# Generative Adversarial Networks in Business and Social Science
## 4 Statistics on GAN Publications in Economics
### Logistic Transport  
In the field of Logistic Transport, there are several papers available (see Table 7).  
These studies address the challenge of anticipating potential accidents. However, they encounter imbalanced and limited datasets, leading to a low incident-detection rate and a high false-alarm rate in detection models. In this context, the study by [209] proposes a new incident detection framework based on GANs. Experimental results demonstrate a significant enhancement in the detection rate and a reduction in false-alarm rates.  
In the paper by [210], the issue of requiring a large labelled dataset regarding travel modes to avoid congestion is addressed. Often, these travel modes are unbalanced in their representation. Hence, the authors develop a hybrid travel-mode detection model using neural networks and GANs. The GANs are employed to augment the size of the dataset and balance it, thereby enhancing the accuracy of the detection model.  
\begin{table}
\begin{tabular}{l l} \hline
**Year** & **Title** \\ \hline
2021 & Artificial intelligence in the fashion industry: consumer responses to generative adversarial network (GAN) technology. \\
2021 & The rise of deepfakes: A conceptual framework and research agenda for marketing. \\
2022 & Ad creative generation using reinforced generative adversarial network. \\
2022 & Preparing for an era of deepfakes and AI-generated ads: A framework for understanding responses to manipulated advertising. \\ How deepfakes and artificial intelligence could reshape the advertising industry: The coming reality of AI fakes and their potential impact on consumer behavior. \\
2022 & Using deep learning to overcome privacy and scalability issues in customer data transfer. \\
2023 & Product aesthetic design: A machine learning augmentation. Towards privacy-preserving digital marketing: an integrated framework for user modeling using deep learning on a data monetization platform. \\ \hline \end{tabular}
\end{table}
Table 6: WOS publications on Marketing and Publicity.  
On the other hand, in order to address the concern of predicting collisions in real-time, Ref. [211] proposes a new data architecture inspired by images that is capable of capturing the microscopic scene in which vehicular interactions occur. For this purpose, an accident prediction model is constructed using multi-input convolutional neural networks and by employing various oversampling methodologies to balance the training data. The authors ascertain that the best results are achieved using deep convolutional generative adversarial networks (DCGANs) with random undersampling.  
The same topic is addressed in [212], but from the perspective of spatio-temporal transferability. A combination of a GAN and transfer learning is used to examine the transferability of real-time collision prediction models in an extremely imbalanced data environment. The practical application involves using a Wasserstein GAN (WGAN) to generate synthetic collision data since the initial dataset had an extreme imbalance between collision and non-collision data. The study revealed that direct transferability is not feasible. However, the models become spatio-temporally transferable in terms of time and space when transfer learning is applied.  
The last study obtained in this field, by [213], proposes a MoGAN model to generate realistic urban mobility networks. This is applied to public bicycle and taxi travel data and demonstrates increased realism compared to classical gravity and radiation models. Moreover, it can be used for data augmentation, simulations, and hypothetical analyses.