Agentic AI for Trip Planning Optimization Application

arXiv cs.AI / 5/4/2026

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Key Points

The paper argues that intelligent-vehicle trip planning should focus on route optimization (e.g., travel time, energy use, and traffic) rather than only generating feasible itineraries.
It introduces an agentic AI framework where an orchestration agent coordinates specialized agents for traffic, charging, and points of interest to iteratively refine plans.
To enable objective evaluation, the authors release the Trip-planning Optimization Problems Dataset, providing ground-truth optimal solutions and category-level task structure.
Experiments report 77.4% accuracy on the TOP Benchmark, outperforming single-agent and workflow-based multi-agent baselines, highlighting the value of orchestrated agentic reasoning.
The work also addresses a key benchmark limitation: prior references lacked ground truth, making true optimization performance hard to measure.

Abstract

Trip planning for intelligent vehicles increasingly requires selecting optimal routes rather than merely producing feasible itineraries, as interacting factors such as travel time, energy consumption, and traffic conditions directly affect plan quality. Yet existing systems are largely designed for feasibility-oriented planning, and current benchmarks provide only reference answers without ground truth, preventing objective evaluation of optimization performance. In our paper, we address these limitations with an agentic AI framework that enables dynamic refinement through an orchestration agent coordinating specialized agents for traffic, charging, and points of interest, and with the Trip-planning Optimization Problems Dataset, which supplies definitive optimal solutions and category-level task structure for fine-grained analysis. Experiments show that our system achieves 77.4\% accuracy on the TOP Benchmark, significantly outperforming single-agent and workflow-based multi-agent baselines, demonstrating the importance of orchestrated agentic reasoning for robust trip planning optimization.