TrafficWave: Generative Deep Learning Architecture for Vehicular Traffic Flow Prediction

Abstract

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.

MF-TCPV: A Machine Learning and Fuzzy Comprehensive Evaluation-Based Framework for Traffic Congestion Prediction and Visualization

“…The method considers the number of lanes, the time ratio of traffic lights, whether to turn left, and other information to obtains better results. Besides LSTM and DBN, deep learning algorithms such as CNN and GRU had also been applied to feature extraction [34], [35].…”

Artificial Intelligence Applications to Smart City and Smart Enterprise

“…A novel generative deep learning architecture, called TrafficWave, is proposed by Impedovo et al [2] and applied to vehicular traffic prediction. The technique is compared with the best performing state-of-the-art approaches: stacked auto encoders, long short-term memory, and gated recurrent unit.…”

Model Evaluation for Forecasting Traffic Accident Severity in Rainy Seasons Using Machine Learning Algorithms: Seoul City Study

“…Several other studies have been performed to forecast fundamental traffic parameters including speed, volume, and density [27,28] through deep learning approaches. There was also an attempt to forecast bus ridership at the stop and stop-to-stop levels and vehicle traffic flow prediction including delay with the development of a deep learning architecture [29][30][31]. In addition, a rainfall prediction model was developed by a machine learning algorithm [32].…”