TrafficGAN: OffDeployment Traffic Estimation with Traffic Generative Adversarial Networks The rapid progress of urbanization has expedited the process of urban planning, e.g., new residential, commercial areas, which in turn boosts the local travel demand. We propose a novel offdeployment traffic estimation problem, namely, to foresee the traffic condition changes of a region prior to the deployment of a construction plan. This problem is important to city planners to evaluate and develop urban deployment plans. However, this task is challenging. Traditional traffic estimation approaches lack the ability to solve this problem, since no data about the impact can be collected before the deployment and old data fails to capture the traffic pattern changes. In this paper, we define the offdeployment traffic estimation problem as a traffic generation problem, and develop a novel deep generative model TrafficGAN that captures the shared patterns across spatial regions of how traffic conditions evolve according to travel demand changes and underlying road network structures. In particular, TrafficGAN captures the road network structures through a dynamic filter in the dynamic convolutional layer. We evaluate our TrafficGAN using a largescale traffic data collected from Shenzhen, China. Results show that TrafficGAN can more accurately estimate the traffic conditions compared with all baselines.

Recognizing New Classes with Synthetic Data in the Loop: Application to Traffic Sign Recognition Onboard vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and realworld samples from known classes to perform synthetictoreal domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIATS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio     xe2x88xbc 1  4     for newknown classes; even for more challenging ratios such as     xe2x88xbc 4  1    , the results are also very positive.