An interpretable model for short term traffic flow prediction

Wang, Wei; Zhang, Hanyu; Li, Tong; Guo, Jianhua; Huang, Wei; Wei, Yun; Cao, Jinde

doi:10.1016/j.matcom.2019.12.013

Cited by 37 publications

(24 citation statements)

References 37 publications

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“…e results showed superior LSTM model performance compared to regression models [32]. Similarly, superior model performance has been shown from using LSTM and GRU models when compared to ARIMA and support vector regression (SVR) models for the track flow prediction [33].…”

Section: Literature Reviewmentioning

confidence: 68%

Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction

Abduljabbar

Dia

Tsai

2021

Journal of Advanced Transportation

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This paper presents the development and evaluation of short-term traffic prediction models using unidirectional and bidirectional deep learning long short-term memory (LSTM) neural networks. The unidirectional LSTM (Uni-LSTM) model provides high performance through its ability to recognize longer sequences of traffic time series data. In this work, Uni-LSTM is extended to bidirectional LSTM (BiLSTM) networks which train the input data twice through forward and backward directions. The paper presents a comparative evaluation of the two models for short-term speed and traffic flow prediction using a common dataset of field observations collected from multiple freeways in Australia. The results showed BiLSTM performed better for variable prediction horizons for both speed and flow. Stacked and mixed Uni-LSTM and BiLSTM models were also investigated for 15-minute prediction horizons resulting in improved accuracy when using 4-layer BiLSTM networks. The optimized 4-layer BiLSTM model was then calibrated and validated for multiple prediction horizons using data from three different freeways. The validation results showed a high degree of prediction accuracy exceeding 90% for speeds up to 60-minute prediction horizons. For flow, the model achieved accuracies above 90% for 5- and 10-minute prediction horizons and more than 80% accuracy for 15- and 30-minute prediction horizons. These findings extend the set of AI models available for road operators and provide them with confidence in applying robust models that have been tested and evaluated on different freeways in Australia.

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Section: Literature Reviewmentioning

confidence: 68%

Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction

Abduljabbar

Dia

Tsai

2021

Journal of Advanced Transportation

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“…The combination of LSTM and DBN 59 was used to design the model for lane changing. A hybrid model 60 combined DNN with SARIMA developed for making the short‐term traffic prediction better. Load Balanced Clustering Technique using Genetic Algorithms, 61 Secure and fair cluster head selection protocol, 62 and Energy Efficient clustering protocol 63 can be applied to cluster the traffic data efficiently.…”

Section: Related Workmentioning

confidence: 99%

An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city

Vijayalakshmi¹,

Ramar²,

Jhanjhi

et al. 2020

Int J Communication

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Summary In the development of smart cities, the intelligent transportation system (ITS) plays a major role. The dynamic and chaotic nature of the traffic information makes the accurate forecasting of traffic flow as a challengeable one in ITS. The volume of traffic data increases dramatically. We enter the epoch of big data. Hence, a 1deep architecture is necessary to process, analyze, and inference such a large volume of data. To develop a better traffic flow forecasting model, we proposed an attention‐based convolution neural network long short‐term memory (CNN‐LSTM), a multistep prediction model. The proposed scheme uses the spatial and time‐based details of the traffic data, which are extracted using CNN and LSTM networks to improve the model accuracy. The attention‐based model helps to identify the near term traffic details such as speed that is very important for predicting the future value of flow. The results show that our attention‐based CNN‐LSTM prediction model provides better accuracy in terms of prediction during weekdays and weekend days in the case of peak and nonpeak hours also. We used data from the largest traffic data set the California Department of Transportation (Caltrans) for our prediction work.

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“…Their data contained three features: volume, speed, and occupancy. Wang et al [27] presented an integrated method, combining Group method of data handling (GMDH) and seasonal autoregressive integrated moving average (SARIMA), for traffic flow prediction in the Nanming district of Guiyang, Guizhou province, China. They collected data for five working days; data from the 1st four days were used for training while the last day's data were used for testing.…”

Section: Related Workmentioning

confidence: 99%

Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks

Awan

Minerva

Crespi

2020

Sensors

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Traffic flow forecasting is one of the most important use cases related to smart cities. In addition to assisting traffic management authorities, traffic forecasting can help drivers to choose the best path to their destinations. Accurate traffic forecasting is a basic requirement for traffic management. We propose a traffic forecasting approach that utilizes air pollution and atmospheric parameters. Air pollution levels are often associated with traffic intensity, and much work is already available in which air pollution has been predicted using road traffic. However, to the best of our knowledge, an attempt to improve forecasting road traffic using air pollution and atmospheric parameters is not yet available in the literature. In our preliminary experiments, we found out the relation between traffic intensity, air pollution, and atmospheric parameters. Therefore, we believe that addition of air pollutants and atmospheric parameters can improve the traffic forecasting. Our method uses air pollution gases, including C O , N O , N O 2 , N O x , and O 3 . We chose these gases because they are associated with road traffic. Some atmospheric parameters, including pressure, temperature, wind direction, and wind speed have also been considered, as these parameters can play an important role in the dispersion of the above-mentioned gases. Data related to traffic flow, air pollution, and the atmosphere were collected from the open data portal of Madrid, Spain. The long short-term memory (LSTM) recurrent neural network (RNN) was used in this paper to perform traffic forecasting.

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An interpretable model for short term traffic flow prediction

Cited by 37 publications

References 37 publications

Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction

Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction

An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city

Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks

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