4.7 Article

Cross-City Multi-Granular Adaptive Transfer Learning for Traffic Flow Prediction

Related references

Note: Only part of the references are listed.
Article Engineering, Civil

Spatio-Temporal Knowledge Transfer for Urban Crowd Flow Prediction via Deep Attentive Adaptation Networks

Senzhang Wang et al.

Summary: This paper proposes a Deep Attentive Adaptation Network model named ST-DAAN for transferring cross-domain Spatio-Temporal knowledge for urban crowd flow prediction. The model maps raw spatio-temporal data to a common embedding space, adopts domain adaptation, and incorporates a global attention mechanism to capture spatial dependencies. Experimental results demonstrate that ST-DAAN outperforms state-of-the-art methods significantly.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

Article Computer Science, Artificial Intelligence

Spatio-Temporal Meta Learning for Urban Traffic Prediction

Zheyi Pan et al.

Summary: This paper proposes a deep meta learning model, ST-MetaNet(+), for predicting urban traffic. The model captures complex spatio-temporal correlations using an encoder and a decoder, and generates weights using embeddings of geo-graph attributes and traffic context. Experimental results demonstrate the effectiveness of ST-MetaNet(+) in surpassing other methods.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2022)

Article Computer Science, Information Systems

Urban Perception: Sensing Cities via a Deep Interactive Multi-task Learning Framework

Weili Guan et al.

Summary: Visual perceptions of urban attributes have a significant impact on residents' behavior and quality of life, but quantifying these perceptions is challenging due to subjectivity, regional differences, and shared information. The DRAMA scheme, a deep interactive multi-task learning approach, effectively quantifies urban attribute perceptions by integrating pairwise comparisons, regional interactions, and attribute correlations. Through extensive experiments, the DRAMA scheme outperforms state-of-the-art baselines and provides a method to rank cities based on their urban attributes.

ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Transfer Learning in Traffic Prediction with Graph Neural Networks

Yunjie Huang et al.

Summary: Statistics on urban traffic speed flows are crucial for city planning, but small cities often lack data collection methods for building data-driven models. The TEEPEE method utilizes graph neural networks for transfer learning, assisting in predicting traffic speed in areas with limited data availability.

2021 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC) (2021)

Article Computer Science, Artificial Intelligence

Multi-Level Attention Networks for Multi-Step Citywide Passenger Demands Prediction

Xian Zhou et al.

Summary: This paper proposes an end-to-end deep neural network model for predicting citywide passenger demands, capturing spatiotemporal influence and temporal dependencies with a multi-level attention model, achieving higher accuracy than state-of-the-art approaches.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2021)

Article Automation & Control Systems

Optimized Configuration of Exponential Smoothing and Extreme Learning Machine for Traffic Flow Forecasting

Hao-Fan Yang et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)

Proceedings Paper Computer Science, Theory & Methods

Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction

Huaxiu Yao et al.

WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019) (2019)

Article Computer Science, Artificial Intelligence

Road Traffic Speed Prediction: A Probabilistic Model Fusing Multi-Source Data

Lu Lin et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2018)

Article Automation & Control Systems

Research on Traffic Flow Prediction in the Big Data Environment Based on the Improved RBF Neural Network

Dawei Chen

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2017)

Article Computer Science, Artificial Intelligence

A Unified Framework for Metric Transfer Learning

Yonghui Xu et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2017)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Article Computer Science, Information Systems

Prediction of urban human mobility using large-scale taxi traces and its applications

Xiaolong Li et al.

FRONTIERS OF COMPUTER SCIENCE (2012)

Article Computer Science, Artificial Intelligence

A Survey on Transfer Learning

Sinno Jialin Pan et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2010)

Article Engineering, Civil

Adaptive seasonal time series models for forecasting short-term traffic flow

Shashank Shekhar et al.

TRANSPORTATION RESEARCH RECORD (2007)

Article Engineering, Civil

Reliable real-time framework for short-term freeway travel time prediction

J. W. C. van Lint

JOURNAL OF TRANSPORTATION ENGINEERING (2006)

Article Engineering, Civil

Travel-time prediction with support vector regression

CH Wu et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2004)