High Altitude Trek Prediction and Gear Renting Platform

Trekking is a popular outdoor activity that offers both physical challenge and the opportunity to connect with nature. However, selecting the right trekking route that matches an individual's experience level, physical ability, and personal preferences can be a daunting task. This paper presents a comprehensive trekking recommendation system designed to assist outdoor enthusiasts in discovering suitable trekking routes and gear renting system. The system leverages a combination of user inputs, such as desired difficulty level, location, duration, along with data on existing trekking trails. Advanced algorithms and machine learning techniques are employed to analyze these inputs and provide personalized recommendations. The system also considers factors like health tracking which makes wonderful trekking experience. The aim of this project is to create an intelligent and user-friendly platform that not only encourages safe and enjoyable trekking experiences but also promotes eco-friendly tourism by recommending lesser-known trails, thereby reducing the impact on overcrowded popular routes. It also promotes a gear renting platform too.This paper details the development process, the challenges faced, and the potential impact of the system on the trekking community.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-23
DOI
https://doi.org/10.5281/zenodo.15267705
Primary Topic
Recreation, Leisure, Wilderness Management
Type
article
Field-Weighted Citation Impact
0.00
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article

High Altitude Trek Prediction and Gear Renting Platform

Zenodo (CERN European Organization for Nuclear Research)
Recreation, Leisure, Wilderness Management
article

High Altitude Trek Prediction and Gear Renting Platform

article en

Abstract

Trekking is a popular outdoor activity that offers both physical challenge and the opportunity to connect with nature. However, selecting the right trekking route that matches an individual's experience level, physical ability, and personal preferences can be a daunting task. This paper presents a comprehensive trekking recommendation system designed to assist outdoor enthusiasts in discovering suitable trekking routes and gear renting system. The system leverages a combination of user inputs, such as desired difficulty level, location, duration, along with data on existing trekking trails. Advanced algorithms and machine learning techniques are employed to analyze these inputs and provide personalized recommendations. The system also considers factors like health tracking which makes wonderful trekking experience. The aim of this project is to create an intelligent and user-friendly platform that not only encourages safe and enjoyable trekking experiences but also promotes eco-friendly tourism by recommending lesser-known trails, thereby reducing the impact on overcrowded popular routes. It also promotes a gear renting platform too.This paper details the development process, the challenges faced, and the potential impact of the system on the trekking community.

Zenodo (CERN European Organization for Nuclear Research)
Decent work and economic growth
Openalex Percentile: Top 47%
Recreation, Leisure, Wilderness Management
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