SeoulMMOD is a new large‑scale public benchmark that provides three years of hourly origin‑destination flow data for six urban travel modes across 25 districts and 426 sub‑districts in Seoul, along with travel‑time, travel‑distance, calendar, rainfall, and point‑of‑interest signals. The dataset enables city‑wide multimodal mobility analysis and supports the evaluation of 14 forecasting baselines under a common protocol. The benchmark reveals that models effective at the district level may not scale to sub‑district forecasting and that naive joint multi‑mode training does not always improve accuracy.
By Taeyoung Yu, Seonbin Jo, Jiwon Kim, Junyoung Byun
AsyncCouple-Flow introduces a new framework for multi‑modal spatio‑temporal forecasting that tackles three key challenges: differing sampling rates, missing modalities, and autoregressive error accumulation. It employs a Modality‑Aware Token Sparsification module to produce equal‑length sequences, an Asynchronous Cross‑Modal Coupling Graph to fuse data under arbitrary asynchrony and missingness, and a Flow‑Matching Forecasting Head that models multi‑step prediction as a conditional ODE. Experiments on weather and traffic datasets demonstrate that the method outperforms state‑of‑the‑art baselines and remains robust even when up to two modalities are missing.
By Zhixiang Wu, Yining Liu, Bo Zhao, Szu-Yu Chen, Huiran Duan, Chu Lin, Chuanguang Yang
arXiv:2608. 09255v1 Announce Type: new Abstract: Residential energy estimates are often needed before detailed envelope characteristics, equipment efficiencies, infiltration, sensor, or billing data are available.
By Aditya Ramnarayan, Fatih Evren, Patti Gunderson, Samuel Rosenberg
arXiv:2509.09710v3 Announce Type: replace-cross
Abstract: This study introduces a Large Language Model (LLM) scheme for generating key attributes of travel diaries in agent-based transportation model...
By Sepehr Golrokh Amin, Devin Rhoads, Fatemeh Fakhrmoosavi, Nicholas E. Lownes, John N. Ivan
MEOX is a compact multimodal masked autoencoder designed for Earth Observation that uses a 2.939 million‑parameter encoder and 3.115 million total parameters. It incorporates sensor‑specific adapters, explicit validity signals, and a shared sparse‑expert block to maintain modality‑dependent processing before a learned patch‑wise fusion, followed by fourteen encoder blocks that process a single spatial sequence with four metadata tokens. Pretrained on 1.228 million MMEarth64 samples, MEOX achieves strong performance on GEO‑Bench tasks, surpassing prior CSMoE results, and demonstrates effective sensor‑flexible representation learning with a modest parameter budget.
By Mohanad Albughdadi
BER-PEF is a Bayes‑error‑rate‑based framework that transforms BER estimation into human mobility predictability estimation, enabling comparison of different estimators even when ground truth predictability is not observable. It maps various data types—symbolic sequences, numeric trajectories, contextual features, and learned representations—into a shared feature–label space and evaluates estimator outputs along controlled perturbation curves against a common predictability reference interval. Experiments on datasets such as Foursquare NYC/TKY, GeoLife, and T‑Drive show that several BER‑based estimators outperform existing methods on symbolic sequences and numeric trajectories, and that aggregating evidence across multiple perturbation levels yields a more reliable basis for selecting estimators.
By En Xu, Jingtao Ding, Zhiwen Yu, Yong Li
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:2606. 16580v1 Announce Type: new Abstract: Top-soil organic carbon (SOC) prediction is fundamental to agricultural sustainability, land use policy and fertilization planning.
By Daniele Mos, Felipe Drummond, Anton Bossenbroek, Soufiane el Khinifri
arXiv:2603.05581v2 Announce Type: replace-cross
Abstract: Urban traffic flow is governed by the complex, nonlinear interaction between land use configuration and spatiotemporally heterogeneous mobili...
By Olaf Yunus Laitinen Imanov
The paper introduces FM4NILM, a prompt‑programmable foundation model that estimates the power usage of any requested appliance from whole‑home meter data, a natural‑language description, and optional activation examples. Trained on 645k sequences from seven public datasets, the lightweight transformer achieves competitive performance across twelve appliance requests, outperforming seven appliance‑specific baselines on key metrics such as event F1 and active‑window MAE. The model’s design allows new appliance coverage to be added via prompts and examples rather than building separate specialist networks.
By Xudong Wang, Jiacheng Cui, Junyu Xue, Tongxin Li, Guoming Tang
The study explores whether environmental data can enhance EEG-based affective-state classification by treating such data as literature-informed priors. Using a dual‑tower model that fuses EEG-Conformer representations with a graph‑based environmental encoder, the authors achieve 76.2% accuracy versus 67.4% for EEG alone on a dataset from Astana. Experiments with controls, dose‑response reversal, and domain‑shift show that the improvement is not solely due to environmental information, and replacing Astana’s environmental distribution with Singapore’s reduces accuracy to 72.8%.
By Utsav Poudel, Jagannath Aryal, Subramaniyaswamy Vairavasundaram
arXiv:2507. 00945v2 Announce Type: replace Abstract: Short-term forecasting of aggregated human mobility flows supports urban planning, intelligent transportation systems, and emergency response, yet existing models often require substantial mobility history and learn spatial structure implicitly through grids or graphs.
By Massimiliano Luca, Ciro Beneduce, Bruno Lepri