Yu Zheng
Impact in
- Transportation top 0.01%
- Human Mobility and Location-Based Analysis
- Transportation Planning and Optimization
- Urban Transport and Accessibility
- Signal Processing top 0.01%
- Data Management and Algorithms
- Time Series Analysis and Forecasting
Papers in
- Transportation 123
- Human Mobility and Location-Based Analysis 111
- Transportation Planning and Optimization 30
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- Data Management and Algorithms 100
- Time Series Analysis and Forecasting 20
- Co-authors
- Xing Xie (38 shared papers)Junbo Zhang (37 shared papers)Wei-Ying Ma (13 shared papers)Dekang Qi (4 shared papers)Guangzhong Sun (6 shared papers)Lizhu Zhang (3 shared papers)Xiuwen Yi (13 shared papers)Yukun Chen (6 shared papers)
- Journals
- IEEE Transactions on Knowledge and Data Engineering (29 papers)IEEE Transactions on Big Data (9 papers)IEEE Transactions on Visualization and Computer Graphics (6 papers)ACM Transactions on Intelligent Systems and Technology (5 papers)IEEE Transactions on Intelligent Transportation Systems (4 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Yu Zheng
237 papers receiving 29.0k citations
Yu Zheng's Hit Papers
Peers
Comparison fields: 5 of 167
- Transportation 17.2k
- Signal Processing 9.6k
- Building and Construction 10.6k
- Geography, Planning and Development 2.3k
- Computational Mathematics 212
Countries citing papers authored by Yu Zheng
This map shows the geographic impact of Yu Zheng's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yu Zheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yu Zheng more than expected).
Fields of papers citing papers by Yu Zheng
This network shows the impact of papers produced by Yu Zheng. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yu Zheng. The network helps show where Yu Zheng may publish in the future.
Co-authors
The 25 scholars most cited alongside Yu Zheng, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 250 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction Hit paper breakdown → | 2017 | 1699 |
| 2 | Mining interesting locations and travel sequences from GPS trajectories Hit paper breakdown → | 2009 | 1540 |
| 3 | Trajectory Data Mining Hit paper breakdown → | 2015 | 1308 |
| 4 | Urban Computing Hit paper breakdown → | 2014 | 1085 |
| 5 | Discovering regions of different functions in a city using human mobility and POIs Hit paper breakdown → | 2012 | 928 |
| 6 | T-drive Hit paper breakdown → | 2010 | 897 |
| 7 | GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory. Hit paper breakdown → | 2010 | 894 |
| 8 | Understanding mobility based on GPS data Hit paper breakdown → | 2008 | 888 |
| 9 | U-Air Hit paper breakdown → | 2013 | 761 |
| 10 | Driving with knowledge from the physical world Hit paper breakdown → | 2011 | 679 |
| 11 | Map-matching for low-sampling-rate GPS trajectories Hit paper breakdown → | 2009 | 652 |
| 12 | DNN-based prediction model for spatio-temporal data Hit paper breakdown → | 2016 | 540 |
| 13 | Mining user similarity based on location history Hit paper breakdown → | 2008 | 532 |
| 14 | Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning Hit paper breakdown → | 2019 | 451 |
| 15 | Urban computing with taxicabs Hit paper breakdown → | 2011 | 441 |
| 16 | Predicting citywide crowd flows using deep spatio-temporal residual networks Hit paper breakdown → | 2018 | 431 |
| 17 | Travel time estimation of a path using sparse trajectories Hit paper breakdown → | 2014 | 426 |
| 18 | GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction Hit paper breakdown → | 2018 | 413 |
| 19 | Recommending friends and locations based on individual location history Hit paper breakdown → | 2011 | 392 |
| 20 | Understanding transportation modes based on GPS data for web applications Hit paper breakdown → | 2010 | 383 |
About Yu Zheng
Yu Zheng is a scholar working on Transportation, Signal Processing, Building and Construction, Geography, Planning and Development and Automotive Engineering, having authored 250 papers that have together received 29.8k indexed citations. Recurring topics across this work include Human Mobility and Location-Based Analysis (111 papers), Data Management and Algorithms (100 papers), Traffic Prediction and Management Techniques (75 papers), Transportation Planning and Optimization (30 papers), Geographic Information Systems Studies (29 papers), Anomaly Detection Techniques and Applications (23 papers), Transportation and Mobility Innovations (20 papers) and Time Series Analysis and Forecasting (20 papers). The work is most often cited by research in Transportation (17.2k citations), Signal Processing (9.6k citations), Building and Construction (10.6k citations), Geography, Planning and Development (2.3k citations) and Computational Mathematics (212 citations). Yu Zheng has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Xing Xie, Junbo Zhang, Wei-Ying Ma, Dekang Qi, Guangzhong Sun, Lizhu Zhang, Xiuwen Yi, Yukun Chen, Jing Yun Yuan and Jing Yuan. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Big Data, IEEE Transactions on Visualization and Computer Graphics, ACM Transactions on Intelligent Systems and Technology and IEEE Transactions on Intelligent Transportation Systems.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.