Case Study: MassDOT ETC Conflation

The Massachusetts Department of Transportation (MassDOT) purchases INRIX data from the Eastern Transportation Coalition (ETC) Traffic Data Marketplace. MassDOT sought to integrate INRIX TMC data into planarized overlays or 'flat files' with existing Massachusetts Road Inventory events. This integration allows MassDOT to utilize collected INRIX data alongside MA Road Inventory attributes for planning, decision-making, analysis, quality control, and reporting.

To achieve this, the data needed to be conflated onto the MassDOT geometric network. Manual conflation or using existing GIS tools proved to be arduous and resource-intensive, with limited staff availability making it unlikely to meet deadlines. A previous conflation effort required four full-time employees (FTEs) a year to complete, with only 70% of the process automated.

MassDOT has previously attempted to perform conflation without the use of 1Spatial Technology and Conflation solutions. These efforts required four people and twelve months to achieve only 70% conflation. Manual conflation or using existing GIS tools proved arduous, and with limited staff resources, MassDOT could not perform conflation biannually with each INRIX dataset update. The automation of conflation efforts presents several distinct challenges:

• Different network coverage: The INRIX TMCs are not represented in all areas where MassDOT collects roadway data. Logic is needed to determine when a TMC is part of the LRS or when there are areas of the LRS without a matching TMC.

• Different linework: The scale and precision of the digitized road centerline network from two different sources are never aligned due to varying scales and precision at which they are collected. This requires fuzzy buffer distance tolerances to account for the offset networks.

• Different segmentation: TMCs are segmented at each intersection, while LRS data is one complete route or segmented by changes in data items only when the route information changes. This necessitates many-to-one matches between TMC road segments and the LRS Routes.

• Different representation of the same feature: Divided highways, ramps, and intersections may be represented differently to support the data's core mission.

• No common attributes: In many cases, the naming conventions differ between the two datasets, requiring fuzzy name matching or pre-processing to format the attributes consistently.

Through the Eastern Transportation Coalition (ETC) Traffic Data Marketplace (TDM), MassDOT contracted with 1Spatial to build an automated conflation process. This process leverages 1Spatial’s 1Integrate, 1Data Gateway, and LRS Conflation Solution to conflate INRIX TMC segments onto MassDOT’s LRS Network. The solution integrates both datasets and generates a series of LRS events on the MassDOT network, representing each TMC segment. A previous conflation effort required four full-time employees (FTEs) a year to complete, with only 70% of the process automated. 1Spatial successfully processed the entire state within two hours, automating the conflation of 91% of TMC features. Automation processing steps as follow:

• Validation: This initial step examines the submitted data in isolation, applying a set of validation rules to ensure the data conforms and is fit to proceed through the conflation process.

• Standardization: This step transforms inputs from multiple contributors with different formats, schemas, and projections into a single format, schema, and projection for processing. Standardizing first removes the need to configure matching and conflation logic for every implementation, creating a more repeatable process.

• Change Detection/Matching: This step compares two datasets to identify where they match or differ. Since datasets are often created by different organizations and at different scales, fuzzy spatial and attribution tolerances are required to ensure proper matching.