# SafeStep: AI-powered Travel Assistance for Elderly People with Frailty or Dementia

arXiv: https://arxiv.org/abs/2607.21156 · HTML: https://mlsanity.com/paper/2607.21156/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 23 Jul 2026.

## Builds on (most cited first, 8 of 8)

- [Human Action Recognition and Prediction: A Survey](https://mlsanity.com/paper/1806.11230/) (2018)
- [AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?](https://mlsanity.com/paper/2307.16368/) (2023)
- [Finetuning LLMs for Human Behavior Prediction in Social Science Experiments](https://mlsanity.com/paper/2509.05830/) (2025)
- [Mobility-LLM: Learning Visiting Intentions and Travel Preferences from Human Mobility Data with Large Language Models](https://mlsanity.com/paper/2411.00823/) (2024)
- [GG-LLM: Geometrically Grounding Large Language Models for Zero-shot Human Activity Forecasting in Human-Aware Task Planning](https://mlsanity.com/paper/2310.20034/) (2023)
- [TR-LLM: Integrating Trajectory Data for Scene-Aware LLM-Based Human Action Prediction](https://mlsanity.com/paper/2410.03993/) (2024)
- [Predicting User Behavior in Smart Spaces with LLM-Enhanced Logs and Personalized Prompts](https://mlsanity.com/paper/2412.12653/) (2024)
- [Supporting the Digital Autonomy of Elders Through LLM Assistance](https://mlsanity.com/paper/2407.15695/) (2024)

## Abstract (arXiv)

More than a million people in the UK suffer from frailty or dementia, which severely compromise their ability to travel in urban environments. This paper presents SafeStep, an AI-driven travel system that assists elderly users with their journeys. At the core of SafeStep is a novel travel graph representation, which integrates route planning with predictive modelling. For each stage of a journey, the system (i) generates personalized failure scenarios using a combi-nation of LLMs and the Anticip8 behavioral prediction engine, (ii) proposes targeted interventions, and (iii) estimates the impact of interventions on out-come probabilities. This enables SafeStep to select interventions that maximize the likelihood of the person reaching their destination. SafeStep was evaluated through experiments on travel graph generation and a field study involving 26 real-world journeys. Results showed that combining Anticip8 for failure pre-diction with GPT-based models for intervention evaluation yields the most re-liable performance. User feedback indicated that SafeStep improves confidence and perceived safety during travel, although interface usability needs to be im-proved for the target demographic. In the future, we would like to improve and release SafeStep. The AI system that was developed for SafeStep could be ap-plied in other areas, such as mental health, career coaching and addiction treatment.