Hu Shao

1.5k total citations
69 papers, 1.1k citations indexed

About

Hu Shao is a scholar working on Transportation, Control and Systems Engineering and Computational Mechanics. According to data from OpenAlex, Hu Shao has authored 69 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Transportation, 25 papers in Control and Systems Engineering and 20 papers in Computational Mechanics. Recurrent topics in Hu Shao's work include Transportation Planning and Optimization (33 papers), Traffic control and management (21 papers) and Sparse and Compressive Sensing Techniques (20 papers). Hu Shao is often cited by papers focused on Transportation Planning and Optimization (33 papers), Traffic control and management (21 papers) and Sparse and Compressive Sensing Techniques (20 papers). Hu Shao collaborates with scholars based in China, Hong Kong and United States. Hu Shao's co-authors include William H. K. Lam, Agachai Sumalee, Pengjie Liu, Bi Yu Chen, Qiang Meng, Xiaoyu Wu, Zhixiang Fang, Qingquan Li, Mei Lam Tam and Ting Wu and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and Transportation Research Part C Emerging Technologies.

In The Last Decade

Hu Shao

56 papers receiving 1.1k citations

Peers

Hu Shao

TRANS

CSE

Sang Quang Nguyen Vietnam

Deren Han China

Juan A. Mesa Spain

Marco E. Lübbecke Germany

H J Payne United States

Kwong Meng Teo Singapore

Antonio M. Rodríguez‐Chía Spain

Stefan Ruzika Germany

Guangmin Wang China

Andrea Raith New Zealand

Sang Quang Nguyen Vietnam

Hu Shao

1.6k ×2.2

TRANS

686 ×1.9

CSE

516 ×1.7

682 ×3.1

30 ×0.2

Citations per year, relative to Hu Shao Hu Shao (= 1×) peers Sang Quang Nguyen

Countries citing papers authored by Hu Shao

Since Specialization

Citations

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

Fields of papers citing papers by Hu Shao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hu Shao

This figure shows the co-authorship network connecting the top 25 collaborators of Hu Shao. A scholar is included among the top collaborators of Hu Shao 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 Hu Shao. Hu Shao 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

Li, Jun, Hu Shao, Shenheng Xu, Pengjie Liu, & Guoxin Li. (2025). A three-term cg projected-based method with control factor and its inertial version for constrained nonlinear equations. RAIRO. Operations research. 59(2). 939–958.

Liu, Pengjie, et al.. (2025). An accelerated Dai–Yuan conjugate gradient projection method with the optimal choice. Engineering Optimization. 57(12). 3648–3676.

Wu, Ting, et al.. (2025). A generalized three-stage optimization model for emergency medical services under uncertainties: Integrating rescue station locations, ambulance deployment, and vehicle dispatch. Transportation Research Part E Logistics and Transportation Review. 205. 104499–104499.

Shao, Hu, et al.. (2025). A physics-informed machine learning framework for speed-flow prediction: Integrating an S-shaped traffic stream model with deep learning models. Transportation Research Part C Emerging Technologies. 180. 105362–105362. 1 indexed citations

Bian, Yongming, et al.. (2025). A modified Riemannian hybrid conjugate gradient method for nonconvex optimization problems. Mathematics and Computers in Simulation. 239. 679–695.

Liu, Pengjie, et al.. (2025). Modeling the sparsity and density of measurement errors for origin–destination demand estimation. Transportation Research Part E Logistics and Transportation Review. 201. 104253–104253.

Sun, Weiwei, et al.. (2025). Multi-type traffic sensor location problem for origin–destination estimation considering spatiotemporal correlation and sensor failure. Transportation Research Part C Emerging Technologies. 179. 105288–105288.

Zhuo, Yue, et al.. (2024). Revisiting the traffic flow observability problem: A matrix-based model for traffic networks with or without centroid nodes. Transportation Research Part B Methodological. 190. 103099–103099. 1 indexed citations

Shao, Hu, et al.. (2024). An improved Dai‐Liao‐style hybrid conjugate gradient‐based method for solving unconstrained nonconvex optimization and extension to constrained nonlinear monotone equations. Mathematical Methods in the Applied Sciences. 48(2). 1563–1588. 2 indexed citations

10.

Yu, Tengjiang, et al.. (2024). Effect of SBS/SBR/component molecular compatibility on anti-aging properties for asphalt binder. Journal of Adhesion Science and Technology. 38(15). 2812–2826. 4 indexed citations

11.

Chen, Zibo, et al.. (2024). An efficient hybrid conjugate gradient method with an adaptive strategy and applications in image restoration problems. Applied Numerical Mathematics. 204. 362–379. 10 indexed citations

12.

Zhang, Kunyu, Hu Shao, Ting Wu, & Xiaoquan Wang. (2024). A class of accelerated GADMM-based method for multi-block nonconvex optimization problems. Numerical Algorithms. 98(2). 943–985.

13.

Shao, Hu, et al.. (2024). A multi-task spatio-temporal generative adversarial network for prediction of travel time reliability in peak hour periods. Physica A Statistical Mechanics and its Applications. 638. 129632–129632. 6 indexed citations

14.

Jiang, Minghu, et al.. (2024). Two hybrid conjugate gradient based algorithms on Riemannian manifolds with adaptive restart strategy for nonconvex optimization problems. Journal of Computational and Applied Mathematics. 461. 116452–116452.

15.

Shao, Hu, et al.. (2023). Two families of self-adjusting spectral hybrid DL conjugate gradient methods and applications in image denoising. Applied Mathematical Modelling. 118. 393–411. 22 indexed citations

16.

Yuan, Yuan, et al.. (2023). A sequential ADMM algorithm to find sparse LCP solutions using a l2−l1 regularization technique with application in bimatrix game. Journal of Computational and Applied Mathematics. 437. 115470–115470. 2 indexed citations

17.

Shao, Hu, et al.. (2023). A generative model for vehicular travel time distribution prediction considering spatial and temporal correlations. Physica A Statistical Mechanics and its Applications. 621. 128769–128769. 5 indexed citations

18.

Shao, Hu, et al.. (2023). A trajectory prediction method based on bayonet importance encoding and bidirectional LSTM. Expert Systems with Applications. 223. 119888–119888. 9 indexed citations

19.

Shao, Hu, et al.. (2023). Study on aging mechanism of SBS/SBR compound-modified asphalt based on molecular dynamics. REVIEWS ON ADVANCED MATERIALS SCIENCE. 62(1). 11 indexed citations

20.

Chen, Bi Yu, William H. K. Lam, Agachai Sumalee, & Hu Shao. (2011). An efficient solution algorithm for solving multi-class reliability-based traffic assignment problem. Mathematical and Computer Modelling. 54(5-6). 1428–1439. 54 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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