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.
Human-like autonomous car-following model with deep reinforcement learning
2018390 citationsMeixin Zhu, Xuesong Wang et al.Transportation Research Part C Emerging Technologiesprofile →
Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving
2020327 citationsMeixin Zhu, Yinhai Wang et al.Transportation Research Part C Emerging Technologiesprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Xuesong Wang'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 Xuesong Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xuesong Wang more than expected).
This network shows the impact of papers produced by Xuesong Wang. 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 Xuesong Wang. The network helps show where Xuesong Wang may publish in the future.
Co-authorship network of co-authors of Xuesong Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Xuesong Wang.
A scholar is included among the top collaborators of Xuesong Wang 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 Xuesong Wang. Xuesong Wang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Wang, Xuesong, et al.. (2019). Application of Driving Simulator for Freeway Design Safety Evaluation: A Sample Size Study. Transportation Research Board 98th Annual MeetingTransportation Research Board.4 indexed citations
11.
Wang, Xuesong, et al.. (2019). Comparison of Calibration Methods for Improving the Transferability of Safety Performance Functions. Transportation Research Board 98th Annual MeetingTransportation Research Board.3 indexed citations
12.
Wang, Xuesong, et al.. (2019). Calibrating Car-Following Models on Surface Roads Using Shanghai Naturalistic Driving Data. Transportation Research Board 98th Annual MeetingTransportation Research Board.1 indexed citations
Zaki, Mohamed H., Tarek Sayed, & Xuesong Wang. (2015). Automatic Classification of Bike Type (Motorized Vs Non-Motorized) During Busy Traffic in the City of Shanghai. Transportation Research Board 94th Annual MeetingTransportation Research Board.2 indexed citations
15.
Deng, Bing, et al.. (2013). Traffic accidents in Shanghai: general statistics and in-depth analysis.6 indexed citations
16.
Wang, Xuesong, et al.. (2013). Safety Analysis of Suburban Arterials in Shanghai, China. Transportation Research Board 92nd Annual MeetingTransportation Research Board.1 indexed citations
17.
Guo, Feng, Xuesong Wang, & Mohamed Abdel‐Aty. (2009). Corridor-Level Signalized Intersection Safety Analysis Using Bayesian Spatial Models. Transportation Research Board 88th Annual MeetingTransportation Research Board.5 indexed citations
18.
Abdel‐Aty, Mohamed, et al.. (2009). Identifying Intersection-Related Traffic Crashes for Accurate Safety Representation. ITE journal. 79(12). 38–44.6 indexed citations
19.
Yan, Xuedong, Mohamed Abdel‐Aty, Essam Radwan, & Xuesong Wang. (2008). Assessing Rear-End Crash Risk at Signalized Intersections Based on Driving Behavior. Transportation Research Board 87th Annual MeetingTransportation Research Board.1 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.