ITSC 2025 Paper Abstract

Paper VP-VP.83

Ali, Gibran (Virginia Tech Transportation Institute), Feierabend, Neal (Virginia Tech Transportation Institute), Doshi, Prarthana (Virginia Tech), Chung, Whoibin (Virginia Department of Transportation), Babiceanu, Simona (Virginia Department of Transportation), Fontaine, Michael (Virginia Transportation Research Council)

Automated Route-Based Conflation between Linear Referencing System Maps and OpenStreetMap Using Open-Source Tools

Scheduled for presentation during the Video Session "On-Demand Video Presentations" (VP-VP), Saturday, November 22, 2025, 08:00−18:00, On-Demand Platform

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on April 2, 2026

Keywords Real-world ITS Pilot Projects and Field Tests, Data Analytics and Real-time Decision Making for Autonomous Traffic Management, Large-scale Deployment of Intelligent Traffic Management Systems

Abstract

Transportation planners utilize a wide range of roadway metrics that are usually associated with different basemaps. Conflation is an important process for transferring these metrics onto a single basemap. However, conflation is often an expensive and time-consuming process based on proprietary algorithms that require manual verification.

In this paper, an automated open-source process is used to conflate two basemaps: the linear reference system (LRS) basemap produced by the Virginia Department of Transportation and the OpenStreetMap (OSM) basemap for Virginia. This process loads one LRS route at a time, determines the correct direction of travel, interpolates to fill gaps larger than 12 meters, and then uses Valhalla’s map-matching algorithm to find the corresponding points along OSM’s segments. Valhalla’s map-matching process uses a Hidden Markov Model (HMM) and Viterbi search-based approach to find the most likely OSM segments matching the LRS route.

This work has three key contributions. First, it conflates the Virginia roadway network LRS map with OSM using an automated conflation method based on HMM and Viterbi search. Second, it demonstrates a novel open-source processing pipeline that could be replicated without the need for proprietary licenses. Finally, the overall conflation process yields over 98% successful matches, which is an improvement over most automated processes currently available for this type of conflation.