Agentic AI for Trip Planning Optimization Application

2026-04-30

AI Summary

This study addresses the limitations of existing intelligent trip planning systems, which typically generate only feasible routes without jointly optimizing multiple objectives such as travel time, energy consumption, and traffic conditions, and lack evaluation benchmarks with ground-truth optimal solutions. To overcome these challenges, this work proposes a coordinated multi-agent framework in which a central orchestrating agent dynamically integrates specialized agents responsible for traffic, charging, and points of interest to enable holistic itinerary optimization. The key contributions include the first-ever Trip Optimization Planning (TOP) dataset containing real-world optimal itineraries, along with a corresponding benchmark suite. Experimental results demonstrate that the proposed approach significantly outperforms both single-agent and pipeline-based multi-agent baselines on this benchmark, achieving an accuracy of 77.4%.

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.

Problem

• trip planning optimization
• agentic AI
• benchmark evaluation
• optimal route selection
• intelligent vehicles

Innovation

• Agentic AI
• Orchestration Agent
• Trip Planning Optimization
• Optimal Solution Benchmark
• Multi-agent Coordination