CausalMob: Causal Human Mobility Prediction with LLMs-derived Human Intentions toward Public Events

Hangli Ge; Jiawei Wang; Noboru Koshizuka; Renhe Jiang; Ryosuke Shibasaki; Xiaojie Yang; Zipei Fan

CausalMob: Causal Human Mobility Prediction with LLMs-derived Human Intentions toward Public Events

T0 review · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read CausalMob claims that injecting LLM-extracted human intentions as causal treatments, with a reweighting and counterfactual loss that removes confounder bias, yields the best reported crowd-flow forecasts across three prediction horizons…

desk verdict A genuinely new LLM-derived treatment representation for event-aware mobility prediction, with credible short/medium-term gains and honest ablations, but the causal claims need an unconfoundedness defense (weather is missing) and the long-term results are within noise. read the letter →

arxiv 2412.02155 v2 pith:7MIGMRLJ submitted 2024-12-03 cs.LG cs.AIcs.IRcs.SI

Xiaojie Yang , Hangli Ge , Jiawei Wang , Zipei Fan , Renhe Jiang , Ryosuke Shibasaki , Noboru Koshizuka This is my paper · ORCID

classification cs.LGcs.AIcs.IRcs.SI

keywords humanmobilitypredictioncausalinferencelargelanguagemodelspubliceventscounterfactuallossspatio-temporalreweightingaveragetreatmenteffect

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

CausalMob claims that human mobility prediction under non-periodic public events can be improved by treating LLM-extracted human intentions toward those events as causal treatments and adjusting away spatio-temporal confounders. The paper builds a pipeline: news articles are converted by prompted LLMs into structured event features and ten intention scores; a graph-based spatio-temporal network learns regional confounders; a reweighting network and a counterfactual loss based on IPM distance make the estimated event effects independent of confounders. On one year of GPS mobility data across 490 Japanese regions, CausalMob reports the best RMSE, MAE, and MAPE on short-, medium-, and long-term tasks, with ablations indicating both the LLM-derived treatments and the counterfactual loss contribute. A sympathetic reader would care because predictable but non-periodic events, such as typhoons and festivals, are exactly where standard periodic mobility models fail.

What carries the argument

The load-bearing object is the causal graph with treatment $E$ (public events), outcome $Y$ (future human mobility), and confounders $Z$ learned from observations. The machinery has five pieces: (1) prompted Llama3-70b with chain-of-thought converts news into structured event time, location, and ten 0-100 intention scores; (2) a GCN-based ST-ResNET, a spatio-temporal residual network architecture, fuses POI, time, region, historical flow, and re-normalized OD adjacency into the confounder $z$; (3) a GRU encodes intentions into the treatment $T$; (4) a reweighting network outputs weights $w$ to balance selection bias; (5) the loss is the reweighted factual MSE plus $\alpha$ times an IPM/MMD counterfactual upper bound. The IPM loss, a distribution-distance penalty implemented with Maximum Mean Discrepancy, is what forces the learned treatment distribution to look independent of confounders, which is the step that turns statistical adjustment into causal-effect estimation.

What would settle it

Find or construct a hidden confounder, such as a same-day economic shock or social-media trend that independently raises both event likelihood and mobility; if adding that variable to the covariates changes the estimated treatment effect or removes CausalMob's prediction advantage over a version without causal adjustment, the unconfoundedness assumption is violated. A simpler check is to randomize the LLM intention scores across regions while keeping event occurrence fixed: if predictions do not degrade, the claimed causal mechanism is not actually carrying the gain.

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Extended reading notes

Core claim

The paper's central claim is that causal adjustment over LLM-derived event representations yields both more accurate crowd-flow forecasts and interpretable estimates of how specific events push mobility up or down. CausalMob encodes each region's news-derived event features into a treatment vector $T$, learns a confounder representation $z$ from POI, time, region, historical flow, and OD network data, then trains an inference network on a reweighted factual loss plus a counterfactual auxiliary loss that bounds the average dose-response error via IPM distance. The reported results show gains over all baselines and, in ablations, removing the counterfactual loss or replacing intention scores with zeros degrades performance; case studies show a weather event's forecast error drops near the event time only when intention features are used.

Load-bearing premise

The model assumes the data it feeds in, including shops and facilities, time, region, past movement, and travel connections, include every outside factor that could influence both whether an event happens and where people go; if any such factor is missing, the estimated event effects are biased.

Editorial extensions

If this is right

Short-term forecasts improve most: reported RMSE drops 10.43%, MAE 13.25%, and MAPE 10.00% over the best baseline for the 6-in/1-out task.
The framework turns unstructured news into quantitative, region-specific treatment features, so event effects can be predicted even in regions that have not experienced that event before.
Because the LLM corrects event time, news that arrives after an event can still be aligned for prediction.
The counterfactual loss contributes on top of reweighting, so the causal adjustment is doing work beyond simple augmentation with event features.
The model provides interpretable causal responses: a fireworks festival raises mobility at all predicted timestamps while an earthquake suppresses it at midday and night.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial inference: the same pipeline could be pointed at live news feeds to nowcast mobility during unfolding disasters, but the paper only validates on retrospective one-year data, so real-time latency and LLM cost are untested.
Editorial inference: the ten intention scores are human-interpretable treatment dimensions; if they generalize across regions and time, they could serve as a reusable event-effect lexicon for other mobility or traffic models.
Editorial inference: the method's reliance on news articles and manually labeled region codes means transfer to other countries or languages requires comparable event corpora and region mapping, which the paper does not address.
Editorial inference: the ablation with all-zero intentions tests presence versus absence of LLM features, but not whether LLM extraction adds value over a cheaper keyword-based event detector; that comparison would isolate the value of semantic intention modeling.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Dear colleague,

Short version: CausalMob is a legitimate, useful paper, but read the causal claims with a grain of salt. The genuinely new piece is the treatment representation — LLM-extracted ten-dimensional human-intention scores from news articles, fed into an ADRF-style counterfactual framework for mobility prediction. That is a real idea, and the paper executes it cleanly. The prediction results are credible for short and medium horizons: CausalMob beats strong baselines like STID and DeepSTN+ by clear margins, and the ablations (dropping the counterfactual loss, the reweighting, or the LLM intentions) all point the same way: the causal components earn their keep. The authors also open-sourced code and provide a synthetic-data path so others can run it. That counts.

The soft spots are in proportion: they do not sink the paper, but they should be addressed. First, the long-term (In 24, Out 24) gains over STID are within one standard deviation, and no significance tests are reported. Second, the text in Section 5.2 says the model is weak on long-term MAE and MAPE, while Table 1 shows it is best on both; that contradiction needs fixing. Third, the LLM intention scores are never validated against human labels or any ground truth; the distribution plots in Figure 6 are descriptive, not evidence of accuracy. Fourth, and most important, the causal interpretation rests on unconfoundedness. The authors claim in Section 4.2 that accurate estimation relies on it, but the confounder set Z contains POIs, time, region embeddings, historical mobility, and OD adjacency — no weather. Their own typhoon case shows wind speed and precipitation driving both the event and mobility changes, so the ATE curves in Figures 10–11 could be biased, and some of the ablation improvement attributed to causal adjustment could come from treatment features proxying for the omitted weather. No sensitivity analysis is offered.

My bottom line: this is an engineering contribution with a novel treatment representation and honest evaluation except for the long-term contradiction. The causal language oversells the unconfoundedness, but the prediction claim is independent and the ablations support it. I would send it to peer review — a careful referee can push on the omitted-confounder issue and the missing significance tests. I would also bring it to the reading group; it is a good example of how to inject LLM-derived event information into predictive models, and a good case study in where causal assumptions bite.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the performance claim is validated on held-out test data against external baselines, and no derivation step reduces by construction to its own inputs.

full rationale

The paper's central performance claim is evaluated on a held-out test split (80/10/10, repeated with fixed seeds) against external baselines (3DGCN, GTS, PromptST, STGNN, DeepST, DeepSTN+, STID), so the reported improvements are not equivalent to the training inputs. The ablation rows (Ours vs. Ours (Naive), Ours (wo LLM), Ours (wo R), Ours (wo Lcf)) compare the same backbone with components removed and are empirical, not definitional. The LLM-derived human-intention features are generated once from news text and then fed into the predictor; no equation defines predicted mobility solely in terms of the intentions, and the counterfactual loss is a learned regularization rather than a pre-imposed answer. The causal-effect plots (Figures 10 and 11) are model outputs not validated against observed counterfactuals, and the unconfoundedness assumption is untestable with the provided data; those are validity limitations, not circularity. Self-citations such as Ref. [48] are used as related work and are not load-bearing. I therefore find no step in the claimed derivation chain that reduces by construction to its own inputs.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The model introduces no new physical or conceptual entities. It relies on a set of hand-set hyperparameters, an untestable unconfoundedness assumption, and an unvalidated LLM output stream. The main fitted quantities are neural network weights, which are standard and not enumerated. The causal inference depends on the assumed causal graph and the ADRF bound from prior work.

free parameters (6)

alpha (counterfactual loss weight) = 1
Set in Section 5.1 without a sensitivity analysis; controls the balance between factual and counterfactual loss in Eq. (10).
J (number of treatment similarity intervals) = 14
Chosen with 0.1 cosine-similarity steps in Section 5.5; affects the IPM/MMD counterfactual loss computation in Eq. (9).
number of POI categories = 17
Aggregated in Section 5.1 and used to build the time-varying covariate X^P in Eq. (2).
input/output window sizes = In 6/Out 1; In 12/Out 6; In 24/Out 24
Three task configurations in Section 5.2 and Table 1; prediction performance depends on these choices.
hidden state dimension = 64
Listed in Appendix B; standard hyperparameter for the GCN/GRU modules.
predictability filtering for output-window events = LLM Yes/No, only predictable retained
Section 5.4 restricts output-window treatments to predictable events to avoid leakage; this choice changes the treatment input and is not swept.

assumptions (5)

domain assumption Unconfoundedness: observed covariates Z (POI, time, region embeddings, historical mobility, OD network) capture all confounders of event and mobility
Stated in Section 4.2 and the causal graph of Figure 2. The entire causal adjustment depends on this untestable assumption.
standard math The ADRF loss upper bound of Wang et al. 2022 is valid and applicable to the multidimensional treatment representation used here
Invoked in Section 4.2 via reference [36] to justify using the IPM/MMD counterfactual loss; the paper does not re-derive the bound.
domain assumption News articles from Kyodo News provide complete coverage of public events relevant to mobility in the study period
Section 3.1 assumes the news corpus is the event source; unobserved events not reported would act as unmeasured confounders.
domain assumption The LLM (Llama3-70b) with the designed prompts produces faithful, mobility-relevant human intention scores without systematic bias
Section 4.1 and Appendix E; no human evaluation or consistency check of the 10 intention scores is reported.
domain assumption Manual labeling of region codes for news articles is accurate
Section 3.1: 'align the news articles with the corresponding regions using manually labeled region codes'; errors would misassign treatments.

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Cite this review

Pith. "Pith review of CausalMob: Causal Human Mobility Prediction with LLMs-derived Human Intentions toward Public Events." pith.science (2026). https://pith.science/paper/7MIGMRLJ

@misc{pith2026241202155,
  author       = {Pith},
  title        = {Pith review of: CausalMob: Causal Human Mobility Prediction with LLMs-derived Human Intentions toward Public Events},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7MIGMRLJ}},
  note         = {Machine review of arXiv:2412.02155}
}

read the original abstract

Large-scale human mobility exhibits spatial and temporal patterns that can assist policymakers in decision making. Although traditional prediction models attempt to capture these patterns, they often interfered by non-periodic public events, such as disasters and occasional celebrations. Since regular human mobility patterns are heavily affected by these events, estimating their causal effects is critical to accurate mobility predictions. Although news articles provide unique perspectives on these events in an unstructured format, processing is a challenge. In this study, we propose a causality-augmented prediction model, called CausalMob, to analyze the causal effects of public events. We first utilize large language models (LLMs) to extract human intentions from news articles and transform them into features that act as causal treatments. Next, the model learns representations of spatio-temporal regional covariates from multiple data sources to serve as confounders for causal inference. Finally, we present a causal effect estimation framework to ensure event features remain independent of confounders during prediction. Based on large-scale real-world data, the experimental results show that the proposed model excels in human mobility prediction, outperforming state-of-the-art models.

Figures

Figures reproduced from arXiv: 2412.02155 by the authors.

**Figure 1.** Conceptual graph of this research leverage LLMs to simulate human intentions toward public events, enabling an explainable exploration of the causal relationship in human mobility [18, 34]. However, traditional causal inference methods that use randomized controlled trials to study causal effects [4] face difficulties in abstracting public event features and controlling their occurrence. Instead, researchers overco… view at source ↗

**Figure 2.** Causal graph of this research from the developed media industry, we can obtain high-quality reports on public events through news articles and extract structured information from them [17, 35]. This is illustrated in [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Public events examples along with changes of daily [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Figures from the paper (8 more)

**Figure 4.** Figure 4: Number of News labeled with categories [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]

**Figure 5.** Figure 5: Framework of CausalMob: We first process news articles to obtain structured information of public events in part [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]

**Figure 6.** Figure 6: Distribution of LLMs-derived human intentions [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

**Figure 7.** Figure 7: Count of news articles labels with public events [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

**Figure 9.** Figure 9: Distribution of news articles labels based on treat [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]

**Figure 8.** Figure 8: Visualization of encoded treatments with t-SNE. [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]

**Figure 10.** Figure 10: Regional average treatment effects of human mo [PITH_FULL_IMAGE:figures/full_fig_p009_10.png]

**Figure 11.** Figure 11: Causal Responses with Counterfactual. We show [PITH_FULL_IMAGE:figures/full_fig_p009_11.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

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Reference graph

Works this paper leans on

51 extracted references · 30 canonical work pages · cited by 1 Pith paper

[1]

Blogwatcher. 2024. Blogwatcher, Inc. https://www.blogwatcher.co.jp/ (Japanese Only)

work page 2024
[2]

Quanjun Chen, Renhe Jiang, Chuang Yang, Zekun Cai, Zipei Fan, Kota Tsubouchi, Ryosuke Shibasaki, and Xuan Song. 2020. Dualsin: Dual sequential interaction network for human intentional mobility prediction. In Proceedings of the 28th International Conference on Advances in Geographic Information Systems. 283–292

work page 2020
[3]

Zipei Fan, Xuan Song, Tianqi Xia, Renhe Jiang, Ryosuke Shibasaki, and Ritsu Sakuramachi. 2018. Online deep ensemble learning for predicting citywide human mobility. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 3 (2018), 1–21

2018
[4]

Ruth E Farmer, Daphne Kounali, A Sarah Walker, Jelena Savović, Alison Richards, Margaret T May, and Deborah Ford. 2018. Application of causal inference methods in the analyses of randomised controlled trials: a systematic review. Trials 19 (2018), 1–14

work page 2018
[5]

Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola. 2012. A kernel two-sample test. The Journal of Machine Learning Research 13, 1 (2012), 723–773

2012
[6]

Ge Hangli, Lifeng Lin, Renhe Jiang, Takashi Michikata, and Noboru Koshizuka

work page
[7]

Md Rakibul Hasan, Md Zakir Hossain, Tom Gedeon, and Shafin Rahman. 2024. LLM-GEm: Large language model-guided prediction of people’s empathy lev- els towards newspaper article. In Findings of the Association for Computational Linguistics: EACL 2024. 2215–2231

work page 2024
[8]

Samiul Hasan, Christian M Schneider, Satish V Ukkusuri, and Marta C González

work page

Show all 51 references

[9]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition . 770–778

2016
[10]

Guido W Imbens. 2000. The role of the propensity score in estimating dose- response functions. Biometrika 87, 3 (2000), 706–710

2000
[11]

Jinhao Jiang, Kun Zhou, Zican Dong, Keming Ye, Wayne Xin Zhao, and Ji-Rong Wen. 2023. Structgpt: A general framework for large language model to reason over structured data. arXiv preprint arXiv:2305.09645 (2023)

2023 arXiv
[12]

Renhe Jiang, Quanjun Chen, Zekun Cai, Zipei Fan, Xuan Song, Kota Tsubouchi, and Ryosuke Shibasaki. 2022. Will you go where you search? A deep learning framework for estimating user search-and-go behavior. Neurocomputing 472 (2022), 338–348

2022
[13]

Renhe Jiang, Xuan Song, Dou Huang, Xiaoya Song, Tianqi Xia, Zekun Cai, Zhao- nan Wang, Kyoung-Sook Kim, and Ryosuke Shibasaki. 2019. Deepurbanevent: A system for predicting citywide crowd dynamics at big events. In Proceedings of the 25th ACM SIGKDD international conference on...

2019
[14]

Renhe Jiang, Zhaonan Wang, Yudong Tao, Chuang Yang, Xuan Song, Ryosuke Shibasaki, Shu-Ching Chen, and Mei-Ling Shyu. 2023. Learning social meta- knowledge for nowcasting human mobility in disaster. In Proceedings of the ACM Web Conference 2023. 2655–2665

2023
[15]

Hanlei Jin, Yang Zhang, Dan Meng, Jun Wang, and Jinghua Tan. 2024. A compre- hensive survey on process-oriented automatic text summarization with explo- ration of llm-based methods. arXiv preprint arXiv:2403.02901 (2024)

2024
[16]

Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)

2016 arXiv
[17]

Shuqi Li, Yuebo Sun, Yuxin Lin, Xin Gao, Shuo Shang, and Rui Yan. 2024. Causal- Stock: Deep End-to-end Causal Discovery for News-driven Stock Movement Prediction. arXiv preprint arXiv:2411.06391 (2024)

2024 arXiv
[18]

Yuan Li, Yixuan Zhang, and Lichao Sun. 2023. Metaagents: Simulating interactions of human behaviors for llm-based task-oriented coordination via collaborative generative agents. arXiv preprint arXiv:2310.06500 (2023)

2023 arXiv
[19]

Ziqian Lin, Jie Feng, Ziyang Lu, Yong Li, and Depeng Jin. 2019. Deepstn+: Context- aware spatial-temporal neural network for crowd flow prediction in metropolis. In Proceedings of the AAAI conference on artificial intelligence , Vol. 33. 1020–1027

2019
[20]

Jing Ma, Yushun Dong, Zheng Huang, Daniel Mietchen, and Jundong Li. 2022. Assessing the causal impact of COVID-19 related policies on outbreak dynamics: A case study in the US. InProceedings of the ACM Web Conference 2022. 2678–2686

2022
[21]

Stephen L Morgan and Christopher Winship. 2015. Counterfactuals and causal inference. Cambridge University Press

2015
[22]

ollama. 2024. ollama. https://github.com/ollama/ollama

2024
[23]

Judea Pearl. 2009. Causal inference in statistics: An overview. (2009)

2009
[24]

Xie Runfeng, Cui Xiangyang, Yan Zhou, Wang Xin, Xuan Zhanwei, Zhang Kai, et al. 2023. Lkpnr: Llm and kg for personalized news recommendation framework. arXiv preprint arXiv:2308.12028 (2023)

2023 arXiv
[25]

Patrick Schwab, Lorenz Linhardt, Stefan Bauer, Joachim M Buhmann, and Walter Karlen. 2020. Learning counterfactual representations for estimating individ- ual dose-response curves. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 5612–5619

2020
[26]

Chao Shang, Jie Chen, and Jinbo Bi. 2021. Discrete graph structure learning for forecasting multiple time series. arXiv preprint arXiv:2101.06861 (2021)

2021 arXiv
[27]

Zezhi Shao, Zhao Zhang, Fei Wang, Wei Wei, and Yongjun Xu. 2022. Spatial- temporal identity: A simple yet effective baseline for multivariate time series forecasting. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 4454–4458

2022
[28]

Ritika Singh and Satwinder Singh. 2021. Text similarity measures in news articles by vector space model using NLP. Journal of The Institution of Engineers (India): Series B 102 (2021), 329–338

2021
[29]

Chaoming Song, Zehui Qu, Nicholas Blumm, and Albert-László Barabási. 2010. Limits of predictability in human mobility. Science 327, 5968 (2010), 1018–1021

2010
[30]

Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Ryosuke Shibasaki, Nicholas Jing Yuan, and Xing Xie. 2016. Prediction and simulation of human mobility following natural disasters. ACM Transactions on Intelligent Systems and Technology (TIST) 8, 2 (2016), 1–23

2016
[31]

Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)

2023 arXiv
[32]

Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research 9, 11 (2008)

2008
[33]

Aishwarya Vijayan. 2023. A prompt engineering approach for structured data extraction from unstructured text using conversational LLMs. In Proceedings of the 2023 6th International Conference on Algorithms, Computing and Artificial Intelligence. 183–189

2023
[34]

Xinglei Wang, Meng Fang, Zichao Zeng, and Tao Cheng. 2023. Where would i go next? large language models as human mobility predictors. arXiv preprint arXiv:2308.15197 (2023)

2023 arXiv
[35]

Xinlei Wang, Maike Feng, Jing Qiu, Jinjin Gu, and Junhua Zhao. 2024. From news to forecast: Integrating event analysis in llm-based time series forecasting with reflection. arXiv preprint arXiv:2409.17515 (2024)

2024 arXiv
[36]

Xin Wang, Shengfei Lyu, Xingyu Wu, Tianhao Wu, and Huanhuan Chen. 2022. Generalization bounds for estimating causal effects of continuous treatments. Advances in Neural Information Processing Systems 35 (2022), 8605–8617

2022
[37]

Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia, and Jian Yu. 2020. Traffic flow prediction via spatial temporal graph neural network. In Proceedings of the web conference 2020 . 1082–1092

2020
[38]

Yingzi Wang, Nicholas Jing Yuan, Defu Lian, Linli Xu, Xing Xie, Enhong Chen, and Yong Rui. 2015. Regularity and conformity: Location prediction using het- erogeneous mobility data. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data m...

2015
[39]

Zhaonan Wang, Renhe Jiang, Hao Xue, Flora D Salim, Xuan Song, and Ryosuke Shibasaki. 2022. Event-aware multimodal mobility nowcasting. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 36. 4228–4236

2022
[40]

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems 35 (2022), 24824–24837

2022
[41]

Wikipedia contributors. 2023. Kyodo News — Wikipedia, The Free Ency- clopedia. https://en.wikipedia.org/w/index.php?title=Kyodo_News&oldid= 1187452593 [Online; accessed 8-July-2024]

2023
[42]

Tong Xia, Junjie Lin, Yong Li, Jie Feng, Pan Hui, Funing Sun, Diansheng Guo, and Depeng Jin. 2021. 3dgcn: 3-dimensional dynamic graph convolutional network for citywide crowd flow prediction. ACM Transactions on Knowledge Discovery from Data (TKDD) 15, 6 (2021), 1–21

2021
[43]

Liuyi Yao, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, and Aidong Zhang. 2021. A survey on causal inference. ACM Transactions on Knowledge Discovery from Data (TKDD) 15, 5 (2021), 1–46

2021
[44]

Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph con- volutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)

2017 arXiv
[45]

Junbo Zhang, Yu Zheng, and Dekang Qi. 2017. Deep spatio-temporal residual net- works for citywide crowd flows prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 31

2017
[46]

Zijian Zhang, Xiangyu Zhao, Qidong Liu, Chunxu Zhang, Qian Ma, Wanyu Wang, Hongwei Zhao, Yiqi Wang, and Zitao Liu. 2023. Promptst: Prompt-enhanced spatio-temporal multi-attribute prediction. In Proceedings of the 32nd ACM Inter- national Conference on Information and Knowledge...

2023
[47]

Kai Zhao, Sasu Tarkoma, Siyuan Liu, and Huy Vo. 2016. Urban human mobility data mining: An overview. In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 1911–1920

2016
[48]

Zhang Zhiwen, Hongjun Wang, Zipei Fan, Ryosuke Shibasaki, and Xuan Song
[2013]

Journal of Statistical Physics 151 (2013), 304–318

Spatiotemporal patterns of urban human mobility. Journal of Statistical Physics 151 (2013), 304–318

2013
[2022]

In 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)

Multi-Weighted Graphs Learning for Passenger Count Prediction on Rail- way Network. In 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 374–382

2022
[2023]

Sumida River Fireworks Festival

Assessing the Continuous Causal Responses of Typhoon-related Weather on Human Mobility: An Empirical Study in Japan. InProceedings of the 32nd ACM International Conference on Information and Knowledge Management . 3524–3533. CausalMob: Causal Human Mobility Prediction with LLM...

2025

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [1]

Blogwatcher. 2024. Blogwatcher, Inc. https://www.blogwatcher.co.jp/ (Japanese Only)

work page 2024

[2] [2]

Quanjun Chen, Renhe Jiang, Chuang Yang, Zekun Cai, Zipei Fan, Kota Tsubouchi, Ryosuke Shibasaki, and Xuan Song. 2020. Dualsin: Dual sequential interaction network for human intentional mobility prediction. In Proceedings of the 28th International Conference on Advances in Geographic Information Systems. 283–292

work page 2020

[3] [3]

Zipei Fan, Xuan Song, Tianqi Xia, Renhe Jiang, Ryosuke Shibasaki, and Ritsu Sakuramachi. 2018. Online deep ensemble learning for predicting citywide human mobility. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 3 (2018), 1–21

2018

[4] [4]

Ruth E Farmer, Daphne Kounali, A Sarah Walker, Jelena Savović, Alison Richards, Margaret T May, and Deborah Ford. 2018. Application of causal inference methods in the analyses of randomised controlled trials: a systematic review. Trials 19 (2018), 1–14

work page 2018

[5] [5]

Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola. 2012. A kernel two-sample test. The Journal of Machine Learning Research 13, 1 (2012), 723–773

2012

[6] [6]

Ge Hangli, Lifeng Lin, Renhe Jiang, Takashi Michikata, and Noboru Koshizuka

work page

[7] [7]

Md Rakibul Hasan, Md Zakir Hossain, Tom Gedeon, and Shafin Rahman. 2024. LLM-GEm: Large language model-guided prediction of people’s empathy lev- els towards newspaper article. In Findings of the Association for Computational Linguistics: EACL 2024. 2215–2231

work page 2024

[8] [8]

Samiul Hasan, Christian M Schneider, Satish V Ukkusuri, and Marta C González

work page