Xiqun Chen

9.2k total citations · 2 hit papers
261 papers, 6.5k citations indexed

About

Xiqun Chen is a scholar working on Transportation, Automotive Engineering and Building and Construction. According to data from OpenAlex, Xiqun Chen has authored 261 papers receiving a total of 6.5k indexed citations (citations by other indexed papers that have themselves been cited), including 154 papers in Transportation, 105 papers in Automotive Engineering and 97 papers in Building and Construction. Recurrent topics in Xiqun Chen's work include Transportation Planning and Optimization (136 papers), Traffic Prediction and Management Techniques (84 papers) and Transportation and Mobility Innovations (74 papers). Xiqun Chen is often cited by papers focused on Transportation Planning and Optimization (136 papers), Traffic Prediction and Management Techniques (84 papers) and Transportation and Mobility Innovations (74 papers). Xiqun Chen collaborates with scholars based in China, United States and Hong Kong. Xiqun Chen's co-authors include Li Li, Hai Yang, Hongyu Zheng, Jintao Ke, Christopher S. Tang, Shuaichao Zhang, Lei Zhang, Michael A. Schwarzschild, Zheng Zhu and Jiaru Bai and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Neuroscience and SHILAP Revista de lepidopterología.

In The Last Decade

Xiqun Chen

243 papers receiving 6.3k citations

Hit Papers

Short-term forecasting of passenger demand under on-deman... 2017 2026 2020 2023 2017 2018 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiqun Chen China 43 3.1k 2.7k 2.2k 1.6k 1.1k 261 6.5k
Francisco C. Pereira Denmark 36 1.8k 0.6× 524 0.2× 1.1k 0.5× 316 0.2× 108 0.1× 183 4.6k
Xiaozheng He United States 35 1.5k 0.5× 1.3k 0.5× 669 0.3× 1.7k 1.1× 20 0.0× 119 4.2k
Zhenhui Li China 42 2.2k 0.7× 609 0.2× 2.4k 1.1× 1.4k 0.9× 43 0.0× 255 7.6k
Gang Pan China 42 1.2k 0.4× 480 0.2× 533 0.2× 182 0.1× 75 0.1× 359 6.7k
Dong‐Kyu Kim South Korea 31 810 0.3× 416 0.2× 427 0.2× 219 0.1× 62 0.1× 228 4.4k
Wei Hao China 32 1.1k 0.3× 859 0.3× 643 0.3× 944 0.6× 47 0.0× 135 3.1k
Li Li China 54 3.7k 1.2× 4.7k 1.8× 4.4k 2.0× 6.2k 4.0× 98 0.1× 351 11.8k
Zhaohui Wu China 42 648 0.2× 325 0.1× 333 0.2× 201 0.1× 117 0.1× 325 7.0k
Zhixiang Fang China 33 1.6k 0.5× 513 0.2× 472 0.2× 189 0.1× 52 0.0× 162 3.4k
Giovanni Tesoriere Italy 31 735 0.2× 360 0.1× 428 0.2× 158 0.1× 74 0.1× 120 2.8k

Countries citing papers authored by Xiqun Chen

Since Specialization
Citations

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

Fields of papers citing papers by Xiqun Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiqun Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Xiqun Chen. A scholar is included among the top collaborators of Xiqun Chen 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 Xiqun Chen. Xiqun Chen 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.
Dong, Zhiwu, et al.. (2025). Understanding commuter preferences for shared autonomous electric vehicles in first-mile-last-mile scenario. Transportation Research Part D Transport and Environment. 140. 104621–104621. 1 indexed citations
2.
3.
Chen, Yong, et al.. (2024). Space-Time adaptive network for origin-destination passenger demand prediction. Transportation Research Part C Emerging Technologies. 167. 104842–104842. 7 indexed citations
4.
Wang, Xiaohan, Xiqun Chen, Chi Xie, & Taesu Cheong. (2024). Coordinative dispatching of shared and public transportation under passenger flow outburst. Transportation Research Part E Logistics and Transportation Review. 189. 103655–103655. 3 indexed citations
5.
Zhang, Kenan, et al.. (2024). Modeling and managing an on-demand meal delivery system with order bundling. Transportation Research Part E Logistics and Transportation Review. 187. 103597–103597. 1 indexed citations
6.
Ye, Yun, et al.. (2024). Safety or efficiency? Estimating crossing motivations of intoxicated pedestrians by leveraging the inverse reinforcement learning. Travel Behaviour and Society. 35. 100760–100760. 3 indexed citations
7.
Mo, Dong, et al.. (2024). Optimizing consolidated shared charging and electric ride-sourcing services. Transportation Research Part E Logistics and Transportation Review. 184. 103484–103484. 13 indexed citations
8.
Hu, Simon, et al.. (2024). Uncovering driving factors and spatiotemporal patterns of urban passenger car CO2 emissions: A case study in Hangzhou, China. Applied Energy. 375. 124094–124094. 7 indexed citations
9.
Zhai, Chunjie, et al.. (2024). Competitive pricing for ride-sourcing platforms with MARL. Transportation Research Part C Emerging Technologies. 165. 104697–104697. 5 indexed citations
10.
Li, Wenxiang, et al.. (2023). Multi-day activity pattern recognition based on semantic embeddings of activity chains. Travel Behaviour and Society. 34. 100682–100682. 7 indexed citations
11.
Zhu, Zheng, et al.. (2023). A non-local grouping tensor train decomposition model for travel demand analysis concerning categorical independent variables. Transportation Research Part C Emerging Technologies. 157. 104396–104396. 2 indexed citations
12.
Li, Junyi, et al.. (2023). A physics-informed Transformer model for vehicle trajectory prediction on highways. Transportation Research Part C Emerging Technologies. 154. 104272–104272. 50 indexed citations
13.
Mo, Dong, et al.. (2023). Optimizing subsidy strategies of the ride-sourcing platform under government regulation. Transportation Research Part E Logistics and Transportation Review. 173. 103112–103112. 19 indexed citations
14.
Zhu, Zheng, et al.. (2023). A Bayesian clustering ensemble Gaussian process model for network-wide traffic flow clustering and prediction. Transportation Research Part C Emerging Technologies. 148. 104032–104032. 15 indexed citations
15.
Chen, Yong, et al.. (2023). Dynamic-learning spatial-temporal Transformer network for vehicular trajectory prediction at urban intersections. Transportation Research Part C Emerging Technologies. 156. 104330–104330. 25 indexed citations
16.
Chen, Yong, et al.. (2023). A multi-scale unified model of human mobility in urban agglomerations. Patterns. 4(11). 100862–100862. 4 indexed citations
17.
Wang, Heng, et al.. (2022). A Bi-Level Optimization Model for Ride-Sourcing Platform’s Spatial Pricing Strategy. Journal of Advanced Transportation. 2022. 1–22. 5 indexed citations
18.
Chen, Xiqun, et al.. (2020). PKMYT1 Promotes Gastric Cancer Cell Proliferation and Apoptosis Resistance. SHILAP Revista de lepidopterología. 3 indexed citations
19.
Chen, Xiqun, et al.. (2018). Adaptive Rolling Smoothing With Heterogeneous Data for Traffic State Estimation and Prediction. IEEE Transactions on Intelligent Transportation Systems. 20(4). 1247–1258. 33 indexed citations
20.
Xiong, Chenfeng, et al.. (2018). Integrating mesoscopic dynamic traffic assignment with agent-based travel behavior models for cumulative land development impact analysis. Transportation Research Part C Emerging Technologies. 93. 446–462. 16 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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