Lixing Yang

15.4k total citations · 1 hit paper
240 papers, 7.8k citations indexed

About

Lixing Yang is a scholar working on Transportation, Industrial and Manufacturing Engineering and Mechanical Engineering. According to data from OpenAlex, Lixing Yang has authored 240 papers receiving a total of 7.8k indexed citations (citations by other indexed papers that have themselves been cited), including 171 papers in Transportation, 159 papers in Industrial and Manufacturing Engineering and 96 papers in Mechanical Engineering. Recurrent topics in Lixing Yang's work include Transportation Planning and Optimization (171 papers), Railway Systems and Energy Efficiency (141 papers) and Railway Engineering and Dynamics (95 papers). Lixing Yang is often cited by papers focused on Transportation Planning and Optimization (171 papers), Railway Systems and Energy Efficiency (141 papers) and Railway Engineering and Dynamics (95 papers). Lixing Yang collaborates with scholars based in China, Italy and United States. Lixing Yang's co-authors include Ziyou Gao, Shukai Li, Yuan Gao, Keping Li, Tao Tang, Jiateng Yin, Jianguo Qi, Xuesong Zhou, Kai Yang and Jungang Shi and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Automatica and European Journal of Operational Research.

In The Last Decade

Lixing Yang

222 papers receiving 7.6k citations

Hit Papers

Dynamic passenger demand oriented metro train scheduling ... 2017 2026 2020 2023 2017 50 100 150 200 250

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lixing Yang China 50 5.1k 4.8k 2.6k 1.5k 1.5k 240 7.8k
Keping Li China 30 1.3k 0.2× 1.5k 0.3× 714 0.3× 943 0.6× 682 0.5× 226 3.1k
Xuesong Zhou United States 36 1.8k 0.4× 2.9k 0.6× 604 0.2× 1.1k 0.7× 1.6k 1.1× 114 4.6k
Anita Schöbel Germany 31 1.3k 0.2× 1.3k 0.3× 386 0.1× 422 0.3× 359 0.2× 134 3.0k
Maged Dessouky United States 45 2.6k 0.5× 2.6k 0.5× 335 0.1× 758 0.5× 1.5k 1.0× 142 5.8k
Jianjun Wu China 38 1.7k 0.3× 3.6k 0.8× 613 0.2× 825 0.5× 1.5k 1.0× 218 5.2k
Jean‐François Cordeau Canada 56 9.3k 1.8× 2.4k 0.5× 332 0.1× 854 0.6× 3.6k 2.5× 185 12.0k
Xiaobo Qu China 53 1.0k 0.2× 3.2k 0.7× 452 0.2× 3.0k 2.0× 2.1k 1.4× 241 8.1k
Daniele Vigo Italy 45 8.7k 1.7× 1.4k 0.3× 300 0.1× 554 0.4× 2.8k 1.9× 117 10.6k
Leo Kroon Netherlands 39 3.9k 0.8× 2.8k 0.6× 1.6k 0.6× 207 0.1× 653 0.4× 107 4.8k

Countries citing papers authored by Lixing Yang

Since Specialization
Citations

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

Fields of papers citing papers by Lixing Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lixing Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Lixing Yang. A scholar is included among the top collaborators of Lixing Yang 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 Lixing Yang. Lixing Yang 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.
Li, Shukai, et al.. (2025). Multi-step look ahead deep reinforcement learning approach for automatic train regulation of urban rail transit lines with energy-saving. Engineering Applications of Artificial Intelligence. 145. 110181–110181.
4.
Li, Shukai, et al.. (2025). Dynamic adjustment strategy of electric bus operations: A spatial branch-and-bound method with acceleration techniques. Transportation Research Part C Emerging Technologies. 171. 105003–105003. 1 indexed citations
5.
Zhang, Jinlei, et al.. (2024). EF-former for short-term passenger flow prediction during large-scale events in urban rail transit systems. Information Fusion. 117. 102916–102916. 14 indexed citations
6.
Luan, Xiaojie, et al.. (2024). Integrated optimization of train timetabling and rolling stock circulation problem with flexible short-turning and energy-saving strategies. Transportation Research Part C Emerging Technologies. 166. 104756–104756. 3 indexed citations
7.
Yin, Jiateng, et al.. (2024). Model for Intercity Railway Schedule Optimization Incorporating Metrorail Transfers. Transportation Research Record Journal of the Transportation Research Board. 2678(12). 1395–1415. 3 indexed citations
8.
Yang, Lixing, et al.. (2024). New Exact Algorithm for the integrated train timetabling and rolling stock circulation planning problem with stochastic demand. European Journal of Operational Research. 316(3). 906–929. 9 indexed citations
10.
Gao, Jiaqi, et al.. (2024). Data-driven traffic sensor location and path flow estimation using Wasserstein metric. Applied Mathematical Modelling. 133. 211–231. 2 indexed citations
11.
Yang, Kai, et al.. (2024). Hybrid risk-averse location-inventory-allocation with secondary disaster considerations in disaster relief logistics: A distributionally robust approach. Transportation Research Part E Logistics and Transportation Review. 186. 103558–103558. 9 indexed citations
12.
Yin, Jiateng, et al.. (2023). Optimization of system resilience in urban rail systems: Train rescheduling considering congestions of stations. Computers & Industrial Engineering. 185. 109657–109657. 21 indexed citations
13.
Yang, Lixing, et al.. (2023). Robust collaborative passenger flow control on a congested metro line: A joint optimization with train timetabling. Transportation Research Part B Methodological. 168. 27–55. 25 indexed citations
14.
Ji, Hangyu, Rui Wang, Chuntian Zhang, et al.. (2023). Optimization of train schedule with uncertain maintenance plans in high-speed railways: A stochastic programming approach. Omega. 124. 102999–102999. 12 indexed citations
15.
Li, Shukai, et al.. (2023). Hierarchical optimal control framework to automatic train regulation combined with energy-efficient speed trajectory calculation in metro lines. Transportation Research Part C Emerging Technologies. 149. 104059–104059. 8 indexed citations
16.
Yang, Jian, et al.. (2023). Short-term passenger flow prediction for multi-traffic modes: A Transformer and residual network based multi-task learning method. Information Sciences. 642. 119144–119144. 20 indexed citations
17.
Yin, Jiateng, Miao Wang, Andrea D’Ariano, Jinlei Zhang, & Lixing Yang. (2023). Synchronization of train timetables in an urban rail network: A bi-objective optimization approach. Transportation Research Part E Logistics and Transportation Review. 174. 103142–103142. 15 indexed citations
19.
Huang, Zhipeng, et al.. (2022). Optimization of train timetables in high-speed railway corridors considering passenger departure time and seat-class preferences. Transportation Letters. 15(2). 111–128. 5 indexed citations
20.
Hu, Yuting, Shukai Li, Maged Dessouky, Lixing Yang, & Ziyou Gao. (2022). Computationally efficient train timetable generation of metro networks with uncertain transfer walking time to reduce passenger waiting time: A generalized Benders decomposition-based method. Transportation Research Part B Methodological. 163. 210–231. 10 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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