Blockchain and AI-Powered Tourist Safety System for Real-Time Risk Management
Bibliographic record
Abstract
Tourism serves as a cornerstone of cultural exchange and economic growth. However, persistent safety challenges and unreliable information continue to undermine traveler confidence, particularly in developing regions such as India. TrustTrip introduces an integrated AI–Blockchain framework for real-time tourist safety management, combining data transparency, security and intelligent assistance. The blockchain layer, secured with AES encryption and JWS tokens, ensures tamper-proof data integrity and trustworthy communication between users and authorities. Complementing this, an AI-driven chatbot delivers contextual safety insights, personalized recommendations and emergency support. A multimodal alert module dispatches verified notifications to first responders and emergency contacts through SMS and push channels, embedding geolocation and severity metadata for timely intervention. In parallel, a digital twin–based visualization layer generates dynamic safety heatmaps and crowd-density overlays, empowering authorities with proactive situational awareness. By fusing blockchain transparency with AI adaptability, TrustTrip establishes a secure, intelligent and scalable paradigm for enhancing tourist safety, trust, and governance in emerging smart destinations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".