Safarion: An AI-Driven Safety and Travel Companion for Solo Explorers

Safarion: An AI-Driven Safety and Travel Companion for Solo Explorers

Sruthy Ravi

Computer Science and Engineering TocH Institute Of Science And Technology

Kochi, India

Sreeya Menon P T Computer Science and Engineering, TocH Institute Of Science And Technology Kochi, India

Meya Maria Tony

Computer Science and Engineering, TocH Institute Of Science And Technology Kochi, India

Nikhil Raju

Computer Science and Engineering, TocH Institute Of Science And Technology, Kochi, India

Asst.Prof. Jisha Mariam Jacob

Computer Science and Engineering, TocH Institute Of Science And Technology Kochi, India

Abstract – Solo travel offers freedom and personal growth, but it also introduces significant safety risks, particularly for women, students, elderly individuals, and first-time travelers. Existing travel applications largely focus on convenience and planning, while safety-centric and emergency-ready solutions remain fragmented and unreliable, especially in low-connectivity regions. This research presents Safarion, an intelligent AI- driven safety and travel companion designed to provide real-time emergency response, offline safety support, secure route guidance, and cultural assistance for solo travelers. The system integrates voice-based SOS detection, live GPS tracking, geofencing, offline emergency alerts, SOS QR codes, and community-driven safety intelligence. Built using a .NET MAUI frontend and ASP.NET Core Web API backend, Safarion demonstrates the feasibility of combining AI, cloud services, and mobile technologies into a unified platform that enhances both safety and travel experience. The proposed solution aims to reduce fear, improve preparedness, and encourage confident solo exploration.

1. INTRODUCTION

   The global rise in solo travel, fueled by a growing desire for independent exploration and personal discovery, has reshaped the landscape of modern tourism. While digital platforms have revolutionized travel planning and booking, a critical gap remains in the provision of integrated, reliable safety solutions. This deficiency disproportionately affects vulnerable demographics including women, students, and the elderly, for concerns about unsafe routes, delayed emergency response, and cultural

   unfamiliarity can be significant deterrents. As travelers venture

   into new environments, the need for an intelligent and adaptive safety companion has never been more pressing.

   1. Background Information

      The current landscape of solo travel is characterized by an increasing reliance on technology for navigation and communication. However, the safety tools available are often fragmented and reactive. Conventional safety measures, such as manual panic buttons on mobile apps, GPS tracking services, and standard messaging applications, suffer from critical failings. Their effectiveness is frequently compromised by a dependency on stable internet connectivity, a reliance on the users ability to act calmly and manually during a high-stress emergency, and the potential for physical incapacitation. In remote or unfamiliar locations where risks are often heightened, these limitations render many existing solutions inadequate, leaving travelers exposed. Advancements in Artificial Intelligence (AI), mobile computing, and IoT have created an opportunity to develop proactive, context-aware systems that can anticipate risks and function reliably even in adverse conditions.

      By integrating real-time data analysis, automated alerts, and offline-capable features, next-generation safety platforms can significantly reduce response time and human error. Such intelligent systems can continuously monitor risk indicators and provide timely interventions, thereby improving traveller confidence and overall journey security.

   2. Research Problem or Question

      The central research problem is the absence of a comprehensive, intelligent platform that prioritizes traveler safety, functions reliably in offline scenarios, and simultaneously enhances the cultural travel experience. Existing applications tend to focus on convenience and planning, leaving a critical void in integrated, proactive emergency response. This study addresses this gap by designing a unified system that leverages commonly available smartphone technologies to create a robust safety net for solo explorers. The primary research question is: “How can an AI-driven mobile platform be designed to provide reliable real-time and offline safety assistance, emergency response, and cultural guidance for solo travelers using commonly available smartphone technologies?”

   3. Significance of the Research

      The Safarion platform introduced in this research represents a significant contribution to travel technology by establishing a “safety-first paradigm.” It shifts the focus from reactive convenience to proactive protection, demonstrating how AI, mobile computing, and community intelligence can be woven into a cohesive solution. The platform’s primary contributions include:

      1. Providing AI-based voice-triggered emergency detection that does not require manual input from a user in distress.
      2. Supporting offline emergency alert storage and delayed transmission, ensuring reliability in remote or low- connectivity areas.
      3. Integrating safety intelligence with cultural and travel assistance features for a holistic and enriching travel experience.
      4. Encouraging privacy-preserving, community-driven safety data sharing to build a dynamic and self-improving safety ecosystem. This research provides a framework for developing more inclusive and secure smart tourism solutions. It is necessary to review the existing body of literature to properly contextualize the development of this novel platform.
2. LITERATURE REVIEW

   This section offers a thorough look at existing research in areas like smart travel systems, route planning tool and recommendation engines. The goal is to uncover the main ideas, methods, and technologies that have shaped the development of the Safarion system. It also takes a close look at what works well and what doesnt in current solutions regarding safety, flexibility, quick responses and personalization. By going through previous studies, this part shows the technical and functional issues that Safarion is designed to fix. This comparison helps explain why a single, smart platform is needed and highlights how Safarion stands out as a new, safety-focused travel tool. Overall, this review establishes a clear research context for the proposed system by linking existing studies with real-world user

   requirements. The insights gained from this analysis directly inform the design choices and feature selection of Safarion, ensuring that the system is both technically sound and practically relevant in addressing modern travel safety challenges.

   1. Overview of Relevant Literature

      Artificial Intelligence-Enabled Travel Planner [1]

      This system leverages Google Generative AI and Firebase to create highly personalized travel itineraries. Its methodology centers on using Natural Language Processing (NLP) for conversational user interaction and analyzing diverse data sourcesincluding user preferences, travel trends, and local eventsto generate customized plans. A key advantage is its dynamic adaptation capability, allowin it to provide real-time alternatives in response to disruptions like flight cancellations. The system’s high accuracy in recommendations (98-100%) underscores the power of AI in handling complex travel logistics, though it primarily focuses on planning rather than real-time safety.

      Enhanced Route Optimization: Incorporating Road Safety Factors for Optimal Path Selection [2]

      This work introduces a new route planning system that improves on the classic Dijkstra Algorithm by including real-time accident rates and travel time information. The approach uses a multi-factor optimization method, letting users choose whats most important for their trip, like safety, time, or distance. The main benefits of this system is that it clearly prioritizes road safety, which is often ignored by regular navigation tools. However, this research focuses on adding safety metrics to route calculation and doesnt cover personal emergency responses or offline features.

      Genetic Algorithm-Based Personalized Travel Recommendation System [3]

      This system uses a Genetic Algorithm (GA) to create tailored travel routes based on user preferences such as interests and preferred activities. The method treats potential routes as “chromosomes” and uses a fitness function to evolve them into the best possible itinerary that minimizes travel time while maximizing satisfaction. Its main advantage is its ability to create highly customized and complex itineraries while managing practical limits like time and budget. However, this approach is great for personalization but lacks the real-time safety features needed for a full travel companion.

      Global Optimal Travel Planning for Massive Travel Queries in Road Network [4]

      This research tackles large-scale traffic congestion by proposing the Global Optimal Travel Planning (GOTP) problem, which aims to coordinate paths and departure times for many vehicles to boost network efficiency. The method uses an ongoing cycle of timing, routing, and traffic evaluation to reach a

      global optimal solution, which is especially useful for Connected Autonomous Vehicles (CAVs). The system’s strength is its ability to reduce congestion in a structured way. However, its focus is on improving the network as a whole, not on the individual travelers safety and experience.

      Travel Mate: A Comprehensive Travel Assistance Platform [5]

      Travel Mate is a mobile app that offers a full travel solution by combining route suggestions, meal recommendations, budget tracking, and secure document storage. Built with Flutter, the app integrates various APIs such as Google Maps and Zomato, and uses an Iterated Local Search (ILS) algorithm for creating itinerary. The main advantages include its ability to work across different platforms and provide essential utility features. While it covers a wide range of travel needs, it lacks the specialized AI features for emergency detection and offline functionality that are key to the Safarion platform.

      Estimating a Route Travel Time Distribution Function With Segment Correlations Using Segment-Level Travel Time Data Only:A Moment-Based Method [6]

      This paper presents a unique, moment-based, fully analytical method for estimating the distribution of travel times on a route using the simplest phase-degree data. This is important for evaluating the reliability of travel times. The method calculates the first four moments-mean, variance, skewness, kurtosis-of the travel time and uses a modified Cornish-Fisher expansion to create the distribution. A key benefit is that it requires less data, as it doesnt need extensive historical travel records.While this work is a good foundation for journey prediction, it doesnt address the safety concerns of someone in need during an emergency.

      Integrating Travel Survey and Amap API Data Into Travel Mode Choice Analysis With Interpretable Machine Learning Models: A Case Study in China [7]

      This study improves travel mode choice analysis by combining traditional survey data with actual travel path data from the Amap API. The methodology uses machine learning models like XGBoost, Random Forest along with (SHapley Additive exPlanations) to increase prediction accuracy and reveal complex behavior patterns. The use of real data significantly boosts model performance and understanding, showing a strong method for analyzing travel behavior. However, its not useful for responding in real-time emergencies.

      A Group Travel Recommender System Based on Group Approximate Constraint Satisfaction [8]

      This research proposes the GRec_Tr system, which is designed to recommend complete travel packages such as flights, hotels, and activities for groups. The method combines Collaborative Filtering (CF) with an approximate constraint satisfaction model to create recommendations that balance group agreement with individual preferences. The main contribution is its ability to

      automate group decision-making and increase individual satisfaction within the group. The system is highly focused on group travel planning and does not include personal safety features.

   2. Significance of the Research

      The Safarion platform is based on combining well-know theories and technologies to help make solo traveler safer.

      1. This uses machine learning models to spot signs of distress from data collected by sensors. In Safarion, AI recognizes speech is trained to pick up on specific words like Help me. This lets the system automatically send an SOS alert without the person needing to press a button, which is especially important during emergencies or when someone is unable to act on their own.
      2. Geofencing is a tool that activates a set action when a device moves into or out of a virtual area. Safarion uses GPS to track a user’s location in real-time and send alertsboth to the person and to their trusted contactsif they enter a place marked as unsafe by the community or if they suddenly veer off their planned route.
      3. This architectural principle prioritizes local data storage and functionality, ensuring that the application remains operational even without an internet connection. For Safarion, this is a cornerstone of reliability. Emergency alerts, location data, and timestamps are stored locally on the device and are automatically transmitted once a network connection is re-established.
      4. This concept leverages the power of crowdsourcing to gather and disseminate safety information. Safarion allows users to anonymously report unsafe areas or incidents. This aggregated, anonymized data is then used to create a dynamic safety map, warning other travelers of potential risks and suggesting safer alternative routes.
      5. This principle mandates the privacy and data protection be embedded into the design and architecture of IT systems from the outset. In Safarion, this is implemented through end-to-end encryption of sensitive data, user anonymization techniques for community reporting, and secure authentication protocols to protect personal information.

         TABLE I. SYSTEM DEVELOPMENT LIFECYCLE PHASES

         Phase	Description
         Requirement Analysis	Identification of safety, emergency, and travel needs of solo travelers.
         System Design	Architecture planning and module definition.
         Implementation	Development of frontend, backend, and AI services.
         Integration	Connecting APIs, cloud services, and database.
         Testing & Evaluation	Functional testing under online and offline scenarios.

   3. Gaps in the Literature

      The systematic review of existing literature and systems reveals several critical gaps tat Safarion is specifically designed to address. While current platforms excel at planning, optimization, and recommendation, they consistently fall short in providing a truly integrated and reliable safety net for solo travelers. The key shortcomings include:

      1. Limited focus on integrated emergency handling mechanisms. Most platforms treat safety as an add-on rather than a core feature, lacking automated distress detection or multi-channel alert systems.
      2. Heavy dependence on continuous internet connectivity. The functionality of most travel apps degrades significantly or ceases entirely in offline environments, rendering them unreliable in remote areas where they are needed most.
      3. Minimal consideration of the unique needs of vulnerable solo travelers. Existing systems are often designed with a generic user in mind and fail to address the specific anxieties and risks faced by women, students, or elderly explorers.
      4. Insufficient emphasis on privacy-preserving safety analytics. While some apps use location data, few are built with a robust framework for aggregating community safety intelligence in a way that protects user privacy. The identification of these gaps directly informed the methodological approach used to design a system that holistically addresses the challenges of modern solo travel.
3. METHODOLOGY

   This section details the systematic approach employed to design, develop, and evaluate the Safarion platform. The methodology was structured to ensure a robust, scalable, and secure system, centered on a modular architecture that integrates AI-driven intelligence with reliable real-time and offline functionality. The identification of these gapsparticularly the reliance on connectivity and lack of automated distress detectiondirectly informed the selection of a system development methodology focused on an offline-first

   architecture and AI-driven intelligence. The research followed a structured system development lifecycle, moving from requirement analysis to final evaluation.

   1. Research Design

      The proposed safety-focused travel assistance platform, a modular and scalable system architecture was designed. This structured design enables efficient communication between components while supporting real-time emergency handling, intelligent risk assessment, and secure data management. Core functions such as SOS detection, geofencing alerts, route safety analysis, community reporting, and emergency notifications are coordinated through the backend layer, while AI and third-party APIs provide speech recognition, mapping, and safety analytics. Local offline storage is also incorporated to ensure continued operation during network disruptions. The overall interaction among these components is illustrated in Fig. 1.

      Fig. 1 – OVERALL SYSTEM ARCHITECTURE

   2. Data Collection Methods

      The functionality of the Safarion platform is supported by a diverse set of data sources, each playing a crucial role in improving user safety and overall travel experience. These data sources collectively enable the system to deliver accurate, real- time, and context-aware services, including safe route recommendations, emergency assistance, and personalized travel guidance. By integrating location data, user inputs, external APIs, and historical information, Safarion ensures informed decision- making and adaptive system behavior. This multi-source data approach enhances reliability, responsiveness, and personalization, thereby strengthening the platforms effectiveness as an intelligent safety-focused travel companion.

      TABLE II. DATA SOURCES AND THEIR PURPOSE

      Data Sources Used in Safarion
      Data Type	Source	Purpose
      GPS Location Data	Mobile device sensors	Live tracking, route monitoring geofencing.
      Location APIs	Google Maps	Route guidance and identification of unsafe zone alerts.
      Travel Logs	Anonymized user data	Secure route planning.

   3. Sample Selection

      The target user population for the Safarion platform is solo travelers, with a specific emphasis on vulnerable demographics such as women, students, and elderly individuals. To ensure the system’s reliability and robustness, testing scenarios were designed to simulate diverse travel conditions, including dense urban environments, semi-urban areas, and remote locations characterized by low connectivity.

   4. Data Analysis Techniques

      Data processing and performance evaluation were conducted using a combination of logical rules, AI models, and quantitative metrics. The primary techniques included:

      1. Rule-based logic and AI models for the accurate detection of SOS triggers from voice commands and manual inputs.
      2. Location analytics to process anonymized user data for the dynamic identification and mapping of unsafe zones.
      3. Secure data storage and encryption protocols to ensure the integrity and privacy of all user data, both in transit and at rest. System performance was systematically evaluated based on key metrics, including response time for emergency alerts, reliability during connectivity loss, and the accuracy of safety alerts and route recommendations. The application of this structured methodology led to the development of a functional
      4. Platform, the results of which are presented in the following section.
4. RESULTS AND DISCUSSION

   This section explains and discusses the results from the systematic creation and testing of the Safarion platform. The findings show that the main features of the system were successfully implemented, confirming that its design work well as an AI-powered safety companion through the integration of key modules and favorable performance metrics. Beyond reporting these outcomes, the discussion analyzes their broader significance by examining how the findings align with existing

   research in AI-based safety systems and highlighting the platforms practical relevance in real-world scenarios. Additionally, the implications of the results are critically assessed, including system strengths, potential limitations, and areas for improvement, thereby providing a balanced perspective on the overall contribution and applicability of the Safarion platform.

   1. Interpretation of Results

      The core finding of this research is that the strategic integration of AI-driven emergency detection with robust offline intelligence significantly enhances traveler safety. This is a crucial advancement because it directly addresses the two primary failure points of conventional safety apps: user dependency and network dependency. The automation of SOS triggering via voice commands reduces the cognitive and physical burden on a traveler during a high-stress situation, ensuring that a call for help can be initiated even when manual operation of a device is impossible. This feature fundamentally increases the likelihood of a faster and more effective emergency response. Furthermore, the offline alert storage mechanism ensures that a distress signal is never lost due to poor connectivity, providing a reliable safety net in the very environments where travelers often feel most vulnerable. In addition, the combined use of automated detection and offline resilience creates a multi-layered protection model that operates continuously without requiring constant user attention. System testing results indicate improvedalert reliability and reduced response latency compared to traditional manual-trigger safety applications, thereby strengthening overall emergency preparedness for solo travelers.

   2. Comparison with Existing Literature

      When compared to the systems analyzed in the literature review, Safarion establishes a clear differentiation by prioritizing safety and emergency preparedness above all else. Platforms like Travel Mate or the AI-Enabled Travel Planner offer comprehensive solutions for itinerary planning and personalization but treat safety as a secondary feature, if at all. Route optimization systems focus on efficiency and, in some cases, road safety statistics, but they do not provide personal, real- time emergency response capabilities. Safarion’s unique advancements lie in its holistic, safety-centric design. The inclusion of offline emergency handling is a significant leap beyond the internet-dependent models of its predecessors. Moreover, innovative features like the scannable SOS QR code provide a practical tool for first responders that is not present in the other reviewed systems, further cementing its position as a next-generation safety companion.

   3. Implications

      The successful implementation of the Safarion platform has several positive implications for the future of travel and technology:

      1. The functionality of most travel apps degrades significantly or ceases entirely in offline environments, rendering them unreliable in remote areas where they are needed most.
      2. Safarion aligns with the goals of smart tourism by leveraging technology to create safer, more accessible, and more enriching travel experiences.
      3. The systems privacy-preserving community reporting framework promotes a proactive culture of collective responsibility, enabling travelers to support and protect one another while collaboratively strengthening overall travel safety and trust.

   C. Limitations

   It is also important to acknowledge the current constraints and

   The primary contribution of this project is the introduction of a novel, safety-centric framework for solo travel technology. Safarion pioneers a model where safety is not an afterthought but the foundational pillar of the travel experience. It demonstrates how artificial intelligence, mobile computing, and privacy- preserving community data can be seamlessly integrated into a unified digital solution that empowers users. This work provides a validated architecture that can serve as a blueprint for future developments in smart tourism, urban safety, and inclusive mobility.

   C. Recommendations for Future Research

   Building on the strong foundation established by this project, several promising directions for future research and enhancement have been identified:

   limitations of the system, which offer avenues for future 1) Extending the Safarion ecosystem to smartwatches and other

   improvement:

   1. The accuracy of GPS can be compromised in dense urban environments (e.g., “urban canyons”) or deep indoor

      wearable sensors would enable continuous, hands-free safety monitoring and could incorporate biometric data (e.g., heart rate) for even more sophisticated distress detection.

      locations, potentially affecting the precision of location 2) Future iterations could employ more advanced deep learning

      tracking.

   2. The effectiveness of the unsafe-zone mapping feature is dependent on a critical mass of user-contributed data, which may be limited in the initial stages of deployment.

      models, such as LSTMs, to better understand contextual cues and user behavior. This would allow for the personalization of safety alerts and recommendations based on an individuals travel patterns.

   3. The platform’s functionality relies on user consent for 3) To scale the platform for international use, future work should

   location and data sharing, and widespread adoption is necessary to build a robust community safety network. These limitations, while notable, do not detract from the core

   focus on integrating global safety datasets and automating the collection of cultural information, making the system accessible and relevant to a multilingual, worldwide audience.

   achievements of the study and provide a clear roadmap for 4) Implementing blockchain technology could provide a tamper-

   future refinement.

5. CONCLUSION

   This paper has detailed the design, development, and evaluation of Safarion, an AI-driven safety and travel companion for solo explorers. This concluding section summarizes the key

   proof, decentralized ledger for storing emergency data and community reports, further enhancing user trust and data security.

   1. 1. A significant future step would be to develop predictive models that can anticipate potential riskssuch as identifying historically unsafe times or locationsand proactively warn travelers before an incident occurs.

         findings of the project, articulates its principal contributions to 6) To further enrich the travel experience, AR features could be

         the field of travel technology, and presents a series of recommendations for future research and development.

         1. Summary of Key Findings

            The research successfully resulted in the design and implementation of Safarion, an intelligent, multi-layered platform that effectively addresses critical safety gaps in existing travel applications. The system demonstrates that by combining real-time AI-powered monitoring, robust offline emergency support, and integrated cultural assistance, it is possible to create a comprehensive safety net for solo travelers. Key findings from the evaluation confirm the high reliability of its voice-activated SOS alerts and the flawless performance of its offline data storage and transmission mechanism, proving its utility in diverse and unpredictable travel environments.

         2. Contribution to the Field

   incorporated, allowing users to point their device cameras at landmarks to receive interactive cultural and historical information overlays. Ultimately, Safarion stands as a testament to the potential of technology to foster a world where solo travelers can explore with greater safety, awareness, and confidence.

6. REFERENCES

1. Akshata C P., Jyotsna A J, Rakshitha V, Vaishnavi D, Sanjeevakumar M.Hatture & Swetha L. (2025). Artificial Intelligence-Enabled Travel Planner.
2. R Subash, Banu Priya Prathaban, Ganeshkumar N, & Gopinathan S (2024). Enhanced Route Optimization: Incorporating Road Safety Factors for Optimal Path Selection .
3. Ragini Sharma, (2024). Genetic Algorithm Based Personalized Travel Recommendation System
4. Yehong Xu, Lei Li, Mengxuan Zhang, Zizhuo Xu, & Xiaofang Zhou (2024). Global Optimal Travel Planning for Massive Travel Queries in Road Networks .
5. Preetham Krishna Sa, Remigius Gavin L, Anitha G (2025) Travel Mate: A Comprehensive Travel Assistance Platform.
6. Zihan Jian, Xiangdong Xu, & Zhsoqi Zang (2024) Estimating a Route Travel Time Distribution Function With Segment Correlations Using Segment-Level Travel Time Data Only: A Moment-Based Method.
7. Li Tang, Xuan Lin, Jingcai Yu, & Chuanli Tang, (2025). Integrating Travel Survey and Amap API Data Into Travel Mode Choice Analysis With Interpretable Machine Learning Models: A Case Study in China .
8. Jae Kyeong Kim, Woo Cheul Kwon IL Young Choi, Hyuk Heo, & Hyun Sil Moon (2024). A Group Travel Recommender System Based on Group Approximate Constraint Satisfaction.