A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing
Authors
Keywords
-
Journal
MULTIMEDIA TOOLS AND APPLICATIONS
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2021-01-19
DOI
10.1007/s11042-020-10486-4
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Citywide Traffic Flow Prediction Based on Multiple Gated Spatio-temporal Convolutional Neural Networks
- (2020) Cen Chen et al. ACM Transactions on Knowledge Discovery from Data
- A spatiotemporal attention mechanism-based model for multi-step citywide passenger demand prediction
- (2019) Yirong Zhou et al. INFORMATION SCIENCES
- An energy, performance efficient resource consolidation scheme for heterogeneous cloud datacenters
- (2019) Ayaz Ali Khan et al. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
- SeSe-Net: Self-Supervised deep learning for segmentation
- (2019) Zeng Zeng et al. PATTERN RECOGNITION LETTERS
- Predicting citywide crowd flows using deep spatio-temporal residual networks
- (2018) Junbo Zhang et al. ARTIFICIAL INTELLIGENCE
- A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features
- (2018) Weihong Chen et al. Future Generation Computer Systems-The International Journal of eScience
- FB-STEP: A Fuzzy Bayesian Network based Data-Driven Framework for Spatio-temporal Prediction of Climatological Time Series Data
- (2018) Monidipa Das et al. EXPERT SYSTEMS WITH APPLICATIONS
- Managing energy, performance and cost in large scale heterogeneous datacenters using migrations
- (2018) Muhammad Zakarya et al. Future Generation Computer Systems-The International Journal of eScience
- Energy-aware dynamic resource management in elastic cloud datacenters
- (2018) Ayaz Ali Khan et al. SIMULATION MODELLING PRACTICE AND THEORY
- Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction
- (2017) Xiaolei Ma et al. SENSORS
- GPU-Accelerated Parallel Hierarchical Extreme Learning Machine on Flink for Big Data
- (2017) Cen Chen et al. IEEE Transactions on Systems Man Cybernetics-Systems
- Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning
- (2013) Marco Lippi et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Predicting Taxi–Passenger Demand Using Streaming Data
- (2013) Luis Moreira-Matias et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Supervised Weighting-Online Learning Algorithm for Short-Term Traffic Flow Prediction
- (2013) Young-Seon Jeong et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Data-Driven Intelligent Transportation Systems: A Survey
- (2011) Junping Zhang et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Predictions of Freeway Traffic Speeds and Volumes Using Vector Autoregressive Models
- (2009) Srinivasa Ravi Chandra et al. Journal of Intelligent Transportation Systems
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationFind the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
Search