期刊论文(SCI/SSCI, * 通讯作者)
[17] Liu,S., Zhang, Y., Wang, Z.*, Liu, X., and Yang, H. (2025) Personalizedorigin-destination travel time estimation with active adversarial inversereinforcement learning and Transformer, Transportation Research Part E, 193, 103839.
[16] Wang, Z., Zheng, Z., Chen X., Ma W., and Yang, H. (2024) Modeling the evolution of incident impact in urban road networks by leveraging the spatiotemporal propagation of shockwaves. Transportation Research Part C, 164, 104668.
[15] Yang, Z., Shang, W., Miao, L., Gupta, S., and Wang, Z. (2024). Pricing decisions of online and offline dual-channel supply chains considering data resource mining. Omega, 126, 103050.
[14] Liu, S., Wang, Z.*, Zhang, Y., and Yang, H. (2024) Anomalous ride-hailing driver detection with deep transfer inverse reinforcement learning. Transportation Research Part C, 159, 104466.
[13] Zheng, Z., Wang, Z.*, Hu, Z., Wan Z., and Ma W.* (2024) Recovering traffic data from the corrupted noise: A doubly physics-regularized denoising diffusion model. Transportation Research Part C, 160, 104513.
[12] Zheng, Z., Wang, Z.*, Liu S., and Ma W. (2024) Exploring the spatial effects on the level of congestion caused by traffic accidents in urban road networks: A case study of Beijing. Travel Behaviour and Society, 35,100728.
[11] Liu, S., Zhang, Y., Wang, Z.*, and S., Gu (2023) AdaBoost-Bagging deep inverse reinforcement learning for autonomous taxi cruising route and speed planning. Transportation Research Part E, 177, 103232.
[10] Zheng, Z., Wang, Z.*, Chen X., Ma W., and Ran, B. (2023). Spatiotemporal clustering for the impact region caused by a traffic incident: an improved fuzzy C-means approach with guaranteed consistency. Transportmetrica A:Transport Science, 1-30.
[9] Liu, S., Wang, Z., and Jiang, H. (2022). Signal timing optimisation with the contraflow left-turn lane design using the cell transmission model. Transportmetrica A:Transport Science, 18(3), 1254-1277.
[8] Zheng, Z., Qi, X., Wang, Z.* and Ran, B. (2021) Incorporating multiple congestion levels into spatiotemporal analysis for the impact of a traffic incident. Accident Analysis & Prevention, 159, 106255.
[7] Wang, Z., Liu, K., Zhu, L., and Jiang, H. (2021). Detecting the occurrence times and locations of multiple traffic crashes simultaneously with probe vehicle data. Transportation Research Part C, 126, 103014.
[6] Wang, Z., and Jiang, H. (2020). Identifying secondary crashes on freeways by leveraging the spatiotemporal evolution of shockwaves in the speed contour plot. Journal of Transportation Engineering, Part A, 146(2), 04019072.
[5] Wang, Z., Zhu, L., Ran, B., and Jiang, H. (2020). Queue profile estimation at a signalized intersection by exploiting the spatiotemporal propagation of shockwaves. Transportation Research Part B, 141, 59-71.
[4] Zheng, Z., Wang, Z., Zhu, L., and Jiang, H. (2020). Determinants of the congestion caused by a traffic accident in urban road networks. Accident Analysis & Prevention, 136, 105327.
[3] Chen, Z., Wang, Z., and Jiang, H. (2019). Analyzing the heterogeneous impacts of high-speed rail entry on air travel in China: A hierarchical panel regression approach. Transportation Research Part A, 127, 86-98.
[2] Wang, Z., and Jiang, H. (2019).Simultaneous correction of the time and location bias associated with a reported crash by exploiting the spatiotemporal evolution of travel speed. Transportation Research Part B,123, 199-223.
[1] Wang, Z., Qi, X., and Jiang, H.(2018). Estimating the spatiotemporal impact of traffic incidents: An integer programming approach consistent with the propagation of shockwaves. Transportation Research Part B,111, 356-369.
