Taoran Song

971 total citations
41 papers, 706 citations indexed

About

Taoran Song is a scholar working on Mechanical Engineering, Civil and Structural Engineering and Safety, Risk, Reliability and Quality. According to data from OpenAlex, Taoran Song has authored 41 papers receiving a total of 706 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Mechanical Engineering, 17 papers in Civil and Structural Engineering and 11 papers in Safety, Risk, Reliability and Quality. Recurrent topics in Taoran Song's work include Railway Engineering and Dynamics (18 papers), Infrastructure Maintenance and Monitoring (12 papers) and Geotechnical Engineering and Analysis (11 papers). Taoran Song is often cited by papers focused on Railway Engineering and Dynamics (18 papers), Infrastructure Maintenance and Monitoring (12 papers) and Geotechnical Engineering and Analysis (11 papers). Taoran Song collaborates with scholars based in China, United States and Canada. Taoran Song's co-authors include Paul Schonfeld, Hao Pu, Jianping Hu, Xianbao Peng, Hong Zhang, Wei Li, Wei Li, Hong Zhang, Lihui Peng and Hong Zhang and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and IEEE Transactions on Intelligent Transportation Systems.

In The Last Decade

Taoran Song

37 papers receiving 706 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Taoran Song China	16	296	227	176	172	139	41	706
Jianping Hu China	16	301 1.0×	230 1.0×	180 1.0×	167 1.0×	142 1.0×	36	699
Hao Pu China	18	481 1.6×	369 1.6×	295 1.7×	247 1.4×	196 1.4×	74	1.1k
Jyh‐Cherng Jong United States	13	183 0.6×	124 0.5×	152 0.9×	262 1.5×	89 0.6×	21	755
Xianbao Peng China	10	170 0.6×	143 0.6×	88 0.5×	78 0.5×	73 0.5×	12	375
Min-Wook Kang United States	12	212 0.7×	97 0.4×	167 0.9×	56 0.3×	154 1.1×	51	644
Chih‐Yuan Chu Taiwan	16	239 0.8×	45 0.2×	160 0.9×	109 0.6×	60 0.4×	38	607
Yousef Shafahi Iran	13	136 0.5×	106 0.5×	252 1.4×	197 1.1×	91 0.7×	50	797
Yanhui Wang China	16	59 0.2×	140 0.6×	68 0.4×	163 0.9×	71 0.5×	56	792
Kamal C. Sarma United States	11	586 2.0×	76 0.3×	273 1.6×	67 0.4×	55 0.4×	13	1.0k

Countries citing papers authored by Taoran Song

Since Specialization

Citations

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

Fields of papers citing papers by Taoran Song

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Taoran Song

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Pu, Hao, et al.. (2025). Optimizing mountain railway alignments with a potential field guided 3D-RRT-star algorithm. 1(1). 57–67.

Song, Taoran, Hao Pu, T.Y. Yang, et al.. (2025). Data‐driven distributionally robust optimization of railway alignments in earthquake‐prone regions considering active fault zone risks. Computer-Aided Civil and Infrastructure Engineering. 40(25). 4321–4341.

Pu, Hao, Paul Schonfeld, Taoran Song, et al.. (2025). Environmental suitability analysis integrating carbon emission evaluation for green railway alignment optimization. International Journal of Rail Transportation. 14(1). 45–71. 1 indexed citations

Pu, Hao, Qingliang Zeng, Taoran Song, et al.. (2025). A hybrid proximal policy optimization and particle swarm algorithm for highway alignment optimization. Advanced Engineering Informatics. 69. 103959–103959.

Pu, Hao, et al.. (2025). Tunnel location optimization for railway alignment design in complex mountainous regions. Automation in Construction. 174. 106132–106132. 1 indexed citations

Pu, Hao, et al.. (2024). An event tree-based distance transform algorithm for simultaneously determining mountain railway alignments and station locations. Expert Systems with Applications. 261. 125575–125575. 3 indexed citations

Pu, Hao, et al.. (2024). Knowledge graph-driven mountain railway alignment optimization integrating karst hazard assessment. Applied Soft Computing. 167. 112421–112421. 4 indexed citations

Pu, Hao, et al.. (2024). Railway alignment optimization in regions with densely-distributed obstacles based on semantic topological maps. Integrated Computer-Aided Engineering. 31(4). 421–437. 4 indexed citations

Pu, Hao, Shumin Xie, Taoran Song, Paul Schonfeld, & Xianbao Peng. (2023). Three-dimensional subway alignment recreation considering tunnel construction gauges. Tunnelling and Underground Space Technology. 141. 105347–105347. 3 indexed citations

10.

Pu, Hao, Ling Cai, Taoran Song, Paul Schonfeld, & Jianping Hu. (2023). Minimizing costs and carbon emissions in railway alignment optimization: A bi-objective model. Transportation Research Part D Transport and Environment. 116. 103615–103615. 23 indexed citations

11.

Pu, Hao, Taoran Song, Paul Schonfeld, et al.. (2023). A 3D Monte Carlo tree search method for railway alignment optimization. Applied Soft Computing. 151. 111158–111158. 9 indexed citations

12.

Pu, Hao, Ting Hu, Taoran Song, et al.. (2023). Modeling and application of a customized knowledge graph for railway alignment optimization. Expert Systems with Applications. 244. 122999–122999. 9 indexed citations

13.

Pu, Hao, et al.. (2023). A 3D-RRT-star algorithm for optimizing constrained mountain railway alignments. Engineering Applications of Artificial Intelligence. 130. 107770–107770. 15 indexed citations

14.

Pu, Hao, et al.. (2023). Multi-Task Deep Learning Methods for Determining Railway Major Technical Standards. IEEE Transactions on Intelligent Transportation Systems. 25(6). 5904–5918. 3 indexed citations

15.

Wang, Guanghui, et al.. (2022). Multi-Task Deep Learning Methods for Determining Railway Major Technical Standards. SSRN Electronic Journal. 1 indexed citations

16.

Pu, Hao, et al.. (2022). Modelling and optimization of constrained alignments for existing railway reconstruction. International Journal of Rail Transportation. 11(3). 428–447. 11 indexed citations

17.

Pu, Hao, et al.. (2022). A geographic information model for 3-D environmental suitability analysis in railway alignment optimization. Integrated Computer-Aided Engineering. 30(1). 67–88. 15 indexed citations

18.

Pu, Hao, Paul Schonfeld, Wei Li, et al.. (2021). Optimization of grade-separated road and railway crossings based on a distance transform algorithm. Engineering Optimization. 54(2). 232–251. 16 indexed citations

19.

Hong, Zhang, Hao Pu, Paul Schonfeld, et al.. (2020). Multi-objective railway alignment optimization considering costs and environmental impacts. Applied Soft Computing. 89. 106105–106105. 61 indexed citations

20.

Pu, Hao, Taoran Song, Paul Schonfeld, et al.. (2019). Mountain railway alignment optimization using stepwise & hybrid particle swarm optimization incorporating genetic operators. Applied Soft Computing. 78. 41–57. 69 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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