FME for HPMS Reporting Challenges

Whether you are glad that the HPMS season will be ending soon, or stressed that the deadline is looming, or both, you owe it to yourself to check out FME-based tools for HPMS.

Why not?  At its core, FME provides a comprehensive set of data ETL tools that extend beyond the spatial domain. Moreover, it provides a platform to design, test, develop and document your workflow which is highly adaptable to changes in requirements as well as data sources.

At the GIS-T 2016 Symposium in Raleigh NC, Dave Campanas of Safe Software and I jointly presented FME & ARNOLD: Superman to the Rescue! After the session, Kyle Konterwitz, GIS Manager of Kansas DOT, approached me for generating a report using FME, something they had attempted for some time now – a project feature report segmented by Functional Classification and NHS designation, as well as  several administrative and political boundaries.  

At first glance, this commonly-requested report is conceptually simple.  A deeper look into the requirements and data sets resulted in the following multi-step process:

  1. Merge HPMS segments based on functional classification and NHS code  
  2. Join (overlay) project events with the events resulting from Step 1
  3. LRS geocode the events resulting from Step 2 to turn it into a feature dataset
  4. Overlay line features from Step 3 with boundary features to get the attributes from the boundaries
  5. LRS reverse geocode the result from Step 4 so each feature will have the correct From Measure and To Measure values in its attributes
  6. Optionally, remove sliver project segments as a result of discrepancies between the data layers

Out of the box, FME does not provide a direct solution.   With the help of LinearBench® custom transformers such as LRS_EventMerger, LRS_EventJoiner, LRS_Geocoder, and LRS_RevGeoCoder, the process was made clean, friendly and adaptable to changes.
The second challenge came from Dave Blackstone, GIS Manager of Ohio DOT, who would like to summarize a subject event data set over a reference data set for key statistics, including length-predominate stats, among other things.   This capability is already implemented in LinearBench® Analyze; still we decided to also offer it as an FME custom transformer, and LRS_EventSummarizer was born.  The following workspace shows the simple workflow for this challenge:  

LRS_EventSummarizer Sample Workflow

Partial result of the report is shown in Table 1.

Table 1  UDOT’s AADT Summary Statistics over Speed Limit Segments

IDRoute_IDSpeed_
Limit
F_MeasT_MeasAADT_MinAADT_MaxAADT_
Count
AADT_
Mean
AADT_length_
Predominant
AADT_length_
Weighted
3940252400.3741.31118701234362210691870118918
3939252451.3112.29222837234362231372343623418
4104254450.1420.6717897891789789788
4103254350.6710.6727897891789789789
4102254450.6721.3337897891789789789
4319255300.0610.6894656192542465509
4318255650.68965.6354078323619832740
43172554065.63566.77440713322870407860
43162555566.77469.094133213321133213321332
4327256550.1956.8191131302122113114
4326256306.8197.1441131131113113113
4325256157.1447.3081131131113113113
432425657.3089.68921132103113112
4323256559.689.78492921929291
4322256159.78410.2292921929292
43212563010.2214.96792921929292
43202565514.96732.4919210229710297
21257400.220.47512978129781129781297812978
20257350.4750.54412978129781129781297812977

While the 2015 HPMS season will end shortly, knowing FME is there to help you with future HPMS challenges may just make the off season more enjoyable!

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