Erik de Romph

405 total citations
22 papers, 294 citations indexed

About

Erik de Romph is a scholar working on Transportation, Control and Systems Engineering and Building and Construction. According to data from OpenAlex, Erik de Romph has authored 22 papers receiving a total of 294 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Transportation, 9 papers in Control and Systems Engineering and 9 papers in Building and Construction. Recurrent topics in Erik de Romph's work include Transportation Planning and Optimization (16 papers), Traffic control and management (9 papers) and Traffic Prediction and Management Techniques (9 papers). Erik de Romph is often cited by papers focused on Transportation Planning and Optimization (16 papers), Traffic control and management (9 papers) and Traffic Prediction and Management Techniques (9 papers). Erik de Romph collaborates with scholars based in Netherlands and Japan. Erik de Romph's co-authors include Ties Brands, Niels van Oort, Gonçalo Homem de Almeida Correia, Harry Timmermans, J. W. Cook, Luc Johannes Josephus Wismans, Adam J. Pel, Serge Hoogendoorn, N.J. Van der Zijpp and Michiel C.J. Bliemer and has published in prestigious journals such as Journal of Transport Geography, International Journal of Forecasting and Transportation Research Record Journal of the Transportation Research Board.

In The Last Decade

Erik de Romph

21 papers receiving 269 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Erik de Romph Netherlands	10	254	96	81	52	14	22	294
Zhanhong Cheng Canada	11	208 0.8×	115 1.2×	73 0.9×	49 0.9×	9 0.6×	23	277
Clas Rydergren Sweden	11	246 1.0×	113 1.2×	59 0.7×	94 1.8×	12 0.9×	37	302
Linjie Gao China	9	196 0.8×	105 1.1×	54 0.7×	39 0.8×	26 1.9×	25	293
Markus Friedrich Germany	12	248 1.0×	89 0.9×	142 1.8×	74 1.4×	15 1.1×	28	343
Dominik Ziemke Germany	9	315 1.2×	98 1.0×	209 2.6×	52 1.0×	19 1.4×	17	383
Ties Brands Netherlands	10	267 1.1×	99 1.0×	71 0.9×	29 0.6×	23 1.6×	28	316
Yongtaek Lim South Korea	8	247 1.0×	94 1.0×	52 0.6×	66 1.3×	14 1.0×	20	331
Marcel Rieser Switzerland	9	293 1.2×	84 0.9×	155 1.9×	92 1.8×	31 2.2×	21	362
Joel Freedman United States	9	350 1.4×	73 0.8×	170 2.1×	38 0.7×	46 3.3×	19	382
Liangpeng Gao China	11	274 1.1×	129 1.3×	122 1.5×	30 0.6×	22 1.6×	29	354

Countries citing papers authored by Erik de Romph

Since Specialization

Citations

This map shows the geographic impact of Erik de Romph'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 Erik de Romph with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Erik de Romph more than expected).

Fields of papers citing papers by Erik de Romph

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Erik de Romph. 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 Erik de Romph. The network helps show where Erik de Romph may publish in the future.

Co-authorship network of co-authors of Erik de Romph

This figure shows the co-authorship network connecting the top 25 collaborators of Erik de Romph. A scholar is included among the top collaborators of Erik de Romph 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 Erik de Romph. Erik de Romph is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Pel, Adam J., et al.. (2018). Static Traffic Assignment with Queuing: model properties and applications. Transportmetrica A Transport Science. 15(2). 179–214. 26 indexed citations

Correia, Gonçalo Homem de Almeida, et al.. (2018). Road Network Design in a Developing Country Using Mobile Phone Data: An Application to Senegal. IEEE Intelligent Transportation Systems Magazine. 12(2). 36–49. 9 indexed citations

Correia, Gonçalo Homem de Almeida, et al.. (2017). Using metro smart card data to model location choice of after-work activities: An application to Shanghai. Journal of Transport Geography. 63. 40–47. 70 indexed citations

Pel, Adam J., et al.. (2016). Improving convergence of quasi dynamic assignment models. Research Repository (Delft University of Technology). 2 indexed citations

Oort, Niels van, Ties Brands, & Erik de Romph. (2015). Short term ridership prediction in public transport by processing smart card data. 1–14. 10 indexed citations

Oort, Niels van, et al.. (2015). Unreliability effects in public transport modelling. University of Twente Research Information. 3(1). 113–130. 11 indexed citations

Oort, Niels van, Ties Brands, & Erik de Romph. (2015). Short-Term Prediction of Ridership on Public Transport with Smart Card Data. Transportation Research Record Journal of the Transportation Research Board. 2535(1). 105–111. 50 indexed citations

Wismans, Luc Johannes Josephus, et al.. (2014). Real Time Traffic Models, Decision Support for Traffic Management. Procedia Environmental Sciences. 22. 220–235. 13 indexed citations

Oort, Niels van, et al.. (2014). Incorporating unreliability of transit in transport demand models: Theoretical and practical approach. University of Twente Research Information. 1–17. 4 indexed citations

10.

Brands, Ties, et al.. (2014). Modelling Public Transport Route Choice, with Multiple Access and Egress Modes. Transportation research procedia. 1(1). 12–23. 39 indexed citations

11.

Romph, Erik de. (2013). Using BIG data in transport modelling. Data Archiving and Networked Services (DANS). 6 indexed citations

12.

Bliemer, Michiel C.J., et al.. (2013). Requirements for Traffic Assignment Models for Strategic Transport Planning: A Critical Assessment. Research Repository (Delft University of Technology). 1–25. 10 indexed citations

13.

Arentze, TA Theo, et al.. (2012). Activity-based dynamic traffic modeling: Influence of population sampling fraction size on simulation error.. TU/e Research Portal (Eindhoven University of Technology). 1–17. 4 indexed citations

14.

Kolen, Bas, et al.. (2011). Evacuation a serious game for preparation. University of Twente Research Information. 317–322. 6 indexed citations

15.

Kolen, Bas, et al.. (2009). SPOEL: An instrument for training, simulation and testing of emergency planning for mass evacuation - user experiences.. University of Twente Research Information. 1 indexed citations

16.

Romph, Erik de, et al.. (2009). Simulating traffic processes for practicing large scale evacuation.. University of Twente Research Information. 1 indexed citations

17.

Zijpp, N.J. Van der & Erik de Romph. (1997). A dynamic traffic forecasting application on the Amsterdam beltway. International Journal of Forecasting. 13(1). 87–103. 5 indexed citations

18.

Romph, Erik de, et al.. (1994). APPLICATION OF DYNAMIC ASSIGNMENT IN WASHINGTON, D.C., METROPOLITAN AREA. Transportation Research Record Journal of the Transportation Research Board. 3 indexed citations

19.

Romph, Erik de. (1994). A dynamic traffic assignment model: Theory and applications. Data Archiving and Networked Services (DANS). 14 indexed citations

20.

Romph, Erik de, et al.. (1993). APPLICATION OF 3DAS (3-DIMENSIONAL ASSIGNMENT) AT THE WASHINGTON METROPOLITAN AREA. 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.

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