Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction

Abduljabbar, Rusul L.; Dia, Hussein; Tsai, Pei‐Wei; Liyanage, Sohani

doi:10.3390/futuretransp1010003

Cited by 19 publications

(14 citation statements)

References 39 publications

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“…Even though RNNs provide good accuracy, they have been found to underperform for long-term memory as RNNs are unable to use information from the distant past. Also, LSTM can learn patterns with long dependencies when compared with traditional RNNs 70 . The inclusion of additional training has resulted in some model extensions of LSTM known as Bidirectional LSTM (BiLSTM).…”

Section: Methodsmentioning

confidence: 99%

Development and evaluation of bidirectional LSTM freeway traffic forecasting models using simulation data

Abduljabbar

Dia

Tsai

2021

Sci Rep

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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 min 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.

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Section: Methodsmentioning

confidence: 99%

Development and evaluation of bidirectional LSTM freeway traffic forecasting models using simulation data

Abduljabbar

Dia

Tsai

2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Even though RNNs provide good accuracy, they have been found to underperform for long-term memory as RNNs are unable to use information from the distant past. Also, LSTM can learn patterns with long dependencies when compared with traditional RNNs [48]. The inclusion of additional training has resulted in some model extensions of LSTM known as Bidirectional LSTM (BiLSTM).…”

Section: ) Modelling Frameworkmentioning

confidence: 99%

“…from the distant past. Also, LSTM can learn patterns with long dependencies when compared with traditional RNNs[48]. The inclusion of additional training has resulted in some model extensions of LSTM known as Bidirectional LSTM (BiLSTM).…”

mentioning

confidence: 99%

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

Abduljabbar

Dia

Tsai

2021

Preprint

Self Cite

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.

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“…To calculate the model predicted values for short-long term, the simple structure and operation of an LSTM cell has been presented in Fig. 4 of the literature [72], comprising of the following 4 stages [73]: -…”

Section: Lstm Model Setupmentioning

confidence: 99%

Assessment of LSTM, Conv2D and ConvLSTM2D Prediction Models for Long-Term Wind Speed and Direction Regression Analysis

Olaofe

2021

Preprint

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This paper assessed the model performance accuracies of 3 forecast-based architectures (Long Short-Term Memory, LSTM; Convolutional Neural Network, Conv2D and hybrid ConvLSTM2D) for multivariate inputs to multi-steps wind speed and direction forecasts. These high-level neural network-based architectures were setup with the Keras sequential models trained to learn the historical patterns from the processed weather input datasets. To build these forecast models, the sampled time series weather observations at different station heights were obtained and reshaped for network layer compatibility, while the Adamax algorithm for the network optimization was considered. The trained and evaluated model performances with different input data sequences (normalized/un-normalized) were assessed while the forecast results were also compared with the Actual and Conv1D models. Upon optimal network training, the Conv2D model returned MSE, MAE and RMSE estimated values of 0.82, 4.48 and 0.91 %, respectively; the LSTM model returned 1.03, 4.75 and 1.01 %; while the ConvLSTM2D model returned 2.11, 10.13 and 1.45 %, respectively. Also, Conv2D validated model values of 3.16, 14.73 and 1.77 % were obtained %, respectively; 3.21, 14.98 and 1.82, for the LSTM-based; while ConvLSTM2D model returned 3.27, 15.92 and 1.91 %, respectively. Studied finding results show that better prediction and evaluation could be achieved for all the trained model architectures as compared to the untrained models. Also, from the predicted model results, the Keras sequential models were found to be useful for replicating the time-series historical wind speed and direction based on the well-tuned model hyperparameters as well as the input sequence structure

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Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction

Cited by 19 publications

References 39 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

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

Assessment of LSTM, Conv2D and ConvLSTM2D Prediction Models for Long-Term Wind Speed and Direction Regression Analysis

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