智能体
arxiv.org ·
利用人口普查与土地利用数据引导的大语言模型生成个人出行日记
该研究提出一种 LLM 方案，用于生成基于智能体的交通模型所需的出行日记关键属性（目的、方式、距离）。方法不依赖专有家庭出行调查，而是从开源 ACS 与 Smart Location Database 数据随机生成 persona，再通过直接提示合成日记。作者提出 one-to-cohort 真实性评分（由出行次数、时间间隔、目的、方式四项指标组成），并用 Jensen-Shannon 散度对照 Connecticut Statewide Transportation Study 日记验证。结果显示 LLM 生成日记的整体真实性与在验证集上校准的经典方法（负二项生成出行次数、多项 Logit 预测方式/目的）相当（LLM 均值 0.692 vs 0.628），在出行目的判定上表现突出、方式预测一致性更高，而经典模型在出行次数与活动时长的数值估计上更优；聚合验证亦显示 LLM 具统计代表性（0.779 vs 0.706），表明其零样本可行性。
AI推荐理由 arXiv 论文（v3，2026-09-15 修订），方法描述具体：用 ACS 与 SLD 开源数据随机生成 persona，再直接提示 LLM 合成出行日记，并以 Jensen-Shannon 散度对照 CSTS 调查数据验证。给出可比较的量化结果（LLM 平均真实性 0.692 vs 经典方法 0.628；聚合验证 0.779 vs 0.706），并指出 LLM 在出行目的与方式上更一致、经典模型在出行次数与活动时长数值估计上更好，局限明确。属于交通仿真与合成数据交叉的应用研究，对 LLM 在 agent-based 建模中的可行性有参考价值，但受众较窄，非通用 AI 突破。
72
正文
Computer Science > Computation and Language
arXiv:2509.09710 (cs)
[Submitted on 7 Sep 2025 (v1), last revised 15 Sep 2026 (this version, v3)]
Title:Generating Individual Travel Diaries Using Large Language Models Informed by Census and Land-Use Data
Authors:Sepehr Golrokh Amin, Devin Rhoads, Fatemeh Fakhrmoosavi, Nicholas E. Lownes, John N. Ivan
View a PDF of the paper titled Generating Individual Travel Diaries Using Large Language Models Informed by Census and Land-Use Data, by Sepehr Golrokh Amin and 4 other authors
View PDF
Abstract:This study introduces a Large Language Model (LLM) scheme for generating key attributes of travel diaries in agent-based transportation models, including purpose, mode and distance, to assess the underlying viability of LLMs for activity generation tasks. While traditional approaches rely on large quantities of proprietary household travel surveys, our method generates personas stochastically from open-source American Community Survey (ACS) and Smart Location Database (SLD) data, then synthesizes diaries through direct prompting. Our study features a novel one-to-cohort realism score: a composite of four metrics (Trip Count Score, Interval Score, Purpose Score, and Mode Score) validated against the Connecticut Statewide Transportation Study (CSTS) diaries, matched across demographic variables. Our validation utilizes Jensen-Shannon Divergence to measure distributional similarities between generated and real diaries. When compared to diaries generated with classical methods (Negative Binomial for trip generation; Multinomial Logit for mode/purpose) calibrated on the validation set, LLM generated diaries achieve comparable overall realism (LLM mean: 0.692 vs. 0.628). The LLM excels in determining trip purpose, and its trip mode predictions demonstrate greater consistency (a narrower Realism Score distribution). Meanwhile, classical models lead to better numerical estimates of trip count and activity duration. Aggregate validation confirms the LLM's statistical representativeness (LLM mean: 0.779 vs. 0.706), demonstrating LLM's zero-shot viability and establishing a quantifiable metric of diary realism for future synthetic diary evaluation systems.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as:
arXiv:2509.09710 [cs.CL]
(or
arXiv:2509.09710v3 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2509.09710
Focus to learn more
arXiv-issued DOI via DataCite
Submission history
From: Sepehr Golrokh Amin [view email]
[v1]
Sun, 7 Sep 2025 17:03:08 UTC (1,705 KB)
[v2]
Mon, 20 Oct 2025 23:59:26 UTC (1,661 KB)
[v3]
Tue, 15 Sep 2026 15:21:29 UTC (1,929 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Generating Individual Travel Diaries Using Large Language Models Informed by Census and Land-Use Data, by Sepehr Golrokh Amin and 4 other authors
View PDF
view license
Current browse context:
cs.CL
new
|
recent
| 2025-09
Change to browse by:
cs
cs.AI
cs.LG
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
export BibTeX citation
Loading...
BibTeX formatted citation
×
loading...
Data provided by:
Bookmark
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? |
Disable MathJax (What is MathJax?)