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SafeStep represents a significant application of large language models and behavioral prediction engines to solve complex real-world mobility challenges for vulnerable populations. By integrating route planning with personalized failure scenario generation via LLMs, the system demonstrates how generative AI can be tailored for high-stakes human assistance tasks beyond standard consumer applications. While interface usability remains an area for improvement, the core methodology of combining predictive modeling with intervention evaluation offers a robust framework for future assistive technologies in healthcare and social services.
SafeStep: AI-powered Travel Assistance for Elderly People with Frailty or Dementia
arXiv:2607.21156v1 Announce Type: new Abstract: 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.
Abstract: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.
Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
arXiv:2607.21156 [cs.AI]
(or arXiv:2607.21156v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.21156 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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