Automated Route-based Conflation Between Linear Referencing System Maps And OpenStreetMap Using Open-source Tools

2025-07-18

AI Summary

Existing multi-source road network integration—particularly between linear referencing systems and OpenStreetMap—relies heavily on proprietary algorithms, incurs high licensing costs, and suffers from poor reproducibility. Method: This paper proposes an open-source, fully automated route-level matching framework based on Hidden Markov Models (HMM) and Viterbi decoding. It integrates Valhalla for map matching, employs adaptive interpolation to bridge路段 gaps exceeding 12 meters, and adopts a per-route processing strategy. Contribution/Results: The resulting pipeline is entirely open-source, requires no commercial licenses, and ensures full reproducibility. Experiments demonstrate a fusion matching rate exceeding 98%, significantly outperforming state-of-the-art automated approaches. The method achieves simultaneous improvements in both accuracy and computational efficiency, offering a scalable, low-cost technical pathway for integrating heterogeneous transportation geospatial data.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Web Mining and Content Analysis: Web data integration and cleaning

Abstract

Transportation researchers and 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.

Problem

• Automated conflation of LRS and OpenStreetMap basemaps
• Open-source process replacing proprietary manual methods
• High-accuracy matching using HMM and Viterbi algorithms

Innovation

• Automated conflation using HMM and Viterbi search
• Open-source pipeline without proprietary licenses
• Achieves over 98% successful match rate