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.
Combining kohonen maps with arima time series models to forecast traffic flow
1996659 citationsMark Dougherty 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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Countries citing papers authored by Mark Dougherty
Since
Specialization
Citations
This map shows the geographic impact of Mark Dougherty'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 Mark Dougherty with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Dougherty more than expected).
This network shows the impact of papers produced by Mark Dougherty. 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 Mark Dougherty. The network helps show where Mark Dougherty may publish in the future.
Co-authorship network of co-authors of Mark Dougherty
This figure shows the co-authorship network connecting the top 25 collaborators of Mark Dougherty.
A scholar is included among the top collaborators of Mark Dougherty 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 Mark Dougherty. Mark Dougherty is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Westin, Jerker, Dag Nyholm, Anders Johansson, et al.. (2010). 12-month results from a novel test battery used in a duodenal levodopa infusion trial. European Journal of Neurology. 17. 21–21.2 indexed citations
Westin, Jerker, Mevludin Memedi, Dag Nyholm, et al.. (2009). A successful computer method for assessing drawing impairment in Parkinson's disease. European Journal of Neurology. 16. 559–559.1 indexed citations
15.
Fleyeh, Hasan & Mark Dougherty. (2007). SVM based traffic sign classification using legender moments. Indian International Conference on Artificial Intelligence. 957–968.2 indexed citations
Dougherty, Mark, et al.. (2007). Machine vision for automating visual condition monitoring of railway sleepers. 289–295.2 indexed citations
18.
Westin, Jerker, Mark Dougherty, Dag Nyholm, & Torgny Groth. (2006). A home environment test battery for status assessment in patients with motor fluctuations. European Journal of Neurology. 13. 213–214.6 indexed citations
19.
Westin, Jerker, Mobyen Uddin Ahmed, Dag Nyholm, Mark Dougherty, & Torgny Groth. (2006). A fuzzy rule-based decision support system for Duodopa treatment in Parkinson's disease. European Journal of Neurology. 13. 214–214.1 indexed citations
20.
Dougherty, Mark, et al.. (1993). THE USE OF NEURAL NETWORKS TO RECOGNISE AND PREDICT TRAFFIC CONGESTION. Traffic engineering & control. 34(6). 311–314.62 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.