DangerMaps leverages retrieval-augmented language models to provide personalized safety information for travelers in urban environments.

• Addresses travelers' need for contextualized safety information not adequately met by search engines
• Combines LLM capabilities with map-based visualization to present safety ratings
• Uses retrieval augmentation to ground safety advice in factual data rather than hallucinated information
• Creates personalized recommendations based on traveler's context and specific needs

This research demonstrates how AI can transform urban travel security by delivering tailored safety advice beyond generic search results, helping travelers make informed decisions in unfamiliar environments.

DangerMaps: Personalized Safety Advice for Travel in Urban Environments using a Retrieval-Augmented Language Model