An improved deep belief network for traffic prediction considering weather factors

The timely access to accurate traffic data is essential to the development of intelligent traffic systems. However, the existing traffic prediction methods cannot achieve satisfactory results, mainly because of three factors: the structure is too simple to extract deep features; many external factors are overlooks, such as weather and traffic incidents; the nonlinearity of traffic flow is not well handled. To solve the problem, this paper improves the deep belief network (DBN), a deep learning method, for accurate traffic prediction under poor weather. Firstly, the data of poor weather and traffic data were collected from IoV, rather than induction coils in traditional methods. Next, the support vector regression (SVR) was introduced to improve the classic DBN. In the improved DBN, the underlying structure is a traditional DBN that learns the key features of traffic data in an unsupervised manner, and the top layer is an SVR that performs supervised traffic prediction. To verify its effectiveness, the improved DBN was applied to predict the traffic data based on the traffic data from the control center of an expressway and the weather data from local monitoring stations, in comparison with the autoregressive integrated moving average (ARIMA) model and the traditional neural network. The experimental results show that the improved DBN controlled the traffic prediction error within 9%, and maintained good robustness despite the extension of the time interval. To sum up, this paper provides an effective way to predict traffic flow under poor weather, shedding new light on the application of deep learning in traffic prediction. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University.

Automated summary

This paper presents an approach to accurate traffic prediction under poor weather conditions by improving the deep belief network (DBN) and integrating support vector regression (SVR). Experimental results demonstrate that the improved DBN effectively controls prediction errors and maintains robustness.