Qixiu Cheng

Research Interests

Transportation Network Modelling and Optimization

• Dynamic network loading
• Dynamic traffic assignment
• Dynamic congestion pricing

Freeway Traffic Management and Control

• Freeway travel time reliability
• Traffic bottleneck analysis, management and control
• Traffic flow theory (macroscopic and microscopic)

Transportation Big Data Analytics and Urban Informatics

• Multi-source data-driven traffic flow model
• Data fusion technology for traffic state estimation and prediction
• Spatial big data analytics with deep learning

Selected Journal Publications (* Corresponding Author)

• Cheng, Q., Liu, Z.*, Lu, J., List, G., Liu, P., & Zhou, X. (2024). Using frequency domain analysis to elucidate travel time reliability along congested freeway corridors. Transportation Research Part B, 184, 102961. [URL]
• Wang, Z., Liu, Z., Cheng, Q.*, & Gu, Z.* (2024). Integrated self-consistent macro-micro traffic flow modelling and calibration framework based on trajectory data. Transportation Research Part C, 158, 104439. [URL]
• Cheng, Q., Lin, Y., Zhou, X., & Liu, Z.* (2024). Analytical formulation for explaining the variation of traffic states: A fundamental diagram modeling perspective with stochastic parameters. European Journal of Operational Research, 312(1), 182-197. [URL]
• Pan, Y., Guo, J., Chen, Y., Cheng, Q.*, Li, W., & Liu, Y. (2024). A fundamental diagram based interpretable framework for traffic flow estimation and prediction by combining a Markovian model with deep learning. Expert Systems with Applications, 238, 122219. [URL]
• Cheng, Q., Lin, Y.,& Lu, J. (2024). Dynamic system modeling and integrated transportation demand-and-supply management with a polynomial arrival queue model. ASCE Journal of Transportation Engineering, Part A, 150(4), 04024005. [URL]
• Xing, J., Liu, R., Zhang, Y., Choudhury, C.F., Fu, X., & Cheng, Q.* (2023). Urban network-wide traffic volume estimation under sparse deployment of detectors. Accepted by Transportmetrica A. [URL]
• Wang, Z., Shi, Y., Tong, W., Gu, Z., & Cheng, Q.* (2023). Car-following models for human-driven vehicles and autonomous vehicles: A systematic review. ASCE Journal of Transportation Engineering, Part A, 149(8), 04023075. [URL]
• Zhang, K., Zhang, H., Cheng, Q., Chen, X., Wang, Z., & Liu, Z. (2023). A customized two-stage parallel algorithm for solving the combined modal split and traffic assignment problem. Computers & Operations Research, 154, 106193. [URL]
• Huo, J., Liu, Z., Chen, J., Cheng, Q., & Meng, Q. (2023). Bayesian optimization for congestion pricing problems: A general framework and its instability. Transportation Research Part B, 169, 1-28. [URL]
• Zhang, Y., Cheng, Q.*, Liu, Y., & Liu, Z. (2023). Full-scale spatio-temporal traffic flow estimation for city-wide networks: A transfer learning based approach. Transportmetrica B, 11(1), 869-895. [URL]
• Cheng, Q., Liu, Z.*, Guo, J., Wu, X., Pendyala, R., Belezamo, B., & Zhou, X.* (2022). Estimating key traffic state parameters through parsimonious spatial queue models. Transportation Research Part C, 137, 103596. [URL]
• Cheng, Q.*, Chen, Y., & Liu, Z. (2022). A bi-level programing model for the optimal lane reservation problem. Expert Systems with Applications, 189, 116147. [URL]
• Wang, Y., Cheng, Q., Wang, M., & Liu, Z. (2022). Weibull-distribution-based neural network for capacity estimation. ASCE Journal of Transportation Engineering, Part A, 148(4), 04022009. [URL]
• Zhou, X., Cheng, Q., Wu, X., Li, P., Belezamo, B., Lu, J., & Abbasi, M. (2022). A meso-to-macro cross-resolution approach for connecting polynomial arrival queue model to volume-delay function with inflow-demand-to-capacity ratio. Multimodal Transportation, 1(2), 100017. [URL]
• Liu, Z., Wang, Y., Cheng, Q., & Yang, H. (2022). Analysis of the information entropy on traffic flows. IEEE Transactions on Intelligent Transportation Systems, 23(10), 18012-18023. [URL]
• Zhang, Y., Li, L., Zhang, W., & Cheng, Q.* (2022). GATC and DeepCut: Deep spatiotemporal feature extraction and clustering for large-scale transportation network partition. Physica A, 606, 128110. [URL]
• Xing, J., Wu, W., Cheng, Q.*, & Liu, R. (2022). Traffic state estimation of urban road network by multi-source data fusion: Review and new insights. Physica A, 595, 127079. [URL]
• Cheng, Q., Liu, Z.*, Lin, Y., & Zhou, X.* (2021). An s-shaped three-parameter (S3) traffic stream model with consistent car following relationship. Transportation Research Part B, 153, 246-271. [URL]
• Liu, Z., Wang, Z, Cheng, Q., Yin, R., & Wang, M. (2021). Estimation of urban network capacity with second-best constraints for multimodal transport systems. Transportation Research Part B, 152, 276-294. [URL]
• Chen, Y., Song, X., Cheng, Q.*, An, Q., & Zhang, Y. (2021). A cordon-based reservation system for urban traffic management. Physica A, 582, 126276. [URL]
• An, Q., Fu, X., Huang, D., Cheng, Q., & Liu, Z. (2020). Analysis of adding-runs strategy for peak-hour regular bus services. Transportation Research Part E, 143, 102100. [URL]
• Cheng, Q., Wang, S., Liu, Z.*, & Yuan, Y. (2019). Surrogate-based simulation optimization approach for day-to-day dynamics model calibration with real data. Transportation Research Part C, 105, 422-438. [URL]
• Cheng, Q., Liu, Z.*, & Szeto, W. Y. (2019). A cell-based dynamic congestion pricing considering travel distance and congestion level. Transportmetrica B, 7(1), 1286-1304. [URL]
• Liu, Z., Liu, Y., Meng, Q., & Cheng, Q. (2019). A tailored machine learning approach for urban transport network flow estimation. Transportation Research Part C, 108, 130-150. [URL]
• Cheng, Q., Liu, Z.*, Liu, F., & Jia, R. (2017). Urban dynamic congestion pricing: An overview and emerging research needs. International Journal of Urban Sciences, 21(S1), 3-18. [URL]
• Liu, Z., Wang, S., Zhou, B., & Cheng, Q. (2017). Robust optimization of distance-based tolls in a network considering stochastic day to day dynamics. Transportation Research Part C, 79, 58-72. [URL]

Conference and Other Publications

• Zhang, Y., Cheng, Q.*, Liu, Y., & Liu, Z. (2021). A Gaussian Process-based Model for Transport Network Flow Estimation. The 4th International Symposium on Multimodal Transportation (ISMT 2021), Nanjing, 12/2021.
• Cheng, Q., Liu, Z., Guo, J., Wu, X., & Zhou, X.* (2020). A queueing-theoretic performance model for oversaturated traffic systems. Proceedings of the 99th Annual Meeting of Transportation Research Board (No. 20-03048), Washington D.C., 01/2020.
• Chen, Y., Cheng, Q.*, & Liu, Z. (2020). A bi-level programming model for the optimal lane reservation problem. Proceedings of the 99th Annual Meeting of Transportation Research Board (No. 20-03266), Washington D.C., 01/2020.
• Liu, Y., Lyu, C., Cheng, Q., & Liu, Z.* (2019). Exploring personalization in the application of intelligent transportation systems. International Workshop on Ride-hailing Algorithms, Applications, and Systems (RAAS 2019) at the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Chicago, 11/2019.
• Cheng, Q., Liu, Z., Guo, J., Wu, X., & Zhou, X. (2019). Fluid approximation for traffic system performance evaluation under oversaturated conditions: Model formulation, parameter calibration and applications. 2019 INFORMS Annual Meeting, Seattle, Washington, 10/2019.
• Fang, Z., Cheng, Q., Liu, Z.*, & Liu, Y. (2019). A deep learning approach for the traffic assignment problem. Proceedings of the 98th Annual Meeting of Transportation Research Board (No. 19-01956), Washington D.C., 01/2019.
• Selmoune, A., Cheng, Q., & Liu, Z.* (2018). Analysis of the influencing factors in the public acceptance of urban congestion pricing practices in Nanjing City. Proceedings of the 23rd International Conference of Hong Kong Society for Transportation Studies (HKSTS), Hong Kong, 12/2018.
• Xie, X., Cheng, Q.*, Selmoune, A., Lu, B., & Liu, Z. (2018). A Lagrangian-based approach for reliable user equilibrium considering link travel time variance. CICTP 2018, Beijing, 06/2018.
• Zhou, D., Cheng, Q.*, An, Q., Lu, B., & Liu, Z. (2018). Link criticality analysis based on reliable shortest path in a network with correlated link travel times. CICTP 2018, Beijing, 06/2018.
• Cheng, Q., & Liu, Z.* (2018). A cell-based dynamic congestion pricing considering travel distance and congestion level. The 7th International Symposium on Dynamic Traffic Assignment, Hong Kong, 06/2018.
• Cheng, Q., Xing, J., Selmoune, A., Fu, X., & Liu, Z.* (2017). Day-to-day dynamics in urban railway networks based on smart card transaction data. Proceedings of the 22nd International Conference of Hong Kong Society for Transportation Studies (HKSTS), Hong Kong, 12/2017.