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Volume 10 Issue 9
Sep.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2023  >  10(9): 1781-1796
H. Y. Lin, Y. Liu, S. Li, and X. B. Qu, “How generative adversarial networks promote the development of intelligent transportation systems: A survey,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 9, pp. 1781–1796, Sept. 2023. doi: 10.1109/JAS.2023.123744
Citation: H. Y. Lin, Y. Liu, S. Li, and X. B. Qu, “How generative adversarial networks promote the development of intelligent transportation systems: A survey,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 9, pp. 1781–1796, Sept. 2023. doi: 10.1109/JAS.2023.123744
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How Generative Adversarial Networks Promote the Development of Intelligent Transportation Systems: A Survey

doi: 10.1109/JAS.2023.123744
Funds:  This work was supported by the National Natural Science Foundation of China (52221005, 52220105001, 52272420) and European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie (101025896)
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    Abstract
  • In current years, the improvement of deep learning has brought about tremendous changes: As a type of unsupervised deep learning algorithm, generative adversarial networks (GANs) have been widely employed in various fields including transportation. This paper reviews the development of GANs and their applications in the transportation domain. Specifically, many adopted GAN variants for autonomous driving are classified and demonstrated according to data generation, video trajectory prediction, and security of detection. To introduce GANs to traffic research, this review summarizes the related techniques for spatio-temporal, sparse data completion, and time-series data evaluation. GAN-based traffic anomaly inspections such as infrastructure detection and status monitoring are also assessed. Moreover, to promote further development of GANs in intelligent transportation systems (ITSs), challenges and noteworthy research directions on this topic are provided. In general, this survey summarizes 130 GAN-related references and provides comprehensive knowledge for scholars who desire to adopt GANs in their scientific works, especially transportation-related tasks.

     

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  • [1]
    I. Ahmed, S. Din, G. Jeon, F. Piccialli, and G. Fortino, “Towards collaborative robotics in top view surveillance: A framework for multiple object tracking by detection using deep learning,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1253–1270, 2021. doi: 10.1109/JAS.2020.1003453
    [2]
    B. Peng, M. F. Keskin, B. Kulcsár, and H. Wymeersch, “Connected autonomous vehicles for improving mixed traffic efficiency in unsignalized intersections with deep reinforcement learning,” Comm. Transportation Research, vol. 1, p. 100017, 2021. doi: 10.1016/j.commtr.2021.100017
    [3]
    J. Dong, S. Chen, M. Miralinaghi, T. Chen, and S. Labi, “Development and testing of an image transformer for explainable autonomous driving systems,” J. Intelligent and Connected Vehicles, vol. 5, no. 3, pp. 235–249, 2022. doi: 10.1108/JICV-06-2022-0021
    [4]
    H. Ding, W. Li, N. Xu, and J. Zhang, “An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: Cooperative velocity and lane-changing control,” J. Intelligent and Connected Vehicles, vol. 5, no. 3, pp. 316–332, 2022. doi: 10.1108/JICV-07-2022-0030
    [5]
    F. Liang, G. Zhiwei, W. Tao, G. Jinfeng, and D. Feng, “Collision avoidance model and its validation for intelligent vehicles based on deep learning LSTM,” J. Autom. Safety and Energy, vol. 13, no. 1, p. 104, 2022.
    [6]
    S. Li, Y. Liu, and X. Qu, “Model controlled prediction: A reciprocal alternative of model predictive control,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 1107–1110, 2022. doi: 10.1109/JAS.2022.105611
    [7]
    Y. Liu, C. Lyu, Y. Zhang, Z. Liu, W. Yu, and X. Qu, “DeepTSP: Deep traffic state prediction model based on large-scale empirical data,” Comm. Transportation Research, vol. 1, p. 100012, 2021. doi: 10.1016/j.commtr.2021.100012
    [8]
    Y. Liu, Z. Liu, and R. Jia, “DeepPF: A deep learning based architecture for metro passenger flow prediction,” Transportation Research Part C: Emerging Technologies, vol. 101, pp. 18–34, 2019. doi: 10.1016/j.trc.2019.01.027
    [9]
    M. Xu, H. Lin, and Y. Liu, “A deep learning approach for vehicle velocity prediction considering the influence factors of multiple lanes,” Electronic Research Archive, vol. 31, no. 1, pp. 401–420, 2023. doi: 10.3934/era.2023020
    [10]
    I. Goodfellow, J. Pouget-Abadie, M. Mirza, et al., “Generative adversarial networks,” Communi. ACM, vol. 63, no. 11, pp. 139–144, 2020. doi: 10.1145/3422622
    [11]
    J. Liu, T. Li, P. Xie, S. Du, F. Teng, and X. Yang, “Urban big data fusion based on deep learning: An overview,” Infor. Fusion, vol. 53, pp. 123–133, 2020. doi: 10.1016/j.inffus.2019.06.016
    [12]
    S. Wang, J. Cao, and P. Yu, “Deep learning for spatio-temporal data mining: A survey,” IEEE Trans. Knowledge and Data Engineering, vol. 34, no. 8, pp. 3681–3700, 2020.
    [13]
    Y. Liu, R. Jia, J. Ye, and X. Qu, “How machine learning informs ride-hailing services: A survey,” Comm. Transportation Research, vol. 2, p. 100075, 2022. doi: 10.1016/j.commtr.2022.100075
    [14]
    J. Ye, J. Zhao, K. Ye, and C. Xu, “How to build a graph-based deep learning architecture in traffic domain: A survey,” IEEE Trans. Intelligent Transportation Systems, vol. 23, no. 5, pp. 3904–3924, 2020.
    [15]
    K.-F. Wang, C. Gou, Y.-J. Duan, Y.-L. Lin, X.-H. Zheng, and F. Wang, “Generative adversarial networks: The state of the art and beyond,” Acta Autom. Sinica, vol. 43, no. 3, pp. 321–332, 2017.
    [16]
    A. Aggarwal, M. Mittal, and G. Battineni, “Generative adversarial network: An overview of theory and applications,” Inter. J. Infor. Management Data Insights, vol. 1, no. 1, p. 100004, 2021. doi: 10.1016/j.jjimei.2020.100004
    [17]
    R. Kulkarni, R. Gaikwad, R. Sugandhi, P. Kulkarni, and S. Kone, “Survey on deep learning in music using GAN,” Int. J. Eng. Res. Technol, vol. 8, no. 9, pp. 646–648, 2019.
    [18]
    W. Xia, Y. Zhang, Y. Yang, J.-H. Xue, B. Zhou, and M.-H. Yang, “Gan inversion: A survey,” IEEE Trans. Pattern Analysis Machine Intelligence, vol. 45, no. 3, pp. 3121–3138, 2022.
    [19]
    X. Wang, H. Guo, S. Hu, M.-C. Chang, and S. Lyu, “GAN-generated faces detection: A survey and new perspectives,” arXiv preprint arXiv: 2202.07145, 2022.
    [20]
    A. You, J. K. Kim, I. H. Ryu, and T. K. Yoo, “Application of generative adversarial networks (GAN) for ophthalmology image domains: A survey,” Eye and Vision, vol. 9, no. 1, pp. 1–19, 2022. doi: 10.1186/s40662-021-00274-y
    [21]
    A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv preprint arXiv: 1511.06434, 2015.
    [22]
    T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of stylegan,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, 2020, pp. 8110–8119.
    [23]
    A. Brock, J. Donahue, and K. Simonyan, “Large scale GAN training for high fidelity natural image synthesis,”arXiv preprint arXiv: 1809.11096, 2018.
    [24]
    H. Zhang, T. Xu, H. Li, et al., “StackGAN: Text to photo-realistic image synthesis with stacked generative adversarial networks,” in Proc. IEEE Int. Conf. Computer Vision, 2017, pp. 5907–5915.
    [25]
    P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2017, pp. 1125–1134.
    [26]
    G. Antipov, M. Baccouche, and J.-L. Dugelay, “Face aging with conditional generative adversarial networks,” in Proc. IEEE Inte. Conf. Image Processing, 2017, pp. 2089–2093.
    [27]
    J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in Proc. IEEE Inte. Conf. Computer Vision, 2017, pp. 2223–2232.
    [28]
    T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training GANs,” in Proc. 30th Int. Conf. Neural Information Proc. Syst., 2016, pp. 2234–2242.
    [29]
    A. Odena, C. Olah, and J. Shlens, “Conditional image synthesis with auxiliary classifier GANs,” in Proc. Int. Conf. Machine Learning, 2017, pp. 2642–2651.
    [30]
    M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” in Proc. Int. Conf. Machine Learning, 2017, pp. 214–223.
    [31]
    T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,”arXiv preprint arXiv: 1710.10196, 2017.
    [32]
    T. Wang, M. Liu, J. Zhu, et al., “Video-to-video synthesis,” arXiv preprint arXiv: 1808.06601, 2018.
    [33]
    L. Yu, W. Zhang, J. Wang, and Y. Yu, “SeqGAN: Sequence generative adversarial nets with policy gradient,” in Proc. AAAI Conf Artificial Intelligence, 2017, vol. 31, p. 1.
    [34]
    Y. Jin, J. Zhang, M. Li, Y. Tian, H. Zhu, and Z. Fang, “Towards the automatic anime characters creation with generative adversarial networks,” arXiv preprint arXiv: 1708.05509, 2017.
    [35]
    M. B. Hedge, M. Nelson, T. Pengilly, and M. Weatherford, “PokéGAN: P2P (Pet to Pokémon) Stylizer,” SMU Data Science Review, vol. 5, no. 2, p. 10, 2021.
    [36]
    M. Brundage, S. Avin, J. Clark, et al., “The malicious use of artificial intelligence: Forecasting, prevention, and mitigation,” arXiv preprint arXiv: 1802.07228, 2018.
    [37]
    S. E. Reed, Z. Akata, S. Mohan, S. Tenka, B. Schiele, and H. Lee, “Learning what and where to draw,” in Prpc. 30th Annual Conf. Neural Information Proc. Syst., 2016, pp. 217–225.
    [38]
    A. Dash, J. C. B. Gamboa, S. Ahmed, M. Liwicki, and M. Z. Afzal, “TAC-GAN-text conditioned auxiliary classifier generative adversarial network,” arXiv preprint arXiv: 1703.06412, 2017.
    [39]
    T.-C. Wang, M.-Y. Liu, J.-Y. Zhu, A. Tao, J. Kautz, and B. Catanzaro, “High-resolution image synthesis and semantic manipulation with conditional gans,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2018, pp. 8798–8807.
    [40]
    D. Yoo, N. Kim, S. Park, A. S. Paek, and I. S. Kweon, “Pixel-level domain transfer,” in Proc. Computer Vision–ECCV: 14th European Conference, Amsterdam, The Netherlands, Proceedings, Part VIII 14, 2016, pp. 517–532.
    [41]
    R. Huang, S. Zhang, T. Li, and R. He, “Beyond face rotation: Global and local perception GAN for photorealistic and identity preserving frontal view synthesis,” in Proc. IEEE Int. Conf. Computer Vision, 2017, pp. 2439–2448.
    [42]
    Y. Taigman, A. Polyak, and L. Wolf, “Unsupervised cross-domain image generation,” arXiv preprint arXiv: 1611.02200, 2016.
    [43]
    G. Perarnau, J. Van De Weijer, B. Raducanu, and J. M. Álvarez, “Invertible conditional gans for image editing,” arXiv preprint arXiv: 1611.06355, 2016.
    [44]
    M.-Y. Liu and O. Tuzel, “Coupled generative adversarial networks,” in Proc. 30th Int. Conf. Neural Information Proc. Syst., 2016, pp. 469–477.
    [45]
    A. Brock, T. Lim, J. M. Ritchie, and N. Weston, “Neural photo editing with introspective adversarial networks,” arXiv preprint arXiv: 1609.07093, 2016.
    [46]
    C. Ledig, L. Theis, F. Huszár, et al., “Photo-realistic single image super-resolution using a generative adversarial network,” in Proc. IEEE Conf Computer Vision and Pattern Recognition, 2017, pp. 4681–4690.
    [47]
    H. Bin, C. Weihai, W. Xingming, and L. Chun-Liang, “High-quality face image SR using conditional generative adversarial networks,” arXiv preprint arXiv: 1707.00737, 2017.
    [48]
    S. Vasu, N. Thekke Madam, and A. Rajagopalan, “Analyzing perception-distortion tradeoff using enhanced perceptual super-resolution network,” in Proc. European Conf. Computer Vision Workshops, 2018, pp. 114–131.
    [49]
    C. Vondrick, H. Pirsiavash, and A. Torralba, “Generating videos with scene dynamics,” Advances Neural Information Processing Systems, vol. 29, 2016.
    [50]
    V. Sandfort, K. Yan, P. J. Pickhardt, and R. M. Summers, “Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks,” Scientific Reports, vol. 9, no. 1, p. 16884, 2019. doi: 10.1038/s41598-019-52737-x
    [51]
    S. Yu, H. Dong, G. Yang, et al., “Deep DE-aliasing for fast compressive sensing MRI,” arXiv preprint arXiv: 1705.07137, 2017.
    [52]
    J. Wu, C. Zhang, T. Xue, B. Freeman, and J. Tenenbaum, “Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling,” in Proc. 30th Int. Conf. Neural Information Proc. Syst., 2016, pp. 82–90.
    [53]
    M. Gadelha, S. Maji, and R. Wang, “3D shape induction from 2D views of multiple objects,” in Proc. Int. Conf. 3D Vision, 2017, pp. 402–411.
    [54]
    A. Li, P. Zhao, X. Liu, A. Mansourian, K. W. Axhausen, and X. Qu, “Comprehensive comparison of E-scooter sharing mobility: Evidence from 30 European cities,” Transportation Research Part D:Transport and Environment, vol. 105, p. 103229, 2022. doi: 10.1016/j.trd.2022.103229
    [55]
    J. Ke, H. Zheng, H. Yang, and X. M. Chen, “Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach,” Transportation Research Part C: Emerging Technologies, vol. 85, pp. 591–608, 2017. doi: 10.1016/j.trc.2017.10.016
    [56]
    J. Bao, X. Shi, and H. Zhang, “Spatial analysis of bikeshare ridership with smart card and POI data using geographically weighted regression method,” IEEE Access, vol. 6, pp. 76049–76059, 2018. doi: 10.1109/ACCESS.2018.2883462
    [57]
    J. Wu and X. Qu, “Intersection control with connected and automated vehicles: A review,” J. Intelligent and Connected Vehicles, vol. 5, no. 3, pp. 260–269, 2022. doi: 10.1108/JICV-06-2022-0023
    [58]
    L. Maoyue, L. Hongyu, H. Xiangmei, X. Guangqi, and Y. Wei, “Surrounding vehicle recognition and information map construction technology in automatic driving,” J. Automotive Safety and Energy, vol. 13, no. 1, p. 131, 2022.
    [59]
    B. Kiran, I. Sobh, V. Talpaert, et al., “Deep reinforcement learning for autonomous driving: A survey,” IEEE Trans. Intelligent Trans- portation Systems, vol. 23, no. 6, pp. 4909–4926, 2021.
    [60]
    M. Bojarski, D. Del Testa, D. Dworakowski, et al., “End to end learning for self-driving cars,” arXiv preprint arXiv: 1604.07316, 2016.
    [61]
    S. Shalev-Shwartz, N. Ben-Zrihem, A. Cohen, and A. Shashua, “Long-term planning by short-term prediction,” arXiv preprint arXiv: 1602.01580, 2016.
    [62]
    E. Santana and G. Hotz, “Learning a driving simulator,” arXiv preprint arXiv: 1608.01230, 2016.
    [63]
    Z. Yang, Y. Chai, D. Anguelov, et al., “SurfelGAN: Synthesizing realistic sensor data for autonomous driving,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, 2020, pp. 11118–11127.
    [64]
    M. Amodio and S. Krishnaswamy, “TraVeLGAN: Image-to-image translation by transformation vector learning,” in Proc. IEEE/CVF Conf. computer Vision and Pattern Recognition, 2019, pp. 8983–8992.
    [65]
    S.-W. Huang, C.-T. Lin, S.-P. Chen, Y.-Y. Wu, P.-H. Hsu, and S.-H. Lai, “AugGAN: Cross domain adaptation with GAN-based data augmentation,” in Proc. European Conf. Computer Vision, 2018, pp. 718–731.
    [66]
    W. Xu, N. Souly, and P. P. Brahma, “Reliability of GAN generated data to train and validate perception systems for autonomous vehicles,” in Proc IEEE/CVF Winter Conf. Applications Computer Vision, 2021, pp. 171–180.
    [67]
    W. Wang, Y. Zhang, J. Gao, et al., “GOPS: A general optimal control problem solver for autonomous driving and industrial control applications,” Communi. Transportation Research, vol. 3, p. 100096, 2023. doi: 10.1016/j.commtr.2023.100096
    [68]
    D. Fei, M. Guanyu, T. En, Z. Nan, B. Jianmin, and Z. Dengyin, “Multi-channel high-resolution network and attention mechanism fusion for vehicle detection model,” J. Automotive Safety and Energy, vol. 13, no. 1, p. 122, 2022.
    [69]
    S. Tulyakov, M.-Y. Liu, X. Yang, and J. Kautz, “MoCoGAN: Decomposing motion and content for video generation,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2018, pp. 1526–1535.
    [70]
    E. L. Denton, “Unsupervised learning of disentangled representations from video,” in Proc. 31st Int. Conf. Neural Information Proc. Syst., 2017, pp. 4417–4426.
    [71]
    J. Walker, K. Marino, A. Gupta, and M. Hebert, “The pose knows: Video forecasting by generating pose futures,” in Proc. IEEE Int. Conf. Computer Vision, 2017, pp. 3332–3341.
    [72]
    A. Clark, J. Donahue, and K. Simonyan, “Adversarial video generation on complex datasets,” arXiv preprint arXiv: 1907.06571,2019.
    [73]
    S. Mozaffari, O. Y. Al-Jarrah, M. Dianati, P. Jennings, and A. Mouzakitis, “Deep learning-based vehicle behavior prediction for autonomous driving applications: A review,” IEEE Trans. Intelligent Transportation Systems, vol. 23, no. 1, pp. 33–47, 2020.
    [74]
    A. Gupta, J. Johnson, L. Fei-Fei, S. Savarese, and A. Alahi, “Social GAN: Socially acceptable trajectories with generative adversarial networks,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2018, pp. 2255–2264.
    [75]
    S. Eiffert, K. Li, M. Shan, S. Worrall, S. Sukkarieh, and E. Nebot, “Probabilistic crowd GAN: Multimodal pedestrian trajectory prediction using a graph vehicle-pedestrian attention network,” IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 5026–5033, 2020. doi: 10.1109/LRA.2020.3004324
    [76]
    Q. Zhang, S. Hu, J. Sun, Q. A. Chen, and Z. M. Mao, “On adversarial robustness of trajectory prediction for autonomous vehicles,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, 2022, pp. 15159–15168.
    [77]
    C. Gómez-Huélamo, M. V. Conde, M. Ortiz, S. Montiel, R. Barea, and L. M. Bergasa, “Exploring attention GAN for vehicle motion prediction,” in Proc. IEEE 25th Int. Conf. Intelligent Transportation Systems, 2022, pp. 4011–4016.
    [78]
    V. Kosaraju, A. Sadeghian, R. Martín-Martín, I. Reid, H. Rezatofighi, and S. Savarese, “Social-BIGAT: Multimodal trajectory forecasting using bicycle-GAN and graph attention networks,” Advances in Neural Information Processing Systems, vol. 32, 2019.
    [79]
    E. Wang, H. Cui, S. Yalamanchi, M. Moorthy, and N. Djuric, “Improving movement predictions of traffic actors in bird’s-eye view models using GANs and differentiable trajectory rasterization,” in Proc. 26th ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining, 2020, pp. 2340–2348.
    [80]
    C. Zhao, Y. Zhu, Y. Du, F. Liao, and C.-Y. Chan, “A novel direct trajectory planning approach based on generative adversarial networks and rapidly-exploring random tree,” IEEE Trans. Intelligent Transportation Systems, vol. 23, no. 10, pp. 17910–17921, 2022. doi: 10.1109/TITS.2022.3164391
    [81]
    M. Ghafoorian, C. Nugteren, N. Baka, O. Booij, and M. Hofmann, “El-GAN: Embedding loss driven generative adversarial networks for lane detection,” in Proc. European Conf. Computer Vision Workshops, 2018, pp. 256–272.
    [82]
    X. Wang, A. Shrivastava, and A. Gupta, “A-fast-RCNN: Hard positive generation via adversary for object detection,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2017, pp. 2606–2615.
    [83]
    J. Li, X. Liang, Y. Wei, T. Xu, J. Feng, and S. Yan, “Perceptual generative adversarial networks for small object detection,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2017, pp. 1222–1230.
    [84]
    Y. Bai, Y. Zhang, M. Ding, and B. Ghanem, “SOD-MTGAN: Small object detection via multi-task generative adversarial network,” in Proc. European Conf. Computer Vision, 2018, pp. 206–221.
    [85]
    A. Liu, X. Liu, J. Fan, et al., “Perceptual-sensitive GAN for generating adversarial patches,” in Proc. AAAI Conf. Artificial Intelligence, 2019, vol. 33, no. 1, pp. 1028–1035.
    [86]
    W. Zhu, J. Wu, T. Fu, J. Wang, J. Zhang, and Q. Shangguan, “Dynamic prediction of traffic incident duration on urban expressways: A deep learning approach based on LSTM and MLP,” J. Intelligent and Connected Vehicles, vol. 4, no. 2, pp. 80–91, 2021. doi: 10.1108/JICV-03-2021-0004
    [87]
    A. Koochali, P. Schichtel, A. Dengel, and S. Ahmed, “Probabilistic forecasting of sensory data with generative adversarial networks–forGAN,” IEEE Access, vol. 7, pp. 63868–63880, 2019. doi: 10.1109/ACCESS.2019.2915544
    [88]
    D. Saxena and J. Cao, “D-GAN: Deep generative adversarial nets for spatio-temporal prediction,” arXiv preprint arXiv: 1907.08556, 2019.
    [89]
    J. Yoon, D. Jarrett, and M. Van der Schaar, “Time-series generative adversarial networks,” Advances in Neural Information Processing Systems, vol. 32, 2019.
    [90]
    L. Zhang, J. Wu, J. Shen, et al., “SATP-GAN: Self-attention based generative adversarial network for traffic flow prediction,” Transportmetrica B:Transport Dynamics, vol. 9, no. 1, pp. 552–568, 2021. doi: 10.1080/21680566.2021.1916646
    [91]
    Y. Zhang, Y. Li, X. Zhou, X. Kong, and J. Luo, “Curb-GAN: Conditional urban traffic estimation through spatio-temporal generative adversarial networks,” in Proc. 26th ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining, 2020, pp. 842–852.
    [92]
    Y. Zhang, Y. Li, X. Zhou, X. Kong, and J. Luo, “TrafficGAN: Off-deployment traffic estimation with traffic generative adversarial networks,” in Proc. IEEE Int. Conf. Data Mining, 2019, pp. 1474–1479.
    [93]
    D. Xu, C. Wei, P. Peng, Q. Xuan, and H. Guo, “GE-GAN: A novel deep learning framework for road traffic state estimation,” Transportation Research Part C: Emerging Technologies, vol. 117, p. 102635, 2020. doi: 10.1016/j.trc.2020.102635
    [94]
    J. Jin J, D. Rong, T. Zhang, et al., “A GAN-based short-term link traffic prediction approach for urban road networks under a parallel learning framework,” IEEE Trans. Intelligent Transportation Syst., vol. 23, no. 9, pp. 16185–16196, 2022. doi: 10.1109/TITS.2022.3148358
    [95]
    Y. Lv, Y. Chen, L. Li, and F.-Y. Wang, “Generative adversarial networks for parallel transportation systems,” IEEE Intelligent Transportation Systems Magazine, vol. 10, no. 3, pp. 4–10, 2018. doi: 10.1109/MITS.2018.2842249
    [96]
    Y. Wang, Y. Zheng, and Y. Xue, “Travel time estimation of a path using sparse trajectories,” in Proc. 20th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2014, pp. 25–34.
    [97]
    I. M. Baytas, C. Xiao, X. Zhang, F. Wang, A. K. Jain, and J. Zhou, “Patient subtyping via time-aware LSTM networks,” in Proc. 23rd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2017, pp. 65–74.
    [98]
    H.-F. Yu, N. Rao, and I. S. Dhillon, “Temporal regularized matrix factorization for high-dimensional time series prediction,” in Proc. 30th Int. Conf. Neural Information Proc. Syst., 2016, pp. 847–855.
    [99]
    Z. Liu, Y. Yang, W. Huang, Z. Tang, N. Li, and F. Wu, “How do your neighbors disclose your information: Social-aware time series imputation,” in Proc. World Wide Web Conf., 2019, pp. 1164–1174.
    [100]
    H. Wu, S. Zheng, J. Zhang, and K. Huang, “GP-GAN: Towards realistic high-resolution image blending,” in Proc. 27th ACM Int. Conf. Multimedia, 2019, pp. 2487–2495.
    [101]
    D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2016, pp. 2536–2544.
    [102]
    X. Wang, D. Xu, and F. Gu, “3D smodel inpainting based on 3D deep convolutional generative adversarial network,” IEEE Access, vol. 8, pp. 170355–170363, 2020. doi: 10.1109/ACCESS.2020.3024288
    [103]
    W. Li, L. Min, Y. Jia-qing, Z. Ling-yu, P. Ke, and L. Zheng-xi, “Urban traffic flow data recovery method based on generative adversarial network,” J. Transportation Systems Engineering and Information Technology, vol. 18, no. 6, p. 63, 2018.
    [104]
    G. Xiong, Z. Li, M. Zhao, et al., “TrajSGAN: A Semantic-guiding adversarial network for Urban trajectory generation,” IEEE Trans. Computational Social Systems, 2023.
    [105]
    A. Borji, “Pros and cons of GAN evaluation measures,” Computer Vision and Image Understanding, vol. 179, pp. 41–65, 2019. doi: 10.1016/j.cviu.2018.10.009
    [106]
    Z. Chen, S. Nie, T. Wu, and C. G. Healey, “High resolution face completion with multiple controllable attributes via fully end-to-end progressive generative adversarial networks,” arXiv preprint arXiv: 1801.07632, 2018.
    [107]
    T. Chen, X. Zhai, M. Ritter, M. Lucic, and N. Houlsby, “Self-supervised gans via auxiliary rotation loss,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, 2019, pp. 12154–12163.
    [108]
    F. Ni, J. Zhang, and M. N. Noori, “Deep learning for data anomaly detection and data compression of a long: Span suspension bridge,” Computer—Aided Civil and Infrastructure Engineering, vol. 35, no. 7, pp. 685–700, 2020. doi: 10.1111/mice.12528
    [109]
    Y. Xu, X. Ouyang, Y. Cheng, et al., “Dual-mode vehicle motion pattern learning for high performance road traffic anomaly detection,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshops, 2018, pp. 145–152.
    [110]
    V. Reddy, C. Sanderson, and B. C. Lovell, “Improved anomaly detection in crowded scenes via cell-based analysis of foreground speed, size and texture,” in Proc. CVPR Workshops, 2011, pp. 55–61.
    [111]
    A. Mohan and S. Poobal, “Crack detection using image processing: A critical review and analysis,” Alexandria Engineering J., vol. 57, no. 2, pp. 787–798, 2018. doi: 10.1016/j.aej.2017.01.020
    [112]
    K. Zhang, Y. Zhang, and H.-D. Cheng, “CrackGAN: Pavement crack detection using partially accurate ground truths based on generative adversarial learning,” IEEE Trans. Intelligent Transportation Systems, vol. 22, no. 2, pp. 1306–1319, 2020.
    [113]
    W. Zhai, J. Zhu, Y. Cao, and Z. Wang, “A generative adversarial network based framework for unsupervised visual surface inspection,” in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, 2018, pp. 1283–1287.
    [114]
    H. Oliveira and P. L. Correia, “CrackIT—An image processing toolbox for crack detection and characterization,” in Proc. IEEE Int. Conf. Image Processing, 2014, pp. 798–802.
    [115]
    Z. Gao, B. Peng, T. Li, and C. Gou, “Generative adversarial networks for road crack image segmentation,” in Proc. Int. Joint Conf. Neural Networks, 2019, pp. 1–8.
    [116]
    Q. Mei and M. Gül, “A cost effective solution for pavement crack inspection using cameras and deep neural networks,” Construction and Building Materials, vol. 256, p. 119397, 2020. doi: 10.1016/j.conbuildmat.2020.119397
    [117]
    G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2017, pp. 4700–4708.
    [118]
    K. Zhang, Y. Zhang, and H. Cheng, “Self-supervised structure learning for crack detection based on cycle-consistent generative adversarial networks,” J. Computing in Civil Engineering, vol. 34, no. 3, p. 04020004, 2020. doi: 10.1061/(ASCE)CP.1943-5487.0000883
    [119]
    J. Mao, H. Wang, and B. F. Spencer Jr, “Toward data anomaly detection for automated structural health monitoring: Exploiting generative adversarial nets and autoencoders,” Structural Health Monitoring, vol. 20, no. 4, pp. 1609–1626, 2021. doi: 10.1177/1475921720924601
    [120]
    K. Lee and D. H. Shin, “Generative model of acceleration data for deep learning-based damage detection for bridges using generative adversarial network,” Journal of KIBIM, vol. 9, no. 1, pp. 42–51, 2019.
    [121]
    P. Yang, W. Jin, and P. Tang, “Anomaly detection of railway catenary based on deep convolutional generative adversarial networks,” in Proc. 3rd IEEE Advanced Information Technology, Electronic and Automation Control Conf., 2018, pp. 1366–1370.
    [122]
    Y. Lyu, Z. Han, J. Zhong, C. Li, and Z. Liu, “A generic anomaly detection of catenary support components based on generative adversarial networks,” IEEE Trans. Instrumentation and Measurement, vol. 69, no. 5, pp. 2439–2448, 2019.
    [123]
    Y. Lyu, Z. Han, J. Zhong, C. Li, and Z. Liu, “A GAN-based anomaly detection method for isoelectric line in high-speed railway,” in Proc. IEEE Int. Instrumentation and Measurement Technology Conf., 2019, pp. 1–6.
    [124]
    L. Xue and S. Gao, “Unsupervised anomaly detection system for railway turnout based on GAN,” in Proc. Journal of Physics: Conf. Series, 2019, vol. 1345, no. 3. p. 032069.
    [125]
    K. Wang, X. Zhang, Q. Hao, Y. Wang, and Y. Shen, “Application of improved least-square generative adversarial networks for rail crack detection by AE technique,” Neurocomputing, vol. 332, pp. 236–248, 2019. doi: 10.1016/j.neucom.2018.12.057
    [126]
    V. Saligrama and Z. Chen, “Video anomaly detection based on local statistical aggregates,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2012, pp. 2112–2119.
    [127]
    S. Akcay, A. Atapour-Abarghouei, and T. P. Breckon, “Ganomaly: Semi-supervised anomaly detection via adversarial training,” in Proc.14th Asian Conf. Computer Proc. Vision, Perth, Australia, Revised Selected Papers, Part III 14, 2019, pp. 622–637.
    [128]
    Y. Sun, W. Yu, Y. Chen, and A. Kadam, "Time series anomaly detection based on GAN," in Proc. 6th Int. Conf. Social Networks Analysis, Management and Security, 2019, pp. 375–382.
    [129]
    S. Akçay, A. Atapour-Abarghouei, and T. P. Breckon, “Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly detection,” in Proc. Int. Joint Conf. Neural Networks, 2019, pp. 1–8.
    [130]
    Y. Qiu, T. Misu, and C. Busso, “Driving anomaly detection with conditional generative adversarial network using physiological and can-bus data,” in Proc. Int. Conf. Multimodal Interaction, 2019, pp. 164–173.
    [131]
    F. Liao, T. Arentze, and H. Timmermans, “Incorporating space–Time constraints and activity-travel time profiles in a multi-state supernetwork approach to individual activity-travel scheduling,” Transportation Research Part B: Methodological, vol. 55, pp. 41–58, 2013. doi: 10.1016/j.trb.2013.05.002
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    Highlights

    • The development of GANs and their existing applications are categorized
    • The applications of GANs in autonomous driving, traffic flow research, and traffic anomaly inspection are classified and demonstrated
    • Challenges and future research directions associated with the integration of GANs into transportation operations are identified

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