Nicholas Fiorentini

521 total citations
20 papers, 382 citations indexed

About

Nicholas Fiorentini is a scholar working on Civil and Structural Engineering, Safety, Risk, Reliability and Quality and Management, Monitoring, Policy and Law. According to data from OpenAlex, Nicholas Fiorentini has authored 20 papers receiving a total of 382 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Civil and Structural Engineering, 7 papers in Safety, Risk, Reliability and Quality and 5 papers in Management, Monitoring, Policy and Law. Recurrent topics in Nicholas Fiorentini's work include Traffic and Road Safety (6 papers), Infrastructure Maintenance and Monitoring (5 papers) and Traffic Prediction and Management Techniques (4 papers). Nicholas Fiorentini is often cited by papers focused on Traffic and Road Safety (6 papers), Infrastructure Maintenance and Monitoring (5 papers) and Traffic Prediction and Management Techniques (4 papers). Nicholas Fiorentini collaborates with scholars based in Italy, Germany and China. Nicholas Fiorentini's co-authors include Massimo Losa, Jiandong Huang, Sara Bressi, Pietro Leandri, Mehdi Maboudi, M. Gerke, Gonzalo León, Qiong Tian, Ji Zhou and Hao Luo and has published in prestigious journals such as SHILAP Revista de lepidopterología, Sensors and Sustainability.

In The Last Decade

Nicholas Fiorentini

17 papers receiving 369 citations

Peers

Nicholas Fiorentini
Rafael Jurado-Piña Spain
Xiuguang Song China
David Veneziano United States
H.R. Pasindu Sri Lanka
Seyed Hooman Ghasemi United States
Daniel Ainalis United Kingdom
Jie Jiang China
E T Harrigan
Rafael Jurado-Piña Spain
Nicholas Fiorentini
Citations per year, relative to Nicholas Fiorentini Nicholas Fiorentini (= 1×) peers Rafael Jurado-Piña

Countries citing papers authored by Nicholas Fiorentini

Since Specialization
Citations

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

Fields of papers citing papers by Nicholas Fiorentini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nicholas Fiorentini

This figure shows the co-authorship network connecting the top 25 collaborators of Nicholas Fiorentini. A scholar is included among the top collaborators of Nicholas Fiorentini 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 Nicholas Fiorentini. Nicholas Fiorentini 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.
Fiorentini, Nicholas & Massimo Losa. (2025). Developing safety performance functions to inform transport policies on urban two-lane roads. Research in Transportation Business & Management. 60. 101357–101357.
2.
Fiorentini, Nicholas & Massimo Losa. (2025). Investigating unobserved heterogeneity in factors of fatal and injury crashes across Italian secondary road networks: Fixed and random parameters approach. Transportation Research Interdisciplinary Perspectives. 30. 101344–101344. 2 indexed citations
3.
Fiorentini, Nicholas & Massimo Losa. (2025). Road Detection, Monitoring, and Maintenance Using Remotely Sensed Data. Remote Sensing. 17(5). 917–917. 1 indexed citations
4.
Fiorentini, Nicholas, et al.. (2025). Balancing Public and Private Interests in Urban Transformations: Handling Uncertainty with the Monte Carlo Method. CINECA IRIS Institutial research information system (University of Pisa). 2(2). 3–3.
5.
Fiorentini, Nicholas, et al.. (2024). Defining Risk Curves in feasibility analyses of urban regeneration projects with Monte Carlo method. SHILAP Revista de lepidopterología. 36. 149–170. 2 indexed citations
6.
7.
Tian, Qiong, Nicholas Fiorentini, Ji Zhou, et al.. (2023). Ensemble learning models to predict the compressive strength of geopolymer concrete: a comparative study for geopolymer composition design. Multiscale and Multidisciplinary Modeling Experiments and Design. 7(3). 1793–1806. 8 indexed citations
8.
9.
Fiorentini, Nicholas, et al.. (2023). Remote Sensing and Machine Learning for Riparian Vegetation Detection and Classification. ISTI Open Portal. 369–374. 1 indexed citations
10.
León, Gonzalo, Nicholas Fiorentini, Pietro Leandri, & Massimo Losa. (2023). A New Region-Based Minimal Path Selection Algorithm for Crack Detection and Ground Truth Labeling Exploiting Gabor Filters. Remote Sensing. 15(11). 2722–2722. 8 indexed citations
11.
Latini, Daniele, Fabio Del Frate, Valerio Gagliardi, et al.. (2023). A Concurrent Approach for Infrastructure Monitoring and Risks Prevention Using Space, Aerial and Ground Measurements. Cineca Institutional Research Information System (Tor Vergata University). 6334–6337. 1 indexed citations
12.
Fiorentini, Nicholas, et al.. (2022). Overfitting Prevention in Accident Prediction Models: Bayesian Regularization of Artificial Neural Networks. Transportation Research Record Journal of the Transportation Research Board. 2677(2). 1455–1470. 21 indexed citations
13.
Fiorentini, Nicholas, Pietro Leandri, & Massimo Losa. (2022). Defining machine learning algorithms as accident prediction models for Italian two-lane rural, suburban, and urban roads. International Journal of Injury Control and Safety Promotion. 29(4). 450–462. 8 indexed citations
14.
Fiorentini, Nicholas, Pietro Leandri, & Massimo Losa. (2021). Predicting international roughness index by deep neural networks with Levenberg-Marquardt backpropagation learning algorithm. CINECA IRIS Institutial research information system (University of Pisa). 11534. 23–23. 3 indexed citations
15.
Fiorentini, Nicholas, Mehdi Maboudi, Pietro Leandri, & Massimo Losa. (2021). Can Machine Learning and PS-InSAR Reliably Stand in for Road Profilometric Surveys?. Sensors. 21(10). 3377–3377. 15 indexed citations
16.
Fiorentini, Nicholas & Massimo Losa. (2020). Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms. Infrastructures. 5(7). 61–61. 112 indexed citations
17.
Fiorentini, Nicholas & Massimo Losa. (2020). Long-Term-Based Road Blackspot Screening Procedures by Machine Learning Algorithms. Sustainability. 12(15). 5972–5972. 11 indexed citations
18.
Fiorentini, Nicholas, Mehdi Maboudi, Pietro Leandri, Massimo Losa, & M. Gerke. (2020). Surface Motion Prediction and Mapping for Road Infrastructures Management by PS-InSAR Measurements and Machine Learning Algorithms. Remote Sensing. 12(23). 3976–3976. 43 indexed citations
19.
Fiorentini, Nicholas, Mehdi Maboudi, Massimo Losa, & M. Gerke. (2020). ASSESSING RESILIENCE OF INFRASTRUCTURES TOWARDS EXOGENOUS EVENTS BY USING PS-INSAR-BASED SURFACE MOTION ESTIMATES AND MACHINE LEARNING REGRESSION TECHNIQUES. SHILAP Revista de lepidopterología. V-4-2020. 19–26. 11 indexed citations
20.
Bressi, Sara, Nicholas Fiorentini, Jiandong Huang, & Massimo Losa. (2019). Crumb Rubber Modifier in Road Asphalt Pavements: State of the Art and Statistics. Coatings. 9(6). 384–384. 134 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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