MobilityBench: A Benchmark for Evaluating Route-Planning Agents in Real-World Mobility Scenarios

• URL: http://arxiv.org/abs/2602.22638v1
• Date: Thu, 26 Feb 2026 05:39:38 GMT
• Title: MobilityBench: A Benchmark for Evaluating Route-Planning Agents in Real-World Mobility Scenarios
• Authors: Zhiheng Song, Jingshuai Zhang, Chuan Qin, Chao Wang, Chao Chen, Longfei Xu, Kaikui Liu, Xiangxiang Chu, Hengshu Zhu,
• Abstract summary: We introduce MobilityBench, a benchmark for evaluating large language models (LLMs)-based route-planning agents in real-world mobility scenarios.<n> MobilityBench is constructed from large-scale, anonymized real user queries collected from Amap.<n>We propose a multi-dimensional evaluation protocol centered on outcome validity, complemented by assessments of instruction understanding, planning, tool use, and efficiency.
• Score: 34.570930885283694
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 constructed from large-scale, anonymized real user queries collected from Amap and covers a broad spectrum of route-planning intents across multiple cities worldwide. To enable reproducible, end-to-end evaluation, we design a deterministic API-replay sandbox that eliminates environmental variance from live services. We further propose a multi-dimensional evaluation protocol centered on outcome validity, complemented by assessments of instruction understanding, planning, tool use, and efficiency. Using MobilityBench, we evaluate multiple LLM-based route-planning agents across diverse real-world mobility scenarios and provide an in-depth analysis of their behaviors and performance. Our findings reveal that current models perform competently on Basic information retrieval and Route Planning tasks, yet struggle considerably with Preference-Constrained Route Planning, underscoring significant room for improvement in personalized mobility applications. We publicly release the benchmark data, evaluation toolkit, and documentation at https://github.com/AMAP-ML/MobilityBench .

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