Short-Term Traffic Flow Prediction Using Variational LSTM Networks

2020/02/18 by Mehrdad Farahani, Farahani, Mehrdad, Marzieh Farahani +6 · 1 citation
Computer Science · Engineering · Social Sciences · #Traffic Prediction and Management Techniques #Traffic control and management #Transportation Planning and Optimization #cs.LG #cs.NE #eess.SP

paper · pdf · doi:10.48550/arxiv.2002.07922

18 pages, 13 figures

arxiv created 2020/02/18 · arxiv updated 2020/02/20

Abstract

Traffic flow characteristics are one of the most critical decision-making and traffic policing factors in a region. Awareness of the predicted status of the traffic flow has prime importance in traffic management and traffic information divisions. The purpose of this research is to suggest a forecasting model for traffic flow by using deep learning techniques based on historical data in the Intelligent Transportation Systems area. The historical data collected from the Caltrans Performance Measurement Systems (PeMS) for six months in 2019. The proposed prediction model is a Variational Long Short-Term Memory Encoder in brief VLSTM-E try to estimate the flow accurately in contrast to other conventional methods. VLSTM-E can provide more reliable short-term traffic flow by considering the distribution and missing values.

Citations

• Auto-Encoding Variational Bayes
• Road traffic accidents prediction modelling: An analysis of Anambra State, Nigeria

Cited by

• Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction

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