Abstract
Predicting urban traffic is of great importance to smart city systems and public security; however, it is a very challenging task because of several dynamic and complex factors, such as patterns of urban geographical location, weather, seasons, and holidays. To tackle these challenges, we are stimulated by the deep-learning method proposed to unlock the power of knowledge from urban computing and proposed a deep-learning model based on neural network, entitled Capsules TCN Network, to predict the traffic flow in local areas of the city at once. Capsules TCN Network employs a Capsules Network and Temporal Convolutional Network as the basic unit to learn the spatial dependence, time dependence, and external factors of traffic flow prediction. In specific, we consider some particular scenarios that require accurate traffic flow prediction (e.g., smart transportation, business circle analysis, and traffic flow assessment) and propose a GAN-based superresolution reconstruction model. Extensive experiments were conducted based on real-world datasets to demonstrate the superiority of Capsules TCN Network beyond several state-of-the-art methods. Compared with HA, ARIMA, RNN, and LSTM classic methods, respectively, the method proposed in the paper achieved better results in the experimental verification.
Highlights
Empowered by Internet of Things (IoTs) technologies and advanced algorithms that can collect and handle massive traffic datasets, urban computing and intelligence can make more informed decisions and create feedback loops between actual traffic situation and management department in the urban environment [1]
Traffic forecasting has been a core issue in transportation planning and management, and it has been a major issue in urban computing
We propose a method based on the Capsules Network and Temporal Convolutional Network to predict traffic flow in local areas of the city
Summary
Empowered by Internet of Things (IoTs) technologies and advanced algorithms that can collect and handle massive traffic datasets, urban computing and intelligence can make more informed decisions and create feedback loops between actual traffic situation and management department in the urban environment [1]. It can bridge the gaps between ubiquitous sensing, intelligent computing, cooperative communication, and big data management technologies to create novel solutions which can improve urban traffic environments, quality of life, and smart city systems [2]. We proposed a further improvement method for spatial-temporal data processing to achieve supervision of urban area vehicle density
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