THICKNESS 21

25 HI DOT PAVEMENT THICKNESS 22

26 MAINTENANCE INVENTORY DATA 23

27 DERIVED DATA MEDIAN WIDTH 24

28 INVENTORY DATA PROCESS: TRAVEL MONITORING 25

29 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

30 TRAVEL MONITORING: INVENTORY DATA SAMPLING HPMS TABLE OF POTENTIAL SAMPLES (TOPS) 27 Functional Class Urban Code Facility Type Through Lanes AADT

31 HPMS TRAVEL INVENTORY ITEMS 28

32 DERIVED HPMS TRAVEL ITEMS 29

33 ANNUAL TRAVEL MONITORING DERIVED DATA 30

34 RAW AND DERIVED DATA 31

35 RAW AND DERIVED DATA 32

36 TRAVEL MONITORING QA/QC CHECKS 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

37 AADT Obtained from Continuous Count Stations (24/7) 34

38 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.

39 MONTHLY FACTORS 36 ISLAND STATION Yr Oahu C7L 2015 Month ADT Factor January February March April May June July August September October November December AADT , , , , , , , ,000 ADT Factor

40 AADT Sample (2015 data): Station C7L Number of Counts 315 AADV = AADT = DHV (30 th Highest Hrly. Volume) = K = DHV/AADT * 100 = 5% Dmax = 53.6% 37

41 VEHICLE CLASSIFICATION 38 Source TMG 2016 pg. 3-37

42 AADT CLASS FACTORS 39 Single Station Class Factors for Station C7L Class MC PC LTrk Bus SU CU TotalVol MADTvc (Jan) AADTVC Class Factors

43 40 CLASSIFICATION DATA COMPUTATION (TO BE UPDATED WITH REAL DATA) Weekday Class Factors for Station C7L Class MC PC LTrk Bus SU CU Sunday Monday Tuesday Wednesday Thursday Friday Saturday

44 AADT Short Duration Counts Factored using Continuous Count Stations 41 COUNTDATE CYCLES PC LT_TRKS BUS SU CU 1/22/2015 (Thursday) /23/2015 (Friday) Factor Jan Thursday Factor Friday Factor Adjusted Data Average

45 HPMS LINK TRAVEL DATA ASSIGNMENT 42

46 TRAVEL MONITORING: THE IDEAL ENVIRONMENT 43

47 TRAVEL MONITORING: THE IDEAL ENVIRONMENT 24/7 continuous counters 44

48 45 TRAVEL MONITORING: AN EVEN BETTER ENVIRONMENT Autonomous Self-Reporting Vehicles

49 TRAVEL MONITORING: THE REAL ENVIRONMENT 24/7 continuous counters Short Term Counters? No Counters 46?????

50 SOURCES OF ERROR Count coverage (continuous vs. short-term counters) 47 Equipment malfunction Vehicle classification Seasonal variations Daily variations

51 48 HPMS LINK TRAVEL DATA ASSIGNMENT Station Link Assignments Spatial Proximity with TOPS attribute filters (functional class, urban code, etc.) Cluster analysis

52 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]

53 DEFINING ROADWAY SEGMENTS 24/7 continuous counters Short Term Counters? No Counters 50 Segment Boundaries?????

54 DEFINING ROADWAY SEGMENTS 51???????

55 CONSIDERATIONS FOR ASSIGNING DATA FROM MONITORED TO UNMONITORED SEGMENTS 52 Functional Classification Urban vs. Rural boundaries Proximity

56 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]

57 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]

58 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

59 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

60 A GEOGRAPHICALLY CLOSED SYSTEM 57 agram_systems.png

61 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

62 OAHU ROADWAYS AND TRAFFIC COUNTS 59

63 OAHU ROADWAYS AND TRAFFIC COUNTS 60

64 OAHU ROADWAYS AND TRAFFIC COUNTS 61 Continuous Counter

65 OAHU ROADWAYS AND TRAFFIC COUNTS Short Term Counts Continuous Counter

66 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

67 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

68 65 CONTACT INFORMATION Maaza Christos Mekuria, PhD, PE, PTOE Hawaii Department of Transportation Dan P. Seedah, PhD Asst. Research Scientist, Texas A&M Transportation Institute

69 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

70 Panelists Presentations After the webinar, you will receive a follow-up containing a link to the recording

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