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MobilityBench: A Benchmark for Evaluating Route-Planning Agents in Real-World Mobility Scenarios
arXiv – CS AI|Zhiheng Song, Jingshuai Zhang, Chuan Qin, Chao Wang, Chao Chen, Longfei Xu, Kaikui Liu, Xiangxiang Chu, Hengshu Zhu||7 views
🤖AI Summary
Researchers introduce MobilityBench, a new benchmark for evaluating LLM-based route-planning agents using real-world mobility data from Amap. The study reveals that current AI models perform well on basic route planning but struggle significantly with preference-constrained routing tasks.
Key Takeaways
- →MobilityBench provides a scalable benchmark for testing LLM route-planning agents using anonymized real user queries from Amap across multiple cities.
- →The benchmark includes a deterministic API-replay sandbox to ensure reproducible evaluations without environmental variance.
- →Current LLM models demonstrate competent performance on basic information retrieval and standard route planning tasks.
- →AI agents show considerable weakness in preference-constrained route planning, highlighting limitations in personalized mobility applications.
- →The benchmark data, evaluation toolkit, and documentation are publicly available for research use.
#llm#route-planning#mobility#benchmark#ai-evaluation#transportation#real-world-data#machine-learning
Read Original →via arXiv – CS AI
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