Linearly Reference TMCs in HPMS - Transportation.org...TMC group and the overall direction of this...
Transcript of Linearly Reference TMCs in HPMS - Transportation.org...TMC group and the overall direction of this...
Linearly Reference TMCs in HPMS
Vincent Fang, MacroSys LLC
March 21, 2018
By Presenter:
In Support of FHWA Office of Highway Policy Information
I. ObjectivesII. ConflationIII. Linear ReferencingIV. Link to HPMSV. Q/A
Layout of Today’s Presentation
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What We Want to Achieve
A functional geospatial network retaining both the Highway Performance Monitoring System (HPMS) data attributes and the National Performance Management Research Data Set (NPMRDS) network attributes, particularly the Traffic Message Channel (TMC) codes in sync with each other in terms of both geospatial locations and time periods.
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Why Integrate?
1. NPMRDS data provided per TMC
2. Integrate the HPMS and NPMRDS datasets to increase the analytical usefulness of the speed data, for example
– Travel speed by highway function class
– Travel speed in urban and rural areas
3. Visualize travel speed on the HPMS network
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What We Have in Hand - 1
ARNOLD
• Key data field: Route_ID
• Each road segment consists of vertices geo-referenced with both x-y coordinates and milepoints
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Geo-referencing in ARNOLD
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What We Have in Hand -2
NPMRDS TMC
• Key data fields: TMC, RoadNumber, RoadName, Direction
• Each road segment consists of vertices geo-referenced with only x-y coordinates
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Geo-referencing in NPMRDS TM
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Two Sets of Procedures
1. Conflate ARNOLD with NPMRDS TMC to associate each road segment in ARNOLD with TMC segments within the corresponding road segment in NPMRDS TMC;
2. Linearly reference each TMC road segment using the default linear referencing system in ARNOLD.
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Conflation and Challenges
1. Combine geographic information from overlapping sources so as to retain accurate data, minimize redundancy, and reconcile data conflicts.
2. Data items, i.e. point, line, and area features and their attributes, in different layers do not overlay with an exact match.
– Road segment in one but not in the other
– Roadway represented with multi-lanes in one but one centerline in the other
– A single line segment between two nodes in one split into several in the other
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One-to-One Match: A Rare Case
The highlighted blue line is ARNOLD 100872011 in state New York.
All the TMC segments with RoadNumber = “I-990” and Direction=“Northbound” are highlighted in brown and layered beneath the ARNOLD.
In this case, the ARNOLD road is almost perfectly matched with all the TMC segments in northbound I-990.
ARNOLD 100872011Matched TMC group NB I-990
Other TMC Groups in RoadNumberOther TMC Groups in RoadName
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ARNOLD 100644011
Other TMC Groups in RoadNumberOther TMC Groups in RoadName
Start point
A
B
C
D
E
ARNOLD 100644011 Part A-B is matched with EB NY-104Part B-C is matched with SB NY-93Part C-D is matched with EB NY-31Part D-E is matched with EB NY-93
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One-to-Many Match: A Common Case
Match Making
1. A systematic way of telling the system which TMC roads correspond to each ARNOLD road segment. There may be more than one TMC road for each ARNOLD segment or the other way around.
2. Six matching types to accommodate the source data: road with one or more attributes such as route_ID, route_name, direction, and direction-signs.
3. A series of automated and manual procedures to decide matching types for each ARNOLD road segment.
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Six Matching Types-1
1. "rdnumbN" – the ARNOLD road or parts of the ARNOLD road matched with the NPMRDS roads based on RoadNumber in TMCN direction (TMC with “-” or “N”).
2. "rdnumbP"– the ARNOLD road or parts of the ARNOLD road matched with the NPMRDS roads based on RoadNumber in TMCP direction (TMC with “+” or “P”).
3. "rdnamN"– the ARNOLD road or parts of the ARNOLD road matched with the NPMRDS roads based on RoadName in TMCN direction (TMC with “-” or “N”).
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Six Matching Types-2
4. "rdnamP"– the ARNOLD road or parts of the ARNOLD road matched with the NPMRDS roads based on RoadName in TMCP direction (TMC with “+” or “P”).
5. "Dir”– the ARNOLD road or parts of the ARNOLD road matched with the NPMRDS roads based on the TMC group and the overall direction of this group, regardless of the sign of TMC codes, N or P.
6. "TMCs" – the ARNOLD road or parts of the ARNOLD road matched with the NPMRDS roads based on individual TMC codes.
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Two Initial Steps for Matching
1. Re-identify TMC roads with a new identifying variable TMCiden, which takes values as follows.
- Non-null RoadNumber: TMCiden=U_RoanNumber_Direction_TMC Sign, such as
U_US-158_Westbound_P
- Null RoadNumber but non-null RoadName: TMCiden=RoadName_Direction_TMCSign, such as I-85 Bus_Southbound_N.
- Null RoadNumber and null RoadName (ramp), TMCiden=’TMC’_TMC Sign, such as TMC_P.
2. Create mid-point matching, associating each ARNOLD road segment with the nearest TMCiden.
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ALL ARNOLD_NHS roadsSelected ARNOLD 44_0082P1Matched TMC groups EB US-82Matched TMC group EB US-45
Other TMC Groups in RoadNumberOther TMC Groups in RoadName
1
23
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• An ARNOLD road segment identified with a Route_ID is matched up with an NPMRDS TMC road segment identified with one or more TMC identifiers such as rdnumbN, rdnumbP, rdnamN, rdnamP, TMCs, or Dir.• Some ARNOLD road segments are not matched with any TMC road segments such as the second record in the table 01_0070P1.
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1. Select matched road segments within a match-type and copy selected line features from target layer as a new road layer.
2. Buffer selected road segments to create polygons to include matching road segments from the source layer.
3. Union to collect all features and their attributes from buffered layers within the polygons.
4. Intersect to transfer all features within the polygons to the selected roads within the polygon for the new road layer.
Four Steps for Final Conflation
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The selected ARNOLD road has two types of matching and is processed with separate scripts:- “44_0082P1, US-45” conflated with P_NUMB.py conflated result: p_numb_001- “44_0082P1, US-82, Eastbound” conflated with DIR.py conflated result: dir_001
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Conflated result p_numb_001:
• The first selected row contains a collection of road segments that do not have conflated TMC features, as highlighted in light blue.• All other segments are conflated with TMC features, as highlighted in dark blue. • This is because only the dark blue part of ARNOLD 44_0082P1 is matched with eastbound US-45.*dots are the gaps that the conflation process is unable to grab the corresponding TMCs for.
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• The first selected row contains a collection of road segments that do not have conflated TMC features, as highlighted light blue.• All other segments are conflated with TMC features, as highlighted in dark blue.•This is because only the dark blue part of ARNOLD 44_0082P1 is matched with eastbound US-82. *dots are the gaps that the conflation process is unable to grab the corresponding TMCs for.
Conflated result dir_001:
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Review and fine-tune two results through the post-processing in their designated groups leading to the complete conflated result as follows:
Because the whole ARNOLD 44_0082P1 has matched TMC features, the complete conflation result in a smooth line without gaps.
The complete conflation result is used as an “identity layer” for the last step to generate the final result in the phase post-conflation.
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The final result embedded in the original dataset for LRS
The attribute table contains all the original attributes and the TMC codes transferred in the “Identity” step.
The original One record road is now divided into 46 parts to reflect the matched TMC segments identified with TMC codes.
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1. Assign unique identifier to each TMC line segment in the conflated network.
2. Convert each TMC line segment into points using the original ARNOLD vertices within the segment.
3. Select among the vertices identified in step 1 the two extreme ones with minimum milepoint and maximum milepoint.
4. Transfer minimum and maximum milepointsrespectively to beginning and ending points
5. Compute the difference of the minimum and maximum milepoints as the length of the TMC segment.
Five Steps for Linear Referencing
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The single ARNOLD road, now, has 23 conflated segments; each segment has its own matched TMC segment with a unique TMC code.
Segment started with Red Arrow
TMCs in One Road Segment
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Start Point
Start Point
Two Sample TMCs
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Two Samples with Vertices
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1. The X-Y coordinates and the milepoints in the dataset developed so far are those of points but not segments such as TMC segments.
2. The X-Y coordinates and the milepoints for a road segment are those for the beginning and ending points of the segment.
3. The task is to identify the X-Y coordinates and the milepoints for the beginning and ending points for each TMC segment and associate them with the TMC in the dataset.
Important to Note
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Summary statistics grouped by unique ID
Minimum and Maximum Points
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Associating Min/Max Values with TMC
Combine the conflated attribute table with “min_m” and “max_m” fields of summary statistics table by join.
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Final Conflated TMCs with LR
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Final Output File
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1. HPMS Geo-referencing: “Both the geospatial and attribute data contain three referencing elements that are used to perform the linkage for linear features: (1) A unique Route ID, (2) a beginning milepoint, and (3) an ending milepoint.” (HPMS Manual p.3-3)
2. HPMS Section Table (HPMS Manual P.3-11)
Link to HPMS
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Wenjing Pu,Transportation [email protected]
Vincent Fang,Transportation AnalystMacroSys, [email protected]
Questions and Comments
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