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.
Review of energy-efficient train control and timetabling
2016293 citationsGerben M. Scheepmaker, Rob M.P. Goverde et al.profile →
Recent applications of big data analytics in railway transportation systems: A survey
2018215 citationsRob M.P. Goverde et al.Transportation Research Part C Emerging Technologiesprofile →
A literature review of Artificial Intelligence applications in railway systems
2022139 citationsLorenzo De Donato, Nikola Bešinović 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 Rob M.P. Goverde
Since
Specialization
Citations
This map shows the geographic impact of Rob M.P. Goverde'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 Rob M.P. Goverde with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rob M.P. Goverde more than expected).
Fields of papers citing papers by Rob M.P. Goverde
This network shows the impact of papers produced by Rob M.P. Goverde. 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 Rob M.P. Goverde. The network helps show where Rob M.P. Goverde may publish in the future.
Co-authorship network of co-authors of Rob M.P. Goverde
This figure shows the co-authorship network connecting the top 25 collaborators of Rob M.P. Goverde.
A scholar is included among the top collaborators of Rob M.P. Goverde 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 Rob M.P. Goverde. Rob M.P. Goverde is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Su, Shuai, Zhongbei Tian, & Rob M.P. Goverde. (2023). Energy-Efficient Train Operation. Research Repository (Delft University of Technology).1 indexed citations
8.
Donato, Lorenzo De, Nikola Bešinović, Francesco Flammini, et al.. (2022). A literature review of Artificial Intelligence applications in railway systems. Transportation Research Part C Emerging Technologies. 140. 103679–103679.139 indexed citations breakdown →
Goverde, Rob M.P. & Gerben M. Scheepmaker. (2015). Running time supplements: Energy-efficient train control versus robust timetables. Data Archiving and Networked Services (DANS).8 indexed citations
Goverde, Rob M.P.. (2012). Robuust spoor met ERTMS. Research Repository (Delft University of Technology).1 indexed citations
14.
Kecman, Pavle & Rob M.P. Goverde. (2012). Process Mining Approach for Recovery of Realized Train Paths and Route Conflict Identification. Data Archiving and Networked Services (DANS).3 indexed citations
Goverde, Rob M.P. & Ingo A. Hansen. (2000). TNV-PREPARE: ANALYSIS OF DUTCH RAILWAY OPERATIONS BASED ON TRAIN DETECTION DATA. Annual Conference on Computers. 779–788.38 indexed citations
Goverde, Rob M.P.. (1998). Synchronization Control of Scheduled Train Services to Minimize Passenger Waiting Times. Research Repository (Delft University of Technology).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.