Update README.md

README.md

@@ -7,4 +7,64 @@ tags:
 - Route-Planning
 size_categories:
 - 10K<n<100K
----
+configs:
+- config_name: default
+  data_files:
+  - split: query
+    path: dataset/sample_10.csv
+---
+
+**This work is currently under review. The full dataset will be released progressively.**
+
+# MobilityBench: A Benchmark for Evaluating Route-Planning Agents in Real-World Mobility Scenarios
+
+[Paper](https://arxiv.org/abs/2602.22638) | [GitHub](https://github.com/AMAP-ML/MobilityBench)
+
+IntTravel is the first large-scale public dataset for integrated travel recommendation, including **4.1 billion interactions from 163 million users with 7.3 million POIs**. Built upon this dataset, the authors introduce an end-to-end, decoder-only generative framework for multi-task recommendation. It incorporates information preservation, selection, and factorization to balance task collaboration with specialized differentiation.
+
+
+
+## Dataset
+**MobilityBench** is a scalable benchmark for evaluating route-planning agents in real-world mobility scenarios. It is built from large-scale, anonymized mobility queries from **Amap**, organized with a comprehensive task taxonomy, and provides **structured ground truth** (required tool calls + verifiable evidence). All tool calls are executed in a **deterministic replay sandbox** for reproducible, multi-dimensional evaluation.
+
+**Scale & Coverage:** 100,000 episodes across **22** countries and **350+** cities (including metropolitan areas), with a **long-tailed** geographic distribution.
+
+### Scenario Distribution (11 intents)
+- **36.6%** Basic Information Retrieval
+- **9.6%** Route-Dependent Information Retrieval
+- **42.5%** Basic Route Planning
+- **11.3%** Preference-Constrained Route Planning
+
+**Download:** [HuggingFace - MobilityBench](https://huggingface.co/datasets/GD-ML/MobilityBench/tree/main) -> place files into `data/`
+
+#### Data Format
+
+| Field | Description |
+|-------|-------------|
+| `query` | User query text |
+| `context` | Context information (JSON, e.g., current location, city) |
+| `task_scenario` | Fine-grained task category |
+| `intent_family` | Coarse-grained intent category for evaluation aggregation |
+| `tool_list` | Expected tool calls (JSON array) |
+| `route_ans` | Ground truth route answer (JSON) |
+
+#### Sample Data (5 Examples)
+
+| Query | Task Scenario | Intent Family |
+|-------|---------------|---------------|
+| 去大石桥不走高速<br>Go to Dashiqiao without taking the highway. | Option-Constrained Route Planning | Preference-Constrained Route Planning |
+| 现在成都大道会堵车吗？看一下地图，会不会堵<br>Is Chengdu Avenue congested now? Looking at the map, is it likely to be congested? | Traffic Info Query | Basic Route Planning |
+| 我在哪<br>Where am I? | Geolocation Query | Basic Information Retrieval |
+| 知道离滇池会展中心有多远<br>How far it is from Dianchi Convention and Exhibition Center? | Route Property Query | Route-Dependent Information Retrieval |
+| 到寨河收费站入口不走高速<br>To reach the Zhaihe toll station entrance without taking the highway. | Option-Constrained Route Planning | Preference-Constrained Route Planning |
+
+## Citation
+If you use this dataset in your research, please cite the following paper:
+```bibtex
+@article{song2026mobilitybench,
+  title={MobilityBench: A Benchmark for Evaluating Route-Planning Agents in Real-World Mobility Scenarios},
+  author={Song, Zhiheng and Zhang, Jingshuai and Qin, Chuan and Wang, Chao and Chen, Chao and Xu, Longfei and Liu, Kaikui and Chu, Xiangxiang and Zhu, Hengshu},
+  journal={arXiv preprint arXiv:2602.22638},
+  year={2026}
+}
+```