Procedure for the Identification of Existing Roads Alignment from Georeferenced Points Database

Cantisani, Giuseppe; Del Serrone, Giulia · 2020 · OpenAlex-citations

DOI: 10.3390/infrastructures6010002

archive: archived pipeline: cataloged verified

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Summary

This paper addresses the challenge of identifying the geometric alignment of existing road networks, specifically Italian two-lane rural roads, to facilitate safety assessments and maintenance planning. Many existing roads were designed decades ago and have undergone modifications, making their original geometric design difficult to verify. While the national public company ANAS provides a database of georeferenced points representing approximately 90% of this network, the data consists of raw coordinates that do not explicitly describe the geometric elements (such as straight lines, circular arcs, and clothoids) that constitute the roadway. The authors aim to develop an automated, economical, and fast procedure to extract these geometric characteristics from the point cloud data, enabling the *ex post* application of regulatory design verification models to assess road consistency, coherence, and homogeneity. The study focuses on horizontal geometry, specifically the continuous trend of curatures. The authors review existing methodologies for road geometry relief, categorizing them into static methods (e.g., GIS map extraction) and dynamic methods (e.g., Mobile Mapping Systems, GNSS with inertial sensors). They note that while these techniques efficiently collect coordinate data, they often fail to define a continuous reference axis mathematically formalized as a succession of basic geometric elements. The proposed procedure utilizes a data fitting process based on least squares regression, rather than interpolation or B-spline approximation. This approach approximates the set of georeferenced points with an optimal curve that minimizes the sum of squared distances between the points and the curve, effectively handling measurement errors and data inaccuracies inherent in surveying instrumentation. The method was implemented in a programming platform to automatically identify the characteristics of the reference axes. The research establishes that the least squares fitting method is particularly suitable for estimating parameters such as radius and arc length with high accuracy, minimizing deviations between computed curves and original points. By converting raw georeferenced points into a structured geometric model, the procedure allows for the identification of specific road sections where design conditions are not significantly met. The authors emphasize that this automated extraction overcomes the lack of shared methodologies for defining reference axes from existing survey data. The resulting geometric model enables engineers to apply standard design rules to evaluate whether the road layout induces safe and predictable driver behavior, aligning with the concept of "self-explaining roads." The significance of this work lies in providing a reliable tool for road safety analysis and infrastructure maintenance. By automating the identification of horizontal geometry, the procedure facilitates the detection of inconsistencies that may lead to risky driving behavior or accidents. This allows for targeted further investigations and the planning of upgrading activities for road sections that fail to meet current safety standards. The method offers a cost-effective solution for processing large datasets from national road networks, bridging the gap between raw survey data and actionable geometric insights required for regulatory compliance and safety enhancement.

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• road geometry