Prediction and modeling of traffic flow of human-driven vehicles at a signalized road intersection using artificial neural network model: A South African road transportation system scenario

Olayode, Isaac Oyeyemi; Tartibu, Lagouge K.; Okwu, Modestus O.

doi:10.1016/j.treng.2021.100095

Cited by 35 publications

(15 citation statements)

References 68 publications

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“…An ANN is a non-parametric model in artificial intelligence and is a powerful tool for dealing with the problems of regression and classification in uncertain situations [ 51 ]. In recent years, ANN models have been widely used and have successfully solved many problems encountered in practical engineering applications [ 25 , 51 , 52 , 53 ], showing good intelligent characteristics. The principle and performance evaluation of an ANN model are described in further detail in relevant studies such as [ 51 , 52 ].…”

Section: Methodsmentioning

confidence: 99%

“…In recent years, ANN models have been widely used and have successfully solved many problems encountered in practical engineering applications [ 25 , 51 , 52 , 53 ], showing good intelligent characteristics. The principle and performance evaluation of an ANN model are described in further detail in relevant studies such as [ 51 , 52 ].…”

Section: Methodsmentioning

confidence: 99%

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Estimation of the Mixed Layer Depth in the Indian Ocean from Surface Parameters: A Clustering-Neural Network Method

Yue

et al. 2022

Sensors

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The effective estimation of mixed-layer depth (MLD) plays a significant role in the study of ocean dynamics and global climate change. However, the methods of estimating MLD still have limitations due to the sparse resolution of the observed data. In this study, a hybrid estimation method that combines the K-means clustering algorithm and an artificial neural network (ANN) model was developed using sea-surface parameter data in the Indian Ocean as a case study. The oceanic datasets from January 2012 to December 2019 were obtained via satellite observations, Argo in situ data, and reanalysis data. These datasets were unified to the same spatial and temporal resolution (1° × 1°, monthly). Based on the processed datasets, the K-means classifier was applied to divide the Indian Ocean into four regions with different characteristics. For ANN training and testing in each region, the gridded data of 84 months were used for training, and 12-month data were used for testing. The ANN results show that the optimized NN architecture comprises five input variables, one output variable, and four hidden layers, each of which has 40 neurons. Compared with the multiple linear regression model (MLR) with a root-mean-square error (RMSE) of 5.2248 m and the HYbrid-Coordinate Ocean Model (HYCOM) with an RMSE of 4.8422 m, the RMSE of the model proposed in this study was reduced by 27% and 22%, respectively. Three typical regions with high variability in their MLDs were selected to further evaluate the performance of the ANN model. Our results showed that the model could reveal the seasonal variation trend in each of the selected regions, but the estimation accuracy showed room for improvement. Furthermore, a correlation analysis between the MLD and input variables showed that the surface temperature and salinity were the main influencing factors of the model. The results of this study suggest that the pre-clustering ANN method proposed could be used to estimate and analyze the MLD in the Indian Ocean. Moreover, this method can be further expanded to estimate other internal parameters for typical ocean regions and to provide effective technical support for ocean researchers when studying the variability of these parameters.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Estimation of the Mixed Layer Depth in the Indian Ocean from Surface Parameters: A Clustering-Neural Network Method

Yue

et al. 2022

Sensors

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“…Comparing the two algorithms, it was concluded that ANN is the best-fitting model for estimating the delay of signalized intersections. Olayode et al [23] obtained datasets from traffic data equipment, such as cameras, at seven road intersections of the most congested roads in South Africa, and developed an ANN model that divides these data sets into thirteen inputs and one output. The results showed that the accuracy of the ANN prediction method is effective.…”

Section: Traditional Video Application Methods Over Cvismentioning

confidence: 99%

Deep Learning for Video Application in Cooperative Vehicle-Infrastructure System: A Comprehensive Survey

Dai

2022

Applied Sciences

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Video application is a research hotspot in cooperative vehicle-infrastructure systems (CVIS) which is greatly related to traffic safety and the quality of user experience. Dealing with large datasets of feedback from complex environments is a challenge when using traditional video application approaches. However, the in-depth structure of deep learning has the ability to deal with high-dimensional data sets, which shows better performance in video application problems. Therefore, the research value and significance of video applications over CVIS can be better reflected through deep learning. Firstly, the research status of traditional video application methods and deep learning methods over CVIS were introduced; the existing video application methods based on deep learning were classified according to generative and discriminative deep architecture. Then, we summarized the main methods of deep learning and deep reinforcement learning algorithms for video applications over CVIS, and made a comparative study of their performances. Finally, the challenges and development trends of deep learning in the field were explored and discussed.

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“…Almeida et al [12] used SARIMA model to understand the traffic flow characteristics. Artificial neural network algorithms have also been proposed in [13] [14] for the forecasting approach. The most commonly used algorithms are the Feed-Forward Neural Network (FFNN), the Long Short-Term Memory (LSTM), the Convolutional Neural Network (CNN) and a hybrid LSTM-CNN.…”

Section: Literature Reviewmentioning

confidence: 99%

Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison

2022

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Time series forecasting using historical data is significantly important nowadays. Many fields such as finance, industries, healthcare, and meteorology use it. Profit analysis using financial data is crucial for any online or offline businesses and companies. It helps understand the sales and the profits and losses made and predict values for the future. For this effective analysis, the statistical methods-Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA models (SARIMA), and deep learning method-Long Short-Term Memory (LSTM) Neural Network model in time series forecasting have been chosen. It has been converted into a stationary dataset for ARIMA, not for SARIMA and LSTM. The fitted models have been built and used to predict profit on test data. After obtaining good accuracies of 93.84% (ARIMA), 94.378% (SARIMA) and 97.01% (LSTM) approximately, forecasts for the next 5 years have been done. Results show that LSTM surpasses both the statistical models in constructing the best model.

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Prediction and modeling of traffic flow of human-driven vehicles at a signalized road intersection using artificial neural network model: A South African road transportation system scenario

Cited by 35 publications

References 68 publications

Estimation of the Mixed Layer Depth in the Indian Ocean from Surface Parameters: A Clustering-Neural Network Method

Estimation of the Mixed Layer Depth in the Indian Ocean from Surface Parameters: A Clustering-Neural Network Method

Deep Learning for Video Application in Cooperative Vehicle-Infrastructure System: A Comprehensive Survey

Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison

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