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.
Dynamic passenger demand oriented metro train scheduling with energy-efficiency and waiting time minimization: Mixed-integer linear programming approaches
This map shows the geographic impact of Bin Ran'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 Bin Ran with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bin Ran more than expected).
This network shows the impact of papers produced by Bin Ran. 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 Bin Ran. The network helps show where Bin Ran may publish in the future.
Co-authorship network of co-authors of Bin Ran
This figure shows the co-authorship network connecting the top 25 collaborators of Bin Ran.
A scholar is included among the top collaborators of Bin Ran 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 Bin Ran. Bin Ran is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ding, Fan, et al.. (2019). Deep Long Short-Term Memory Network based Long-Term Vehicle Trajectory Prediction. Transportation Research Board 98th Annual MeetingTransportation Research Board.
Liu, Xiaobo, et al.. (2015). Optimizing Passenger Transfer Coordination in a Large Scale Rapid Rail Network. Transportation Research Board 94th Annual MeetingTransportation Research Board.2 indexed citations
14.
Lin, Peiqun, et al.. (2015). Cooperative Movement of Intersectional Traffic Flow in a Connected Vehicle Environment. Transportation Research Board 94th Annual MeetingTransportation Research Board.3 indexed citations
Yang, Fan, Peter J. Jin, Xia Wan, Rui Li, & Bin Ran. (2014). Dynamic Origin-Destination Travel Demand Estimation Using Location Based Social Networking Data. Transportation Research Board 93rd Annual MeetingTransportation Research Board.17 indexed citations
17.
Ren, Gang, et al.. (2013). Evacuation in Large-scale Transportation Network: A Bi-Level Control Method with Uncertain Arterial Demand. Transportation Research Board 92nd Annual MeetingTransportation Research Board.5 indexed citations
18.
Ran, Bin, Chanyoung Lee, & Qin Xiao. (2006). Analysis of Winter Maintenance Logs Using Regression Tree Algorithm. Transportation Research Board 85th Annual MeetingTransportation Research Board.2 indexed citations
19.
Ran, Bin, et al.. (1996). DRIVER INTELLIGENCE REPLACEMENT IN A DECISION-ORIENTED DEPLOYMENT FRAMEWORK FOR DRIVING AUTOMATION.10 indexed citations
20.
Ran, Bin, et al.. (1989). A GENERAL MODEL AND ALGORITHM FOR THE DYNAMIC TRAFFIC ASSIGNMENT PROBLEMS. 4.26 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.