Abstract

Abstract. The feature categories of an orienteering map are prepared to allow the map reader to estimate the travel time between any two points on the map with a good approximation. This requires not only an accurate map, but also a key that adapts to the speed of travel. Such map key is developed and maintained by the IOF (International Orienteering Federation), and technically all the orienteering maps are compiled by using it. Estimated time also plays an important role in planning the courses of orienteering races. The course setter estimates time based on a route he thinks is ideal, but the speed of travel is basically a non-linear function of terrain, road network and land cover. Because of this, the easiest (ideal) route between the two points and its time cost can be calculated using the least-cost path (LCP) GIS method, which can be prepared to take into account these three map feature categories. This method is based on the calculation of a cost surface, then the analysis of the ideal path from a given point to the destination. The automation can be adapted to any orienteering map due to the similarities of the map keys. This study shows that if the weight corresponding to the different feature categories is given properly, the ideal path between two points on orienteering maps can be calculated. The ideal path, however is still a subjective category, which may depend on the capabilities and preferences of the orienteer. In this study the routes calculated with the LCP method were compared with the suggestions of the ideal routes by orienteering runners of different ages. The results show that the routes given by sportsmen can be simulated with the LCP method and even the time cost of the calculated routes can be calculated. This study can lay the groundwork for a GIS tool helping the course setting process on standard orienteering maps.

Highlights

  • Route planning algorithms are frequently used in modern navigation tools, including smartphones and help to find the most practical way between a departure and a destination point

  • We found that the combination of the two cost rasters were the best when the HCRa had 60% and the area cost-raster (ACR) 40% weight

  • In the case no. 6 the least-cost path (LCP) differed from the summed path but matched with popular alternative path

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Summary

Introduction

Route planning algorithms are frequently used in modern navigation tools, including smartphones and help to find the most practical way between a departure and a destination point. The algorithms are based on the mathematical analysis of the existing road network taking into account different attributes of the road sections to precisely estimate the time-, and distance-costs of the route (Luxen & Vetter, 2011) Navigation tools such as Google Maps require a digital road map, which can be considered mathematically as a network, and the analysis concentrates on finding the shortest path that connects key nodes in the network (Delling et al, 2009). On those places where road network is sparse, yet some sort of traffic is existent, the route analysis requires a different data structure, which is the raster model (Herzog, 2013). The raster map used for the analysis contains numerical values representing the “travel cost” of a given surface type, and is called a “cost raster” (e.g. Herzog, 2013; Alberti, 2019)

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