Abstract
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.
Highlights
This study evaluated BiLSTM performance on future traffic scenarios when the traffic demand increased by 25%, 50%, 75% and 100%
The results showed that BiLSTM is capable of prediction even if traffic demand increases by up to 100% in the future
Speed and occupancy, prediction accuracies were above 92% for all scenarios for a prediction horizon up to 60 min into the future
Summary
Short-term Traffic prediction plays an important role in the success of Intelligent Transport Systems (ITS) for travel information systems, adaptive traffic management systems, public transportation scheduling and commercial vehicle o perations[1,2,11]. [25], compared Convolutional-LSTM models against BiLSTM models and showed that they provided better accuracy for traffic flow prediction. Other authors developed an end to-end deep learning with 1 BiLSTM layer for future traffic flow prediction, and the results showed that the model was capable of solving stochastic flow characteristics and overcoming overfitting p roblems[43]. Multiple layers of BiLSTM and LSTM models were investigated to predict network wide traffic speeds resulting in superior performance compared to other models[44]. A long short-term memorygenetic algorithm support vector regression (LSTMGASVR) algorithm was investigated to predict future traffic flows with a superior performance in comparison to other m odels[50]. This paper tests the model on multiple prediction horizons on multiple traffic variables such as speed, flow and occupancy using data generated from a calibrated freeway model which hasn’t been established in any previous literature on the topic
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