MobilityBench: A Benchmark for Evaluating Route-Planning Agents in Real-World Mobility Scenarios
arXiv:2602.22638v2 Announce Type: replace Abstract: Route-planning agents powered by large language models (LLMs) have emerged as a promising paradigm for supporting everyday human mobility through natural language interaction and tool-mediated decision making. However, systematic evaluation in real-world mobility settings is hindered by diverse routing demands, non-deterministic mapping services, and limited reproducibility. In this study, we introduce MobilityBench, a scalable benchmark for evaluating LLM-based route-planning agents in real-world mobility scenarios. MobilityBench is construc
The proliferation of LLMs creates a need for robust evaluation benchmarks to validate their increasing application in real-world scenarios, which is critical for their adoption and improvement.
A standardized benchmark for LLM-based route-planning agents facilitates performance comparison, accelerates development, and ensures reliability in a critical future application of AI.
The introduction of MobilityBench provides a common framework for assessing language model performance in complex, real-world mobility tasks, moving beyond theoretical capabilities to practical utility.
- · AI researchers
- · route-planning software developers
- · logistics companies
- · transportation sector
- · untested LLM-based agents
- · less rigorous evaluation methodologies
The benchmark allows for objective comparison and advancement of LLM-based route-planning agents.
Improved and more reliable AI agents could lead to more efficient and personalized urban mobility solutions.
The success of such benchmarks could inspire similar evaluation frameworks for other complex AI agent applications, further accelerating AI integration into daily life.
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Read at arXiv cs.AI