The field of travel planning and human mobility is moving towards increased personalization and adaptability, with a focus on developing robust and resilient systems that can handle real-world disruptions and uncertainties. Recent research has highlighted the importance of evaluating and improving the temporal consistency and spatial coherence of travel itineraries generated by Large Language Models (LLMs). Additionally, there is a growing interest in understanding and replicating human mobility patterns, with a focus on capturing the cognitive hierarchy underlying travel decisions and developing scalable pathways for understanding and predicting complex urban mobility behaviors. Noteworthy papers in this area include: TripTide, which establishes a benchmark for evaluating adaptability and resilience in LLM-based travel planning under real-world uncertainty. Iti-Validator, which presents a validation framework for improving the temporal consistency of LLM-generated travel itineraries. From Narrative to Action, which proposes a hierarchical LLM-agent framework for generating human mobility patterns that capture the semantic coherence and causal logic of human behavior. Exploring Dissatisfaction in Bus Route Reduction, which employs an LLM-calibrated agent-based modeling approach to examine the impact of bus route cutbacks on passenger dissatisfaction and network resilience.