Abstract
Traffic prediction is essential for advanced traffic planning, design, management, and network sustainability. Current prediction methods are mostly offline, which fail to capture the real-time variation of traffic flows. This paper establishes a sustainable online generative adversarial network (GAN) by combining bidirectional long short-term memory (BiLSTM) and a convolutional neural network (CNN) as the generative model and discriminative model, respectively, to keep learning with continuous feedback. BiLSTM constantly generates temporal candidate flows based on valuable memory units, and CNN screens out the best spatial prediction by returning the feedback gradient to BiLSTM. Multi-dimensional indicators are selected to map the multi-view fusion local trend for accurate prediction. To balance computing efficiency and accuracy, different batch sizes are pre-tested and allocated to different lanes. The models are trained with rectified adaptive moment estimation (RAdam) by dividing the dataset into the training and testing sets with a rolling time-domain scheme. In comparison with the autoregressive integrated moving average (ARIMA), BiLSTM, generating adversarial network for traffic flow (GAN-TF), and generating adversarial network for non-signal traffic (GAN-NST), the proposed improved generating adversarial network for traffic flow (IGAN-TF) successfully generates more accurate and stable flows and performs better.
Highlights
As a crucial technology in intelligent transportation systems (ITS) and a research hotspot, traffic flow prediction is necessary for a sustainable traffic network
The remainder of this paper is organized as follows: we review the literature on generative adversarial network (GAN)
improved generating adversarial network for traffic flow (IGAN-TF) was compared with the following four benchmarks: 1. autoregressive integrated moving average (ARIMA): Autoregressive integrated moving average is mainly used in time series analysis, such as in the analysis of traffic flows, whose data have non-stationarity
Summary
As a crucial technology in intelligent transportation systems (ITS) and a research hotspot, traffic flow prediction is necessary for a sustainable traffic network. Many deep learning-based models have been proposed to predict traffic volume, and they process the time-series traffic data [1,2]. Distribution, which time series methods are not suitable to handle. The deep learning method is considered based on generative adversarial networks (GANs) [3]. They are promising image generation methods because they generate samples based on their simulated probability distribution of a complex dataset.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.