arXiv:2606. 01046v1 Announce Type: new Abstract: The development of Large Language Models (LLMs) has significantly improved travel planning applications, yet evaluating such models is limited by existing benchmarks' limitations: 1) overemphasis on constraint compliance, neglecting multi-dimensional qualities like spatio-temporal cost; 2) datasets lacking real-world authenticity and coverage in key areas (e.
By Weiyi Chen, Shuaixiong Wang, Ziyun Gao, Kaichun Hu, Wangze Ni, Shimin Di, Chen Jason Zhang, Lei Chen
Behavior2Trip introduces a new task—Behavior‑Aware Travel Planning—where user preferences are inferred from past behavior trajectories rather than explicit instructions. The benchmark contains 11,400 instances from a major Chinese travel platform, each with nearly 40 recorded behaviors across 14 attributes and 5 preference dimensions. A reinforcement‑learning agent, B2T‑Agent, leverages these trajectories, external retrieval tools, and internal memory, outperforming GPT‑4.1 and other baselines on the dataset.
By Zihao Cheng, Yingyu Shan, Hongru Wang, Zeming Liu, Xinyi Wang, Xiangrong Zhu, Yuhang Guo, Wei Lin, Yunhong Wang
Behavior2Trip introduces a new task—Behavior‑Aware Travel Planning—where user preferences are inferred directly from past behavior trajectories rather than explicit instructions. The benchmark contains 11,400 Chinese travel‑planning instances, each with an average of 39.8 past behaviors across 14 attributes and 5 preference dimensions. A reinforcement‑learning agent, B2T‑Agent, leveraging behavior trajectories, external retrieval tools, and internal memory, outperforms strong baselines such as GPT‑4.1 on this challenging dataset.
arXiv:2602. 16727v2 Announce Type: replace Abstract: Simulating large-scale human mobility is fundamental to understanding population movement patterns and supporting real-world geospatial applications such as urban planning, epidemic response, and transportation analysis.
By Hua Yan, Heng Tan, Yingxue Zhang, Yu Yang
arXiv:2606. 15315v1 Announce Type: new Abstract: Personalized public transit routing in public transit systems remains challenging due to the difficulty of capturing and integrating diverse user preferences into routing algorithms.
By Tingting Yang, Chenhao Xue, Jun Chen
arXiv:2606. 12657v1 Announce Type: new Abstract: Human mobility data is important for transportation, urban planning, and epidemic control, but large-scale trajectory collection is often costly and privacy-constrained, motivating realistic synthetic trajectory generation.
By Siyu Li, Toan Tran, Lingyi Zhao, Khurram Shafique, Li Xiong