TripTide: A Benchmark for Adaptive Travel Planning under Disruptions
Priyanshu Karmakar (1), Soumyabrata Chaudhuri (1), Shubhojit Mallick (2), Manish Gupta (2), Abhik Jana (1), Shreya Ghosh (1) ((1) School of Electrical, Computer Sciences, IIT Bhubaneswar, India, (2) Microsoft, India)

TL;DR
TripTide introduces a comprehensive benchmark to evaluate how well Large Language Models can adapt travel itineraries in response to realistic disruptions, addressing a critical gap in travel planning AI.
Contribution
This paper presents the first benchmark, TripTide, for assessing LLMs' ability to revise travel plans under disruptions, including new automatic metrics and evaluation methods.
Findings
LLMs maintain strong sequential and semantic consistency during revisions.
Spatial deviations are larger in shorter trips but improve with longer plans.
Disruption-handling ability decreases as plan length increases.
Abstract
Recent efforts like TripCraft and TravelPlanner have advanced the use of Large Language Models ( LLMs) for personalized, constraint aware travel itinerary generation. Yet, real travel often faces disruptions. To address this, we present TripTide, the first benchmark evaluating LLM's ability to revise itineraries under realistic disruptions. TripTide models key dimensions such as disruption severity and traveler tolerance, enabling nuanced assessment of LLM adaptability to events like flight cancellations, weather closures, or overbooked attractions. We conduct a threefold evaluation. First, we introduce automatic metrics including Preservation of Intent (how well the revised plan maintains feasibility and goals), Responsiveness (promptness and appropriateness of disruption handling), and Adaptability (semantic, spatial, and sequential divergence between original and revised plans).…
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Taxonomy
TopicsHuman Mobility and Location-Based Analysis · Data Management and Algorithms · Transportation and Mobility Innovations
