Horizontal highway segmentation optimisation using genetic algorithms

Abstract

This paper presents the use of genetic algorithms (GAs) for optimising different global positioning system-based procedures for horizontal roadway alignment extraction. Two algorithms are proposed – one uses design information to guide the GA, aiming to evaluate the segmentation procedures' precision, while the other uses curve-similarity measures. The linear matching model, the discrete Fréchet distance and the modified Hausdorff distance were tested for guiding the optimisation algorithm in cases when there is no design information available. This paper also presents an extension to a segmentation method available in the literature for increasing the optimisation performance. The proposed algorithms were evaluated on a synthetic data set with 2100 curves. In the experiments, both algorithms correctly identified all the curves, with the best segmentation precision achieved by the algorithm with design information, closely followed by the curve-similarity metrics. Compared with manual segmentation, all showed good results.

Editorial

“…Finally, in their paper 'Horizontal highway segmentation optimisation using genetic algorithms', Borges et al (2018) look at determining highway alignment information for the Brazilian road network to support the installation of automated weigh propose the use of GPS data collected by vehicles travelling on the road network, in this case to record the alignment. The paper describes the use of genetic algorithms to optimise the process of extracting the horizontal alignment from the collected data.…”