comparative study of various strategies to concatenate road segments into strokes

What is it about?

The study of road networks has been a topic of interest for some time. A road network in a database is often represented by intersections and segments. However, in many cases (e.g., traffic flow analysis and map generalization), one needs to consider individual roads as a whole, instead of individual segments. Thus, it is sometimes very desirable to concatenate road segments into long lines – ‘strokes’ as they are called in the literature. For stroke building, a number of strategies are available and the effectiveness of using these strategies needs to be evaluated. This article presents a comparative analysis of 17 such strategies, including 3 of the geometric approach, 1 of the thematic approach, and 13 of the hybrid approach for road network generalization purposes. Three sets of real-life data with different patterns are used to test these strategies. Corresponding road maps at smaller scales are used as benchmarks and a new measure called the accuracy rate is proposed to indicate the correctness of the concatenated strokes. The results show that if only the geometric approach is considered, the every-best-fit strategy performs best; if thematic attributes are also added, road class can be more effective than road name. Also significance tests (the chi-square test and the Marascuilo procedure) are carried out to give all pairwise comparisons of these strategies. The results indicate that 45 of the 136 pairs of strategies have statistically significant differences; the purely geometry-based every-best-fit performs significantly better than the purely geometry-based self-fit; and the inclusion of thematic attributes, especially road class, sometimes improves the accuracy rate but the improvement is not significant.

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Why is it important?

The results indicate that 45 of the 136 pairs of strategies have statistically significant differences; the purely geometry-based every-best-fit performs significantly better than the purely geometry-based self-fit; and the inclusion of thematic attributes, especially road class, sometimes improves the accuracy rate but the improvement is not significant.

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