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.
Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment
2009688 citationsJuan Carlos Herrera, Daniel B. Work 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 Ryan Herring'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 Ryan Herring with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ryan Herring more than expected).
This network shows the impact of papers produced by Ryan Herring. 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 Ryan Herring. The network helps show where Ryan Herring may publish in the future.
Co-authorship network of co-authors of Ryan Herring
This figure shows the co-authorship network connecting the top 25 collaborators of Ryan Herring.
A scholar is included among the top collaborators of Ryan Herring 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 Ryan Herring. Ryan Herring is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
16 of 16 papers shown
1.
Hofleitner, Aude, Ryan Herring, & Alexandre M. Bayen. (2012). Probability Distributions of Travel Times on Arterial Networks: Traffic Flow and Horizontal Queuing Theory Approach. Transportation Research Board 91st Annual MeetingTransportation Research Board.19 indexed citations
Hofleitner, Aude, Ryan Herring, Alexandre M. Bayen, et al.. (2012). Large-Scale Estimation of Arterial Traffic and Structural Analysis of Traffic Patterns from Probe Vehicles.9 indexed citations
Herring, Ryan, Aude Hofleitner, Saurabh Amin, et al.. (2010). Using Mobile Phones to Forecast Arterial Traffic through Statistical Learning. Transportation Research Board 89th Annual MeetingTransportation Research Board.59 indexed citations
9.
Bayen, Alexandre M., Laurent El Ghaoui, & Ryan Herring. (2010). Real-time traffic modeling and estimation with streaming probe data using machine learning.15 indexed citations
Ban, Xuegang, et al.. (2009). Optimal Sensor Placement for Both Traffic Control and Traveler Information Applications. Transportation Research Board 88th Annual MeetingTransportation Research Board.6 indexed citations
12.
Herrera, Juan Carlos, Daniel B. Work, Ryan Herring, Xuegang Ban, & Alexandre M. Bayen. (2009). Evaluation of Traffic Data Obtained via GPS-Enabled Mobile Phones: the Mobile Century Field Experiment. eScholarship (California Digital Library).12 indexed citations
Herrera, Juan Carlos, Daniel B. Work, Ryan Herring, et al.. (2009). Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment. Transportation Research Part C Emerging Technologies. 18(4). 568–583.688 indexed citations breakdown →
15.
Hunter, Timothy, Ryan Herring, Pieter Abbeel, & Alexandre M. Bayen. (2009). Path and travel time inference from GPS probe vehicle data.94 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.