相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction
Ahmad Ali et al.
NEURAL NETWORKS (2022)
Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network
Jintao Ke et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2021)
Transferability improvement in short-term traffic prediction using stacked LSTM network
Junyi Li et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2021)
Road network generalization considering traffic flow patterns
Wenhao Yu et al.
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2020)
Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast
Tao Ma et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2020)
Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting
Hao Peng et al.
INFORMATION SCIENCES (2020)
Urban ride-hailing demand prediction with multiple spatiotemporal information fusion network
Guangyin Jin et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2020)
Network based definition of functional regions: A graph theory approach for spatial distribution of traffic flows
Pavel Klapka et al.
JOURNAL OF TRANSPORT GEOGRAPHY (2020)
Discovering Frequent Movement Paths From Taxi Trajectory Data Using Spatially Embedded Networks and Association Rules
Wenhao Yu
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2019)
Traffic speed prediction for urban transportation network: A path based deep learning approach
Jiawei Wang et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2019)
Multistep speed prediction on traffic networks: A deep learning approach considering spatio-temporal dependencies
Zhengchao Zhang et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2019)
A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources
Shuguan Yang et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2019)
UrbanFM: Inferring Fine-Grained Urban Flows
Yuxuan Liang et al.
KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING (2019)
A hybrid deep learning based traffic flow prediction method and its understanding
Yuankai Wu et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2018)
A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data
Abien Fred M. Agarap
PROCEEDINGS OF 2018 10TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND COMPUTING (ICMLC 2018) (2018)
Large-Scale Learnable Graph Convolutional Networks
Hongyang Gao et al.
KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING (2018)
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky et al.
COMMUNICATIONS OF THE ACM (2017)
Prediction of functional ARMA processes with an application to traffic data
J. Klepsch et al.
ECONOMETRICS AND STATISTICS (2017)
Analysis of Urban Road Traffic Network Based on Complex Network
Zhao Tian et al.
GREEN INTELLIGENT TRANSPORTATION SYSTEM AND SAFETY (2016)
Traffic Flow Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm
Yuliang Cong et al.
GREEN INTELLIGENT TRANSPORTATION SYSTEM AND SAFETY (2016)
Short-term traffic volume forecasting: A k-nearest neighbor approach enhanced by constrained linearly sewing principle component algorithm
Zuduo Zheng et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2014)
The network analysis of urban streets: A dual approach
Sergio Porta et al.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS (2006)
分享论文
