Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A deep learning approach for detecting traffic accidents from social media data
2017246 citationsZhenhua Zhang, Qing He et al.Transportation Research Part C Emerging Technologiesprofile →
Recent applications of big data analytics in railway transportation systems: A survey
2018215 citationsFaeze Ghofrani, Qing He et al.Transportation Research Part C Emerging Technologiesprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Qing He'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 Qing He with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qing He more than expected).
This network shows the impact of papers produced by Qing He. 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 Qing He. The network helps show where Qing He may publish in the future.
Co-authorship network of co-authors of Qing He
This figure shows the co-authorship network connecting the top 25 collaborators of Qing He.
A scholar is included among the top collaborators of Qing He based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Qing He. Qing He is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Mohammadi, Reza Karami, et al.. (2019). Exploring the Relationship between Foot-by-Foot Track Geometry and Rail Defects: a Data-Driven Approach. Transportation Research Board 98th Annual MeetingTransportation Research Board.1 indexed citations
Khani, Alireza, et al.. (2018). A Probabilistic Trip Chaining Algorithm for Transit Origin–Destination Matrix Estimation Using Automated Data. Transportation Research Board 97th Annual MeetingTransportation Research Board.1 indexed citations
14.
He, Qing, et al.. (2017). Multi-modal Hierarchically Responsive Signal Control with a Lexicographical Dynamic Programming Approach. Transportation Research Board 96th Annual MeetingTransportation Research Board.1 indexed citations
15.
Zhang, Zhenhua, Qing He, & Shanjiang Zhu. (2017). Exploring Travel Behavior with Social Media: An Empirical Study of Abnormal Movements Using High-Resolution Tweet Trajectory Data. Transportation Research Board 96th Annual MeetingTransportation Research Board.3 indexed citations
He, Qing, et al.. (2013). A novel expert system of fault diagnosis based on vibration for rotating machinery. SHILAP Revista de lepidopterología.2 indexed citations
18.
Ding, Jun, et al.. (2013). Development and Testing of Priority Control System in Connected Vehicle Environment. Transportation Research Board 92nd Annual MeetingTransportation Research Board.9 indexed citations
19.
He, Qing, et al.. (2013). Railway Track Geometry Defect Modeling: Deterioration, Derailment Risk, and Optimal Repair. Transportation Research Board 92nd Annual MeetingTransportation Research Board.3 indexed citations
20.
He, Qing & Larry Head. (2010). Lane Level Vehicle Positioning with Low Cost GPS. Transportation Research Board 89th Annual MeetingTransportation Research Board.4 indexed citations
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.