Sina Dabiri

811 total citations · 1 hit paper
8 papers, 575 citations indexed

About

Sina Dabiri is a scholar working on Transportation, Building and Construction and Control and Systems Engineering. According to data from OpenAlex, Sina Dabiri has authored 8 papers receiving a total of 575 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Transportation, 5 papers in Building and Construction and 2 papers in Control and Systems Engineering. Recurrent topics in Sina Dabiri's work include Traffic Prediction and Management Techniques (5 papers), Human Mobility and Location-Based Analysis (4 papers) and Web Data Mining and Analysis (2 papers). Sina Dabiri is often cited by papers focused on Traffic Prediction and Management Techniques (5 papers), Human Mobility and Location-Based Analysis (4 papers) and Web Data Mining and Analysis (2 papers). Sina Dabiri collaborates with scholars based in United States. Sina Dabiri's co-authors include Kevin Heaslip, Chandan K. Reddy, Chang‐Tien Lu, Montasir Abbas, Nikola Marković, Naren Ramakrishnan, Michael Eichler, Qiao Li, Peter A. Noseworthy and Joel Xue and has published in prestigious journals such as Expert Systems with Applications, IEEE Transactions on Knowledge and Data Engineering and Transportation Research Part C Emerging Technologies.

In The Last Decade

Sina Dabiri

7 papers receiving 559 citations

Hit Papers

Inferring transportation modes from GPS trajectories usin... 2017 2026 2020 2023 2017 50 100 150 200 250
What are hit papers?

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.

  1. Inferring transportation modes from GPS trajectories using a convolutional neural network
    2017 287 citations Sina Dabiri, Kevin Heaslip Transportation Research Part C Emerging Technologies profile →

Peers

Sina Dabiri
Leon Stenneth United States
Tianfu He China
Kun Ouyang China
George Rosario Jagadeesh Singapore
Iulian Sandu Popa France
Zhaosheng Yang China
Pranamesh Chakraborty United States
Donald J Dailey United States
Aude Hofleitner United States
Zeyu Yang China
Leon Stenneth United States
Sina Dabiri
Citations per year, relative to Sina Dabiri Sina Dabiri (= 1×) peers Leon Stenneth

Countries citing papers authored by Sina Dabiri

Since Specialization
Citations

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

Fields of papers citing papers by Sina Dabiri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sina Dabiri

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

All Works

8 of 8 papers shown
1.
Li, Qiao, Erick Andres Perez Alday, Ali Bahrami Rad, et al.. (2022). QT-interval estimation improved with fusion of multiple machine-based annotators. Journal of Electrocardiology. 73. 16–16.
2.
Dabiri, Sina, Nikola Marković, Kevin Heaslip, & Chandan K. Reddy. (2020). A deep convolutional neural network based approach for vehicle classification using large-scale GPS trajectory data. Transportation Research Part C Emerging Technologies. 116. 102644–102644. 45 indexed citations
3.
Dabiri, Sina, Chang‐Tien Lu, Kevin Heaslip, & Chandan K. Reddy. (2019). Semi-Supervised Deep Learning Approach for Transportation Mode Identification Using GPS Trajectory Data. IEEE Transactions on Knowledge and Data Engineering. 32(5). 1010–1023. 105 indexed citations
4.
Dabiri, Sina, et al.. (2019). Assigning Bus Delay and Predicting Travel Times using Automated Vehicle Location Data. Transportation Research Record Journal of the Transportation Research Board. 2673(3). 624–636. 6 indexed citations
5.
Dabiri, Sina & Kevin Heaslip. (2018). Developing a Twitter-based traffic event detection model using deep learning architectures. Expert Systems with Applications. 118. 425–439. 101 indexed citations
6.
Dabiri, Sina. (2018). Tweets with traffic-related labels for developing a Twitter-based traffic information system.. Data Archiving and Networked Services (DANS). 1. 3 indexed citations
7.
Dabiri, Sina & Kevin Heaslip. (2017). Inferring transportation modes from GPS trajectories using a convolutional neural network. Transportation Research Part C Emerging Technologies. 86. 360–371. 287 indexed citations breakdown →
8.

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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