scholarly journals TrafficWave: Generative Deep Learning Architecture for Vehicular Traffic Flow Prediction

2019 ◽  
Vol 9 (24) ◽  
pp. 5504 ◽  
Author(s):  
Donato Impedovo ◽  
Vincenzo Dentamaro ◽  
Giuseppe Pirlo ◽  
Lucia Sarcinella
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Short Term Memory ◽  
Vehicular Traffic ◽  
Traffic Flow Prediction ◽  
Critical Areas ◽  
Flow Prediction ◽  
Vehicular Traffic Flow ◽  
Day Of The Week ◽  
Gated Recurrent Unit

Vehicular traffic flow prediction for a specific day of the week in a specific time span is valuable information. Local police can use this information to preventively control the traffic in more critical areas and improve the viability by decreasing, also, the number of accidents. In this paper, a novel generative deep learning architecture for time series analysis, inspired by the Google DeepMind’ Wavenet network, called TrafficWave, is proposed and applied to traffic prediction problem. The technique is compared with the most performing state-of-the-art approaches: stacked auto encoders, long–short term memory and gated recurrent unit. Results show that the proposed system performs a valuable MAPE error rate reduction when compared with other state of art techniques.

scholarly journals Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yuhan Jia ◽  
Jianping Wu ◽  
Ming Xu
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Short Term Memory ◽  
Complex Model ◽  
Urban Traffic ◽  
Environmental Data ◽  
Traffic Modeling ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
The Impact

Accurate traffic flow prediction is increasingly essential for successful traffic modeling, operation, and management. Traditional data driven traffic flow prediction approaches have largely assumed restrictive (shallow) model architectures and do not leverage the large amount of environmental data available. Inspired by deep learning methods with more complex model architectures and effective data mining capabilities, this paper introduces the deep belief network (DBN) and long short-term memory (LSTM) to predict urban traffic flow considering the impact of rainfall. The rainfall-integrated DBN and LSTM can learn the features of traffic flow under various rainfall scenarios. Experimental results indicate that, with the consideration of additional rainfall factor, the deep learning predictors have better accuracy than existing predictors and also yield improvements over the original deep learning models without rainfall input. Furthermore, the LSTM can outperform the DBN to capture the time series characteristics of traffic flow data.

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