International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 01 | Jan 2026
p-ISSN: 2395-0072
www.irjet.net
TrekMate: Integrating Real-time Community Networking and Dynamic Trek Planning for Adventure Enthusiasts Prof.Payal Nikam1, Anuj Newase2, Rushikesh Nikam3, Hariom Raut4 ,Divya Raut5 2,3,4,5 Student, Dept. of Computer Engineering, Dhole Patil college of Engineering, Pune, Maharashtra, India 1Professor, Dept. of Computer Engineering, Dhole Patil college of Engineering, Pune, Maharashtra, India
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conducted through user surveys, online research, and interviews with trekking professionals to gather insights.
Abstract - Trekking and adventure communities are
challenged by fragmented platforms, making it difficult to find suitable partners, plan secure routes, and communicate effectively in real time. Trek-mate addresses these by offering a community-driven, web-based platform enabling trekkers to discover treks matching their fitness and experience, connect with peers, plan collaboratively, and use real-time group chat. Combining scientific insights on hiking tourism, social interaction, and real-time communication (Socket.IO, MERN stack), Trekmate integrates social and technological advances to position itself as a unique, smart solution for trek management.
2.System Design and Architecture Planning: Based on the requirements, a modular system architecture was designed to ensure smooth data flow and component interoperability. The architecture integrates front-end (user interface), back-end (server-side logic), and AI components (recommendation and analysis). 3. Technology Selection: The project leverages React Native for front-end mobile development, Node.js and Express.js for server-side processing, Mongo DB for data management, and AI frameworks such as Tensor Flow or Scikit-learn for predictive modeling and analytics.
Key Words: Trek Management System, Real time group chat, Community based tracking, Route Planning tools
1. INTRODUCTION
4. Algorithm Development:
Recent years have witnessed exponential growth in the popularity of trekking and adventure activities, driven by their contributions to health, well-being, and sustainable tourism. However, digital solutions often lack specificity for trek planning, community coordination, and real-time group management. Fragmented information, absence of smart recommendations, and limited group communication has hindered the trekking community’s ability to connect and plan efficiently. Trek-mate aims to solve these challenges by providing a centralized platform with personalized trek recommendations, intelligent route planning, and seamless group communication using advanced web technologies.
AI algorithms were designed to analyze user fitness metrics, environmental data, and route information. Machine learning techniques such as decision trees, knearest neighbors (KNN), or regression models are employed to generate intelligent recommendations.
2. METHODOLOGY
Each module underwent multiple rounds of testing — including unit testing, integration testing, and user acceptance testing — to ensure reliability and accuracy in real-world conditions.
5. Implementation: The actual coding and integration of various modules were carried out using agile principles, allowing for iterative development and continuous feedback incorporation. 6. Testing and Validation:
The problem-solving methodology adopted for Trek Mate follows a structured and iterative approach, ensuring that the final product is efficient, reliable, and user centric. The system development life cycle involves the following major phases:
7. Deployment and Maintenance: Once validated, the application was deployed on cloud servers for scalable performance and maintained through periodic updates and performance monitoring.
1. Problem Identification and Requirement Analysis: The first step involved identifying the key challenges faced by trekkers such as unpredictable weather, navigation errors, fitness misjudgment, and emergency inaccessibility. A detailed requirement analysis was
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Impact Factor value: 8.315
This systematic methodology ensured that the Trek Mate application evolved through iterative refinements, aligning
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