A hybrid autoregressive fractionally integrated moving average and nonlinear autoregressive neural network model for short-term traffic flow prediction

Xu, Xuecai; Jin, Xiaofei; Xiao, Daiquan; Ma, Changxi; Wong, Stephen C. P.

doi:10.1080/15472450.2021.1977639

Cited by 23 publications

(10 citation statements)

References 51 publications

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“…That is, deviations or errors will be converted into absolute values. For calculate the error or error value from forecasting, what needs to be done is to take actual data deducted by forecasting data that has been done previously [9].…”

Section: Single Moving Average Methodsmentioning

confidence: 99%

Computer Sales Forecasting System Application Using Web-Based Single Moving Average Method

Permatahati¹,

Muqorobin²

2022

IJCIS

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Abstract—Hamas Com Computer Store is one of the computer shops in the city of Surakarta. There is a change in the turnover of toy sales every year, management management in managing the store's financial statements. hence the need for a system that can forecast sales of goods for the coming year. The purpose of this research is to design a computer goods sales forecasting system by applying the Single Moving Average method. This method was chosen because it has the ability to observe, look for the average value as a forecast for the future period. Data collection techniques in this research are observation (observation), interviews (interviews) and literature study. In the design of this system is made with Context Diagram, HIPO, DAD, relations between tables, database design and UML. This application is made using the PHP programming language and the database uses MySQL. The final result is a sales data report and sales prediction results. The results of the tracking signal control chart error test results show a value of no more than 4 so the system is declared good.

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Section: Single Moving Average Methodsmentioning

confidence: 99%

Computer Sales Forecasting System Application Using Web-Based Single Moving Average Method

Permatahati¹,

Muqorobin²

2022

IJCIS

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“…Grey CNN outperformed the other models like ANN, ARIMA, and GRU. Xuecai et al [13] proposed a hybrid model for trafc fow prediction. Te Autoregressive Fractionally Integrated Moving Average (ARFIMA) model is integrated with Nonlinear Auto-Regressive (NAR) for trafc fow prediction.…”

Section: Hybrid Modelsmentioning

confidence: 99%

Aquila Optimizer-Based Hybrid Predictive Model for Traffic Congestion in an IoT-Enabled Smart City

Chahal,

Gulia,

Gill

et al. 2024

International Journal of Intelligent Systems

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Effective traffic congestion prediction is need of the hour in a modern smart city to save time and improve the quality of life for citizens. In this study, AB_AO (ARIMA Bi-LSTM using Aquila optimizer), a hybrid predictive model, is proposed using the most effective time-series data prediction statistical model ARIMA (Autoregressive Integrated Moving Average) and sequential predictive Deep Learning (DL) technique LSTM (Long Short-Term Memory) which helps in traffic congestion prediction with a minimum error rate. Also, the Aquila optimizer (AO) is used to elevate the adequacy of the AB_AO model. Three road traffic datasets of different cities from the “CityPulse EU FP7 project” are used to implement the proposed hybrid model. In a time-series dataset, two components need to be handled with care, i.e., linear and nonlinear. In this study, the ARIMA model has been used to manage linear components and Bi-LSTM is used to handle nonlinear components of the time-series dataset. The Aquila Optimizer (AO) is used for hyperparametric tuning to enhance the performance of Bi-LSTM. Error measurement parameters like the Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE) are used to validate the results. A detailed mathematical and empirical analysis is given to justify the performance of the AB_AO model using an ablation study and comparative analysis. The AB_AO model acquires more stable and precise results with MSE as 18.78, MAE as 3.18, and MAPE as 0.21 than other models. It may further help to predict the vehicle count on the road, which may be of great help in reducing wastage of time in traffic congestion.

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“…Auto-Regressive Integrated Moving Average (ARIMA) is expressed as ARIMA (p,d,q) where (AR) is autoregressive, (I) is the integration (MA) is the moving average, (p) an autoregressive term, (p) denotes the number of autoregressive orders, (d) specifies the order of differentiation applied to the series to estimate model, and (q) specifies the order of moving average parts [31,32]. This time series method depends on the assumption that the series is stationary; stationary means that the series is free of periodic fluctuations; therefore, if the series is not stationary, it should be made stationary before developing the forecasting model.…”

Section: Arima Modelmentioning

confidence: 99%

Forecasting the Traffic Flow by Using ARIMA and LSTM Models: Case of Muhima Junction

Katambire,

Musabe,

Uwitonze

et al. 2023

Forecasting

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Traffic operation efficiency is greatly impacted by the increase in travel demand and the increase in vehicle ownership. The continued increase in traffic demand has rendered the importance of controlling traffic, especially at intersections. In general, the inefficiency of traffic scheduling leads to traffic congestion, resulting in a rise in fuel consumption, exhaust emissions, and poor quality of service. Various methods for time series forecasting have been proposed for adaptive and remote traffic control. The prediction of traffic has attracted profound attention for improving the reliability and efficiency of traffic flow scheduling while reducing congestion. Therefore, in this work, we studied the problem of the current traffic situation at Muhima Junction one of the busiest junctions in Kigali city. Future traffic rates were forecasted by employing long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA) models, respectively. Both the models’ performance criteria for adequacy were the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). The results revealed that LSTM is the best-fitting model for monthly traffic flow prediction. Within this analysis, we proposed an adaptive traffic flow prediction that builds on the features of vehicle-to-infrastructure communication and the Internet of Things (IoT) to control traffic while enhancing the quality of service at the junctions. The real-time actuation of traffic-responsive signal control can be assured when real-time traffic-based signal actuation is reliable.

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A hybrid autoregressive fractionally integrated moving average and nonlinear autoregressive neural network model for short-term traffic flow prediction

Cited by 23 publications

References 51 publications

Computer Sales Forecasting System Application Using Web-Based Single Moving Average Method

Computer Sales Forecasting System Application Using Web-Based Single Moving Average Method

Aquila Optimizer-Based Hybrid Predictive Model for Traffic Congestion in an IoT-Enabled Smart City

Forecasting the Traffic Flow by Using ARIMA and LSTM Models: Case of Muhima Junction

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