arXiv Machine Learning

DSETA: A Dual-Stage Continual Learning Framework for Travel Time Prediction in Dynamic Traffic Environments

arXiv:2608. 00402v1 Announce Type: new Abstract: Estimated Time of Arrival (ETA) prediction is a core component of intelligent transportation systems.

arXiv AI

3d ago

UniST-Pred: A Robust Unified Framework for Spatio-Temporal Traffic Forecasting in Transportation Networks Under Disruptions

arXiv:2602.14049v2 Announce Type: replace-cross Abstract: Spatio-temporal traffic forecasting is a core component of intelligent transportation systems, supporting various downstream tasks such as si...

By Yue Wang, Areg Karapetyan, Djellel Difallah, Samer Madanat

agents benchmarks

arXiv Machine Learning

Sep 15

A Multi-Resolution Multi-Domain Pre-Training Framework for Universal Traffic Forecasting

arXiv:2609.13878v1 Announce Type: new Abstract: Spatio-temporal traffic data are central to intelligent transportation systems, yet their heterogeneity poses significant challenges for large-scale mo...

By Zhouyang Liu, Jindong Han, Hao Wang, Xinyue Liu, Hui Gao, Dongsheng Li, Hao Liu

diffusion benchmarks

arXiv AI

Jul 7

Unveiling Stochasticity: Universal Multi-modal Probabilistic Modeling for Traffic Forecasting

arXiv:2604. 16084v2 Announce Type: replace-cross Abstract: Traffic forecasting is a challenging spatio-temporal modeling task and a critical component of urban transportation management.

By Weijiang Xiong, Robert Fonod, Nikolas Geroliminis

multimodal data-engineering

arXiv Machine Learning

Jun 25

Cellular Predictions on the Move: What about Data?

arXiv:2606. 25709v1 Announce Type: new Abstract: Mobile cellular load forecasting is native to network resource optimization and delivery of services with reliability, latency and quality guarantees.

By Natalia Vesselinova, Pauliina Ilmonen

arXiv AI

Aug 26

msData: A Millisecond-Resolution Network Dataset for Advancing Time Series Foundation Models

arXiv:2603.16497v3 Announce Type: replace-cross Abstract: Time series foundation models (TSFMs) require diverse, real-world datasets to adapt across varying domains and temporal frequencies. However,...

By Subina Khanal, Seshu Tirupathi, Merim Dzaferagic, Marco Ruffini, Torben Bach Pedersen

llms fine-tuning benchmarks

arXiv Machine Learning

Jun 25

Towards Continuous Power Forecasting: Practical Continual Learning for Real-World Energy Systems in Nonstationary Time Series

arXiv:2606. 24955v1 Announce Type: new Abstract: Power forecasting models deployed in real-world energy markets must operate under nonstationary conditions, where data distributions continually evolve due to weather variability, infrastructure upgrades, and changing consumption behaviors.

By Yujiang He, Frederic Uhrweiller, Bernhard Sick

arXiv AI

Jul 29

A2TTA: Anchored-and-Agile Test-Time Adaptation for Evolving Traffic Sensor Networks

arXiv:2607. 25875v1 Announce Type: cross Abstract: Traffic forecasting is important for efficient traffic management and route planning in smart cities.

By Du Yin, Xiachong Lin, Yue Tan, Jinliang Deng, Estrid He, Hao Xue, Flora D. Salim

arXiv AI

Aug 24

RiskTraf: Risk-Extrapolated Residual Learning for Multi-Variate Traffic Flow Prediction

RiskTraf introduces a risk-extrapolated residual learning approach for multi-variate traffic flow prediction, leveraging raw flow, speed, and occupancy data from the new PEMSB-3V benchmark. The method freezes a trained spatio-temporal backbone and adds a lightweight residual head that learns from historical speed and occupancy to correct flow predictions across different traffic regimes. Experiments show consistent improvements over various backbones and outperform existing debiasing and distribution-shift adaptation techniques.

By Guangyu Wang, Zhidan Liu

benchmarks

arXiv AI

Jul 28

AutoFed: Personalized Federated Traffic Prediction via Adaptive Prompt

arXiv:2512. 24625v3 Announce Type: replace-cross Abstract: Accurate traffic prediction is essential for Intelligent Transportation Systems, including ride-hailing, urban road planning, and vehicle fleet management.

By Zijian Zhao, Yitong Shang, Sen Li

safety

arXiv AI

Jun 8

Proxy Reconstruction Pre-training for Ramp Flow Prediction at Highway Interchanges

arXiv:2510. 03381v3 Announce Type: replace-cross Abstract: Interchanges are crucial nodes for vehicle transfers between highways, yet the lack of real-time ramp detectors creates blind spots in traffic prediction.

By Yongchao Li, Jun Chen, Zhuoxuan Li, Chao Gao, Yang Li, Chu Zhang, Changyin Dong

multimodal benchmarks

arXiv AI

Aug 3

SqLinear: Balanced Square Partitioning Makes Linear Interaction Sufficient for Large-Scale Traffic Forecasting

arXiv:2606. 21072v2 Announce Type: replace-cross Abstract: Traffic prediction is a core task in intelligent transportation systems and urban-scale decision making.

By Yongfeng Su, Hongwen Li, Zijian Zhang, Ziquan Fang, Lu Chen, Christian S. Jensen, Hong Gao, Yinjun Han

llms

arXiv Machine Learning

Jul 24

Climate-resilient electric vehicle charging infrastructure for sustainable cities: An interpretable causal-ensemble framework for preventive maintenance and low-carbon mobility

arXiv:2607. 21444v1 Announce Type: cross Abstract: Reliable electric vehicle (EV) charging infrastructure is a cornerstone of sustainable, low-carbon cities, yet urban climate stress such as extreme heat, heavy precipitation, and humidity increasingly raises equipment fault risk and undermines the resilience of urban energy and mobility services.

By Cande Lian (School of Management, Foshan University, Foshan, China), Wentao Zeng (School of Management, Foshan University, Foshan, China), Jiabin Wu (School of Management, Foshan University, Foshan, China), Yiming Bie (School of Transportation, Jilin University, Changchun, China), Wei Zhou (Department of Civil and Environmental Engineering, National University of Singapore)

safety

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