Product
Help
Pricing
About
Jobs
Log in
Get started
Author
Add to collection
Chuanhao Dong
Institution:
Qingdao University
Observed institution:
Qingdao University
ORCID:
0000-0002-7791-0171
Observed ORCID:
0000-0002-7791-0171
OpenAlex ID:
A5085317558
Deep learning in the COVID-19 epidemic: A deep model for urban traffic revitalization index
2021
·
Zhiqiang Lv
, Jianbo Li
, et al.
·
Data & Knowledge Engineering
·
79
DeepSTF: A Deep Spatial–Temporal Forecast Model of Taxi Flow
2021
·
Zhiqiang Lv
, Jianbo Li
, et al.
·
The Computer Journal
·
53
Multi-attribute Graph Convolution Network for Regional Traffic Flow Prediction
2022
·
Yue Wang
, Aite Zhao
, et al.
·
Neural Processing Letters
·
20
A classification method for urban functional regions based on the transfer rate of empty cars
2021
·
Zhihao Xu
, Jianbo Li
, et al.
·
IET Intelligent Transport Systems
·
18
PDF
DeepPTP: A Deep Pedestrian Trajectory Prediction Model for Traffic Intersection
2021
·
Zhiqiang Lv
, Jianbo Li
, et al.
·
KSII Transactions on Internet and Information Systems
·
17
PDF
A Deep Spatial-Temporal Network for Vehicle Trajectory Prediction
2020
·
Zhiqiang Lv
, Jianbo Li
, et al.
·
Lecture notes in computer science
·
12
Traffic Flow Prediction Model Based on Spatio-Temporal Dilated Graph Convolution
2020
·
Xiufang Sun
, Jianbo Li
, et al.
·
KSII Transactions on Internet and Information Systems
·
7
PDF
A Forecasting Method of Dual Traffic Condition Indicators Based on Ensemble Learning
2021
·
Chuanhao Dong
, Zhiqiang Lv
, et al.
·
IEEE International Conference on Parallel and Distributed Systems (ICPADS)
·
2
Spatial and Dual-channel Temporal Convolution Networks: A Novel Method for Speed Forecasting
2021
·
Chuanhao Dong
, Jianbo Li
, et al.
·
ACM Turing Award Celebration Conference - China (ACM TURC)
·
1
Multi-step Focused Graph Convolution Network: Traffic Speed Prediction
2023
·
Yinong Li
, Shizhen Fan
, et al.
·
Lecture notes in electrical engineering
Show more works
View as search
Works count:
10
Citation count:
209
H-index:
7
I10-index:
6
Year
Topic
Type