Noelia Cáceres

717 total citations
20 papers, 524 citations indexed

About

Noelia Cáceres is a scholar working on Transportation, Building and Construction and Mechanical Engineering. According to data from OpenAlex, Noelia Cáceres has authored 20 papers receiving a total of 524 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Transportation, 6 papers in Building and Construction and 4 papers in Mechanical Engineering. Recurrent topics in Noelia Cáceres's work include Urban Transport and Accessibility (9 papers), Human Mobility and Location-Based Analysis (9 papers) and Transportation Planning and Optimization (8 papers). Noelia Cáceres is often cited by papers focused on Urban Transport and Accessibility (9 papers), Human Mobility and Location-Based Analysis (9 papers) and Transportation Planning and Optimization (8 papers). Noelia Cáceres collaborates with scholars based in Spain. Noelia Cáceres's co-authors include Francisco G. Benítez, Johan Wideberg, J. M. del Castillo and Teresa Martins and has published in prestigious journals such as IEEE Transactions on Intelligent Transportation Systems, Transportation and Transportation Research Record Journal of the Transportation Research Board.

In The Last Decade

Noelia Cáceres

17 papers receiving 491 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Noelia Cáceres Spain 9 416 222 69 63 58 20 524
Shahadat Iqbal United States 8 405 1.0× 130 0.6× 60 0.9× 24 0.4× 53 0.9× 18 479
Jingtao Ma United States 9 626 1.5× 239 1.1× 140 2.0× 19 0.3× 67 1.2× 27 773
Abdullah Kurkcu United States 11 189 0.5× 115 0.5× 37 0.5× 28 0.4× 18 0.3× 26 304
L. Montero Spain 9 281 0.7× 200 0.9× 102 1.5× 25 0.4× 15 0.3× 15 340
Zhiren Huang China 10 279 0.7× 159 0.7× 38 0.6× 9 0.1× 29 0.5× 21 378
Merkebe Getachew Demissie Canada 15 454 1.1× 183 0.8× 18 0.3× 22 0.3× 54 0.9× 25 525
Jingqin Gao United States 10 172 0.4× 88 0.4× 98 1.4× 22 0.3× 23 0.4× 37 357
Zeyang Cheng China 11 186 0.4× 221 1.0× 140 2.0× 19 0.3× 21 0.4× 33 429
David Gundlegård Sweden 10 209 0.5× 138 0.6× 49 0.7× 64 1.0× 8 0.1× 32 312
Baohua Mao China 12 279 0.7× 130 0.6× 108 1.6× 20 0.3× 20 0.3× 58 466

Countries citing papers authored by Noelia Cáceres

Since Specialization
Citations

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

Fields of papers citing papers by Noelia Cáceres

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Noelia Cáceres. 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 Noelia Cáceres. The network helps show where Noelia Cáceres may publish in the future.

Co-authorship network of co-authors of Noelia Cáceres

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

All Works

20 of 20 papers shown
1.
Cáceres, Noelia, et al.. (2022). USING GETIS-ORD GI* MAPS TO UNDERSTAND BICYCLE MOBILITY DURING THE WINTER SEASON IN VALENCIA, SPAIN. DYNA. 97(4). 436–444. 4 indexed citations
2.
Cáceres, Noelia, et al.. (2020). Land use inference from mobility mobile phone data and household travel surveys. Transportation research procedia. 47. 417–424. 3 indexed citations
3.
Cáceres, Noelia, et al.. (2020). A machine learning methodology to predict alerts and maintenance interventions in roads. Road Materials and Pavement Design. 22(10). 2267–2288. 13 indexed citations
4.
Cáceres, Noelia, et al.. (2020). Exploring strengths and weaknesses of mobility inference from mobile phone data vs. travel surveys. Transportmetrica A Transport Science. 16(3). 574–601. 26 indexed citations
5.
Cáceres, Noelia & Francisco G. Benítez. (2018). Supervised Land Use Inference from Mobility Patterns. Journal of Advanced Transportation. 2018. 1–12. 8 indexed citations
6.
Cáceres, Noelia, et al.. (2018). Automatic Prediction of Maintenance Intervention Types in Roads using Machine Learning and Historical Records. Transportation Research Record Journal of the Transportation Research Board. 2672(44). 43–54. 3 indexed citations
7.
8.
Cáceres, Noelia, et al.. (2017). Historical maintenance relevant information road-map for a self-learning maintenance prediction procedural approach. IOP Conference Series Materials Science and Engineering. 236. 12107–12107. 4 indexed citations
9.
Benítez, Francisco G., et al.. (2014). Towards automated and cost-efficient track maintenance. Final developments of the ACEM-Rail project. 4 indexed citations
10.
Benítez, Francisco G., et al.. (2014). Estimating of Bootstrap Confidence Intervals for Freight Transport Matrices. Procedia - Social and Behavioral Sciences. 111. 1155–1164.
11.
Castillo, J. M. del, et al.. (2014). Models for the Hazardous Goods Railway Transportation in Spain Considering the Effect of the Catchment Area of the Station. Transportation research procedia. 3. 584–591. 2 indexed citations
12.
Cáceres, Noelia, et al.. (2013). A Logical Framework and Integrated Architecture for the Rail Maintenance Automation. 5 indexed citations
13.
Benítez, Francisco G., et al.. (2013). The Bootstrapping Approach for Inferring Confident Freight Transport Matrices. Rivista Internazionale di Economia dei Trasporti. 42(1).
14.
Benítez, Francisco G., et al.. (2013). Adjustment of Origin–Destination Matrices Based on Traffic Counts and Bootstrapping Confidence Intervals. Transportation Research Record Journal of the Transportation Research Board. 2343(1). 43–50.
15.
Cáceres, Noelia, et al.. (2012). Estimating Traffic Flow Profiles According to a Relative Attractiveness Factor. Procedia - Social and Behavioral Sciences. 54. 1115–1124. 17 indexed citations
16.
Cáceres, Noelia, et al.. (2012). Traffic Flow Estimation Models Using Cellular Phone Data. IEEE Transactions on Intelligent Transportation Systems. 13(3). 1430–1441. 109 indexed citations
17.
Cáceres, Noelia, et al.. (2011). Inferring origin–destination trip matrices from aggregate volumes on groups of links: a case study using volumes inferred from mobile phone data. Journal of Advanced Transportation. 47(7). 650–666. 23 indexed citations
18.
Cáceres, Noelia, Johan Wideberg, & Francisco G. Benítez. (2008). Review of traffic data estimations extracted from cellular networks. IET Intelligent Transport Systems. 2(3). 179–192. 100 indexed citations
19.
Cáceres, Noelia, Johan Wideberg, & Francisco G. Benítez. (2007). Deriving origin–destination data from a mobile phone network. IET Intelligent Transport Systems. 1(1). 15–26. 187 indexed citations
20.
Wideberg, Johan, Noelia Cáceres, & Francisco G. Benítez. (2006). DERIVING TRAFFIC DATA FROM A CELLULAR NETWORK. idUS (Universidad de Sevilla). 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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