Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMAX Modeling

2001 ◽  
Vol 1776 (1) ◽  
pp. 194-200 ◽  
Author(s):  
Billy M. Williams
Keyword(s):  
Traffic Flow ◽  
Vehicular Traffic ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Vehicular Traffic Flow ◽  
Prediction Evaluation

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.

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