A parallel spatiotemporal deep learning network for highway traffic flow forecasting
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A parallel spatiotemporal deep learning network for highway traffic flow forecasting
Authors
Keywords
-
Journal
International Journal of Distributed Sensor Networks
Volume 15, Issue 2, Pages 155014771983279
Publisher
SAGE Publications
Online
2019-02-27
DOI
10.1177/1550147719832792
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- A hybrid deep learning based traffic flow prediction method and its understanding
- (2018) Yuankai Wu et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach
- (2017) Hao-Fan Yang et al. IEEE Transactions on Neural Networks and Learning Systems
- High-Order Gaussian Process Dynamical Models for Traffic Flow Prediction
- (2016) Jing Zhao et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- A Two-Stage Algorithm for Origin-Destination Matrices Estimation Considering Dynamic Dispersion Parameter for Route Choice
- (2016) Yong Wang et al. PLoS One
- Spatiotemporal Context Awareness for Urban Traffic Modeling and Prediction: Sparse Representation Based Variable Selection
- (2015) Su Yang et al. PLoS One
- Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning
- (2014) Wenhao Huang et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Short-Term Traffic State Prediction Based on Temporal–Spatial Correlation
- (2013) T. L. Pan et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION 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
- Supervised Weighting-Online Learning Algorithm for Short-Term Traffic Flow Prediction
- (2013) Young-Seon Jeong et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Representation Learning: A Review and New Perspectives
- (2013) Y. Bengio et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Efficient missing data imputing for traffic flow by considering temporal and spatial dependence
- (2013) Li Li et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Real-time road traffic forecasting using regime-switching space-time models and adaptive LASSO
- (2012) Yiannis Kamarianakis et al. APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY
- 3D Convolutional Neural Networks for Human Action Recognition
- (2012) Shuiwang Ji et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Localized Extended Kalman Filter for Scalable Real-Time Traffic State Estimation
- (2011) Chris P. I. J. van Hinsbergen et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Real-Time Traffic Flow Forecasting Using Spectral Analysis
- (2011) Tigran T. Tchrakian et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Wavelet-Based Denoising for Traffic Volume Time Series Forecasting with Self-Organizing Neural Networks
- (2010) Daniel Boto-Giralda et al. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started