Deep Bidirectional and Unidirectional LSTM Neural Networks in Traffic Flow Forecasting from Environmental Factors

Kouziokas, Georgios N.

doi:10.1007/978-3-030-61075-3_17

Cited by 6 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reference [29] captured complex spatial-temporal correlation by using BiLSTM model for traffic flow prediction. Likewise, traffic flow-related environmental factors were taken into consideration to improve the accuracy of traffic flow prediction using BiLSTM models [30]. Other research also demonstrated an improved traffic flow prediction accuracy when using this model under connected and automated vehicle environments [31,32].…”

Section: Literature Reviewmentioning

confidence: 99%

Development and Evaluation of Bidirectional LSTM Freeway Traffic Forecasting Models Using Simulation Data

Abduljabbar

Dia

Tsai

2021

Preprint

View full text Add to dashboard Cite

Long short-term memory (LSTM) models provide high predictive performance through their ability to recognize longer sequences of time series data. More recently, bidirectional deep learning models (BiLSTM) have extended the LSTM capabilities by training the input data twice in forward and backward directions. In this paper, BiLSTM short term traffic forecasting models have been developed and evaluated using data from a calibrated micro-simulation model for a congested freeway in Melbourne, Australia. The simulation model was extensively calibrated and validated to a high degree of accuracy using field data collected from 55 detectors on the freeway. The base year simulation model was then used to generate loop detector data including speed, flow and occupancy which were used to develop and compare a number of LSTM models for short-term traffic prediction up to 60 minutes into the future. The modelling results showed that BiLSTM outperformed other predictive models for multiple prediction horizons for base year conditions. The simulation model was then adapted for future year scenarios where the traffic demand was increased by 25-100 percent to reflect potential future increases in traffic demands. The results showed superior performance of BiLSTM for multiple prediction horizons for all traffic variables.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Development and Evaluation of Bidirectional LSTM Freeway Traffic Forecasting Models Using Simulation Data

Abduljabbar

Dia

Tsai

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…This method utilized the spatial dimension characteristics of traffic flow data and achieved ideal prediction results. Kouziokas (2020) proposed an long short-term memory (LSTM)-based algorithm for traffic flow data prediction. The algorithm took full advantage of the features of LSTM to better extract the temporal features of traffic flow data, and then achieved the prediction of traffic flow data.…”

Section: Introductionmentioning

confidence: 99%

A novel traffic optimization method using GRU based deep neural network for the IoV system

Wen

Yang³

2023

PeerJ Computer Science

View full text Add to dashboard Cite

At present, China is moving towards the direction of “Industry 4.0”. The development of the automobile industry, especially intelligent automobiles, is in full swing, which brings great convenience to people’s life and travel. However, at the same time, urban traffic pressure is also increasingly prominent, and the situation of traffic congestion and traffic safety is not optimistic. In this context, the Internet of Vehicles (also known as “IoV”) opens up a new way to relieve urban traffic pressure. Therefore, in order to further optimize the road network traffic conditions in the IoV environment, this research focuses on the traffic flow prediction algorithm on the basis of deep learning to enhance traffic efficiency and safety. First, the study investigates the short-time traffic flow prediction by combining the characteristics of the IoV environment. To address the issues that existing algorithms cannot automatically extract data features and the model expression capability is weak, the study chooses to build a deep neural network using GRU model in deep learning for short-time traffic flow prediction, thereby improving the prediction accuracy of algorithm. Secondly, a fine-grained traffic flow statistics approach suitable for the IoV situation is suggested in accordance with the deep learning model that was built. The algorithm sends the vehicle characteristic data obtained through GRU model training into the fine-grained traffic flow statistics algorithm, so as to realize the statistics of traffic information of various types of vehicles. The advantage of this algorithm is that it can well count the traffic flow of multiple lanes, so as to better predict the current traffic status and achieve traffic optimization. Finally, the IoV environment is constructed to confirm the effectiveness of the prediction model. The prediction results prove that the new algorithm has good performance in traffic flow statistics in different scenarios.

show abstract

STTD: spatial-temporal transformer with double recurrent graph convolutional cooperative network for traffic flow prediction

Zeng,

Cui,

Huang

et al. 2024

Cluster Comput

View full text Add to dashboard Cite

Deep Bidirectional and Unidirectional LSTM Neural Networks in Traffic Flow Forecasting from Environmental Factors

Cited by 6 publications

References 30 publications

Development and Evaluation of Bidirectional LSTM Freeway Traffic Forecasting Models Using Simulation Data

Development and Evaluation of Bidirectional LSTM Freeway Traffic Forecasting Models Using Simulation Data

A novel traffic optimization method using GRU based deep neural network for the IoV system

STTD: spatial-temporal transformer with double recurrent graph convolutional cooperative network for traffic flow prediction

Contact Info

Product

Resources

About