A Platform for Difficulty Assessment and Recommendation of Hiking Trails

Calbimonte, Jean-Paul; Simón, Martín; Calvaresi, Davide; Cotting, Alexandre

doi:10.1007/978-3-030-65785-7_9

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…In addition to the presence of such dangerous sections along a trail, information about their exact location is very useful to a hiker (see Calbimonte et al, 2021). The locations of DPs were overlaid with the sections of mountain trails equipped with safety devices obtained from a vector layer of the Utility and Public Infrastructure Cadastre -GJI (GURS, 2022).…”

Section: Dangerous Mountain Trail Sectionsmentioning

confidence: 99%

Reconsidering the basics of mountain trail categorisation: case study in Slovenia

Krevs¹,

Repe²,

Mazej³

2023

EJG

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Mountain trail categorisation remains an important piece of information for hikers when selecting a trail and preparing for a hike, despite new developments in trail difficulty assessment and trail recommendation systems and tools. National or regional trail categorisations should maintain a simple, mainly descriptive form to remain a useful guide for the majority of hikers. However, they are often still based on subjective expert assessments of trail difficulty. In this paper, we combine findings from our own empirical research and from the literature to support two arguments: available or obtainable geographical information and GIS -based methods can contribute to the objectivity of mountain trail categorisation, and improved categorisation contributes to increased hiking safety. An experimental GIS-based categorisation of mountain trails in two areas in Slovenia is conducted to support the above arguments. The comparison of the experimental categorisation with the existing official categorisation and the identification of potentially dangerous trail sections support perhaps the most important of the two arguments, namely the potential to improve hiking safety. However, it also stimulates further discussion on the establishment and implementation of an international standard-isation of trail categorisation.

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Section: Dangerous Mountain Trail Sectionsmentioning

confidence: 99%

Reconsidering the basics of mountain trail categorisation: case study in Slovenia

Krevs¹,

Repe²,

Mazej³

2023

EJG

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“…In all these sports, the user has a common problem, namely to match the trail's or route difficulty level with the hiker's or climber's ability. One notable study was conducted by Calbimonte et al in [6][7][8], where they outlined the problem of matching hiker to hiking trails according to the hiker's physical level and trail's difficulty characteristics. The authors have developed a semantic model to represent the hikes using Semantic Web ontologies and have built user profiles via a questionnaire, which requires users to fill in the information manually.…”

Section: Related Workmentioning

confidence: 99%

Content-Based Recommendations for Crags and Climbing Routes

Ivanova

Andrić

Ricci

2022

Information and Communication Technologies in Tourism 2022

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Climbing is a popular sport for active tourists and recreational sportsmen. Alpine climbing areas, such as the Alps, can attract tourists from all over the world. Various websites, mobile applications, and books are used by climbers to obtain information on important aspects of the available climbing routes, including their properties, location, and especially their difficulty. Considering this large amount of information and options, it is in reality difficult for climbers to properly select which routes to climb. Hence, we propose recommendation technologies aimed at supporting climbers in this decision task. The developed system prototype constructs a climber’s profile with preferences derived from climber’s logbook data collected by a mobile app. Then, the system can recommend suitable crags and climbing routes within the selected crags. The designed interface and the basic computational models for such a system prototype are presented. The proposed technology aims at complementing existing electronic climbing guidebooks and providing decision support to climbers.

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“…This increased interest from the population leads to a rise in the development of new climbing routes and crags and creates an information overload problem for people who plan their climbing trips. Recommender systems (RSs) are introduced as a tool to deal with such problems (Ricci et al, 2022 ) and have been intensively employed in other outdoor sports, e.g., running (Feely et al, 2022 ; Vardhan et al, 2022 ) and hiking (Posti et al, 2014 ; Calbimonte et al, 2020 , 2021 ). In the OSC domain, a climbing recommender system (RS) provides crag recommendations based on the number of suitable climbing routes located within the crag (Ivanova et al, 2022b ).…”

Section: Introductionmentioning

confidence: 99%

Climbing crags recommender system in Arco, Italy: a comparative study

Ivanova,

Wald

2023

Front. Big Data

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Outdoor sport climbing is popular in Northern Italy due to its vast amount of rock climbing places (such as crags). New climbing crags appear yearly, creating an information overload problem for tourists who plan their sport climbing vacation. Recommender systems partly addressed this issue by suggesting climbing crags according to the most visited places or the number of suitable climbing routes. Unfortunately, these methods do not consider contextual information. However, in sport climbing, as in other outdoor activities, the possibility of visiting certain places depends on several contextual factors, for instance, a suitable season (winter/summer), parking space availability if traveling with a car, or the possibility of climbing with children if traveling with children. To address this limitation, we collected and analyzed the crag visits in Arco (Italy) from an online guidebook. We found that climbing contextual information, similar to users' content preferences, can be modeled by a correlation between recorded visits and crags features. Based on that, we developed and evaluated a novel context-aware climbing crags recommender system Visit & Climb, which consists of three stages as follows: (1) contextual information and content tastes are learned automatically from the users' logs by computing correlation between users' visits and crags' features; (2) those learned tastes are further made adjustable in a preference elicitation web interface; (3) the user receives recommendations on the map according to the number of visits made by a climber with similar learned tastes. To measure the quality of this system, we performed an offline evaluation (where we calculated Mean Average Precision, Recall, and Normalized Discounted Cumulative Gain for top-N), a formative study, and an online evaluation (in a within-subject design with experienced outdoor climbers N = 40, who tried three similar systems including Visit & Climb). Offline tests showed that the proposed system suggests crags to climbers accurately as the other classical models for top-N recommendations. Meanwhile, online tests indicated that the system provides a significantly higher level of information sufficiency than other systems in this domain. The overall results demonstrated that the developed system provides recommendations according to the users' requirements, and incorporating contextual information and crag characteristics into the climbing recommender system leads to increased information sufficiency caused by transparency, which improves satisfaction and use intention.

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A Platform for Difficulty Assessment and Recommendation of Hiking Trails

Cited by 7 publications

References 17 publications

Reconsidering the basics of mountain trail categorisation: case study in Slovenia

Reconsidering the basics of mountain trail categorisation: case study in Slovenia

Content-Based Recommendations for Crags and Climbing Routes

Climbing crags recommender system in Arco, Italy: a comparative study

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