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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

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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!

Bo Guo, PhD, PE

At Gistic Research, Bo leads a team in LinearBench product design and development. He is a passionate LRS researcher and practitioner who believes that LRS challenges can be solved through technology design and integration.

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