Abstract
Real-time and accurate short-term traffic prediction can effectively improve traffic efficiency, reduce accidents, and facilitate relevant departments to take reasonable traffic guidance measures. Therefore, we propose a two-level data driven model for short-term traffic prediction in an edge computing environment. Firstly, a Deep Belief Network (DBN) is developed to extract the traffic characteristics between the road occupancy and road flow collected by the deployed detectors. Then, we predict the developed future road flow of each road segment based on the output of the DBN, which would be used as one of the inputs of a Hidden Markov Model (HMM). Finally, a HMM is developed to predict the future road speed of each road segment characterizing the statistical relationship between the road flow and road speed. To validate the effectiveness of our proposed model, the data from the Performance Measurement System (PeMS) of the California Department of Transportation is applied. Simulation results show that our proposed model has better prediction performance in short-term traffic prediction than other models.
Highlights
In recent years, the rapid development of urban traffic construction, the increase in the number of motor vehicles and unreasonable traffic guidance have made traffic congestion increasingly serious and traffic accidents continue to increase
As a vital component of Intelligent Transportation System (ITS), the traffic prediction aims at effectively predicting the road flow and road speed for a certain road segment, which requires the communication and processing of a huge amount of traffic data
Duan et al [32] proposed an effective deep hybrid neural network based on Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) structures to improve urban traffic flow prediction and taxi Global Positioning System (GPS) tracking
Summary
The rapid development of urban traffic construction, the increase in the number of motor vehicles and unreasonable traffic guidance have made traffic congestion increasingly serious and traffic accidents continue to increase. The time series analysis is based on the historical sensor data, and the short-term traffic prediction for the road is realized by finding the law how the traffic flow varies with time. Duan et al [32] proposed an effective deep hybrid neural network based on Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) structures to improve urban traffic flow prediction and taxi GPS tracking. We propose a two-level data-driven short-term traffic prediction model referred to as DBN-HMM model in 5G-enabled edge computing environment. In the first level of model, we use DBN to effectively extract the traffic characteristics between the road occupancy and road flow, and use the prediction results as the input data of the second-level network.
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