arXiv:2604. 20288v2 Announce Type: replace Abstract: Flight diversions are rare but high-impact events in aviation, making their reliable prediction vital for both safety and operational efficiency.
By Karim Aly, Alexei Sharpanskykh, Jacco Hoekstra
arXiv:2608. 11951v1 Announce Type: cross Abstract: Extreme events in air transport, such as severe arrival delays and abnormal air times, cause cascading network disruptions with substantial operational, economic, and safety costs.
By Karim Aly, Alexei Sharpanskykh, Jacco Hoekstra
CALM is a reproducible hybrid framework that combines an optional large language model (LLM) activity planner with calibrated stochastic choice, shared network feedback, memory and habit, typed feasibility checks, and deterministic offline replay. It executes a closed traveler‑day loop and evaluates each generative module against an empirical, reproducible baseline, using the 2024 New York City Citywide Mobility Survey data. The framework demonstrates significant improvements in mode‑choice accuracy, quantifies trade‑offs through live‑LLM ablation, and supports controlled stress testing and deterministic replay of downstream simulations.
By Yezhou Cheng
arXiv:2509. 09960v2 Announce Type: replace-cross Abstract: Synthetic tabular data generation is increasingly essential in machine learning, supporting downstream applications when real-world, high-quality tabular data is insufficient.
By Mingxuan Jiang, Keyang Chen, Yongxin Wang, Yongsheng Zhao, Ziyue Dai, Yicun Liu, Zeping Li, Qiuyang Zhang, Hongyi Nie, Hongbin Zhu, Sen Liu, Guangnan Ye, Hongfeng Chai
arXiv:2609.08288v1 Announce Type: new
Abstract: Travel survey data are essential for transportation planning and travel behavior analysis, yet collecting large-scale representative samples is costly...
By Zijian Shen, Bin Zhou, Jiguang Wang, Ya Zhao, Jintao Ke
The paper introduces Nomad, a transfer-and-ground framework for generating human mobility trajectories without target-city trajectory data. It learns relative transitions from source cities using POI attributes and then grounds these transitions onto a target city’s POI map via a behavior graph and exploration–return walk. Experiments across ten cities show Nomad improves trajectory fidelity and downstream utility by roughly 15% and 3% respectively over adaptation baselines.
By Yidi Wang, Yunhe Zhang, Bangchao Deng, Dingqi Yang, Pengyang Wang