The paper evaluates how Retrieval-Augmented Generation (RAG) can improve Large Language Model (LLM) predictions of travel mode choice. Four retrieval strategies—basic RAG, balanced retrieval, cross‑encoder re‑ranking, and a combination of balanced retrieval with cross‑encoder—are tested on three LLMs (GPT‑4o, o4‑mini, o3) using 2023 Puget Sound travel survey data. Results show that RAG boosts accuracy across models, with GPT‑4o plus balanced retrieval and cross‑encoder achieving 80.8% accuracy, surpassing traditional statistical and machine learning baselines and demonstrating strong zero‑shot transfer.
By Yiming Xu, Junfeng Jiao
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
arXiv:2604. 10475v2 Announce Type: replace Abstract: Modeling household-level decisions is central to many real-world applications, including trip planning, residential mobility and migration, disaster management, etc.
By Yuran Sun, Mustafa Sameen, Yaotian Zhang, Rongguan Gu, Mrunal Vibhute, Chia-yu Wu, Yuanyuan Lei, Xilei Zhao
The paper demonstrates that fine‑tuning large language models (LLMs) on local tourist trajectory data can predict visitor movements under varying conditions. Using 566 trajectories from Wakayama Castle Park, Japan, the authors fine‑tuned Llama‑3.1‑8B, achieving 49.1% accuracy for next point‑of‑interest predictions and maintaining strong performance even on undersampled scenarios such as rainy days. This shows that LLMs can serve as high‑fidelity, context‑aware behavior models for tourist prediction and enable counterfactual analysis of mobility interventions.
By Tatsuya Amano, Hirozumi Yamaguchi
arXiv:2606. 11070v1 Announce Type: cross Abstract: Recent advances in reasoning and tool-calling capabilities of large language models (LLMs) have enabled increasingly capable agentic systems.
By Genta Indra Winata, Amartya Chakraborty, Yuzhen Lin, Swasthi P Rao, Shikhhar Siingh, Houhan Lu, Nadia Bathaee, Sriharsha Hatwar, Paresh Dashore, Anmol Jain, Kshitij Tayal, Xiuzhu Lin, Anirban Das, Sambit Sahu, Shi-Xiong Zhang
arXiv:2607. 17437v1 Announce Type: new Abstract: Large language model (LLM) agents offer a generative approach to simulating human behavior under conditions that may have few or no direct historical analogues, a common challenge in disaster and infrastructure-disruption planning.
By Chen Xia, Zexi Kuang, Yuqing Hu
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
arXiv:2608.30924v1 Announce Type: new
Abstract: Travel itinerary generation requires balancing strict spatio-temporal constraints with human preferences. Existing LLM-based planners mainly rely on st...
By Priyanshu Karmakar, Borru Vijay Sai, Shubhojit Mallick, Abhik Jana, Shreya Ghosh, Manish Gupta
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:2606. 15890v1 Announce Type: new Abstract: Understanding urban wellbeing from multimodal data requires integrating heterogeneous spatial and temporal signals, posing significant challenges for current multimodal large language models (MLLMs).
By Yanxin Xi, Xiang Su, Jie Feng, Yu Liu, Sasu Tarkoma, Pan Hui
arXiv:2607. 26724v1 Announce Type: new Abstract: Large language model (LLM) agents have been widely applied in automating data science tasks.
By Zhilun Zhou, Jianghao Yu, Yuming Lin, yongjun yang, Sun Yongquan, Depeng Jin, Yong Li
arXiv:2507.21790v3 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) are becoming widely used to support various workflows across different disciplines, yet their potential in discr...
By Georges Sfeir, Gabriel Nova, Stephane Hess, Sander van Cranenburgh