Abstract
With the rapid economic development, urban areas are seeing more and more vehicles, leading to frequent urban traffic congestion. To solve this problem, the forecasting of traffic parameters is essential, in which, road operating speed (hereinafter referred to as “road speed”) is a key parameter for forecasting road congestion. This paper proposes a hybrid radial basis function (RBF) neural network algorithm for forecasting road speed. First, it proposes a fuzzy RBF neural network structure by combining the fuzzy logic system with the RBF neural network. Then, it incorporates factors such as weather, holidays and road grades into the input layer. Considering the uncertainty and sensitivity of the initial centre of the traditional membership function layer, it uses fuzzy C-means clustering to determine the centre and other parameters of the membership function layer. Then using the gradient descent method, it trains the weights between the fuzzy inference layer and the output layer. Finally, this paper trains the proposed hybrid RBF neural network with the traffic road network data and weather data of a city, and uses the trained hybrid neural network to predict the road speed and the congestion status. The prediction results show that, compared with simplex prediction methods, such as BP neural network, time series method, and RBF neural network, the hybrid RBF neural network has a higher forecasting accuracy, with the mean absolute percentage error (MAPE) being reduced to 6.4%. Experimental results verify the accurate forecasting, enhanced learning feature and mapping capability of this method in short-term road speed forecasting, indicating that it can provide reliable predicted values to help solve urban congestion problems.
Highlights
Regional traffic congestion is commonly seen in most cities, especially during rush hours
The rest of this paper is organized as follows: Section 2 analyses road speed in urban traffic flow; Section 3 proposes the hybrid radial basis function (RBF) neural network with a fuzzy logic system; Section 4 designs a short-term road speed forecasting method based on the hybrid RBF neural network; Section 5 gives the experimental results; and Section 6 presents conclusions and prospects
Regarding the forecasting of short-term road speed in urban traffic flow, this paper proposes a hybrid RBF neural network forecasting method by integrating the fuzzy logic system, fuzzy C-means clustering and RBF neural network
Summary
Regional traffic congestion is commonly seen in most cities, especially during rush hours. C. Ai et al.: Short-Term Road Speed Forecasting Based on Hybrid RBF Neural Network short-term traffic prediction include traffic flow, travel time and road speed. Considering the wide application of the fuzzy logic system and the radial basis function neural network in the identification of nonlinear systems, this paper applies them in the forecasting of road speed in short-term traffic flow. The rest of this paper is organized as follows: Section 2 analyses road speed in urban traffic flow; Section 3 proposes the hybrid RBF neural network with a fuzzy logic system; Section 4 designs a short-term road speed forecasting method based on the hybrid RBF neural network; Section 5 gives the experimental results; and Section 6 presents conclusions and prospects. I=1 where, ωi is the connection weight matrix between the hidden layer and the output layer
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
