Forecasting Day-Ahead Traffic Flow Using Functional Time Series Approach

Shah, Ismail; Muhammad, Izhar; Ali, Sajid; Ahmed, Saira; Almazah, Mohammed M. A.; Al-Rezami, A. Y.

doi:10.3390/math10224279

Cited by 23 publications

(11 citation statements)

References 44 publications

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“…MAE, MAPE, and RMSE 2022 [27] ARIMA, Random Walk Forecast, and Deviation from historical average. root mean square error of prediction (RMSEP), mean absolute deviation (MAD) and MAPE 2002 [28] Forecasting day-ahead traffic flow using functional time series approach (FAR) and ARIMA.…”

Section: Mae Mape and Rmse 2022 [26]mentioning

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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Section: Mae Mape and Rmse 2022 [26]mentioning

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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show abstract

“…CNN and LSTM were compared to existing baseline models to determine their effectiveness. Shah, Almazah and Rezami, [25] analyze how well functional time series modeling predicts traffic flow one day in advance. Additionally, researchers compared the developed model FAR (1) with the conventional ARIMA Model.…”

Section: Literature Reviewmentioning

confidence: 99%

Experimental Analysis of Time Series Models for Traffic Flow Prediction

Tiwari,

Prasad

2023

SMSJ

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Traffic flow prediction is a research topic signified by several researchers in a league span of disciplines. In this context, one of the most in-demand techniques of Machine Learning, especially time series-based techniques, helps in predicting traffic flow forecasting and increases the accuracy of the prediction model. In order to deliver extremely precise traffic forecasts, it is crucial that we put the prediction system into practice in the actual world. We aim to perform computations related to traffic on the traffic datasets and determine the accuracy of each model. For this purpose, we are using three distinct time series models: Long Short Term Memory (LSTM), the Autoregressive Integrated Moving Average (ARIMA), and the Seasonal Autoregressive Integrated Moving Average (SARIMA). From the results obtained, it is concluded that the proposed model achieves the highest prediction accuracy with the lowest root mean squared error.

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“…This research contributes to the literature on O 3 forecasting by applying functional data analysis (FDA) methods, which can capture the dynamic and complex features of the O 3 concentration as a function of time. FDA methods have been used in various fields such as bio-statistics, econometrics, and environmental science, but are less explored in the context of O 3 forecasting (Jan et al 2022;Shah et al 2022). This study proposes a novel time series model based on FDA, which treats each day as a single functional observation with 24 discrete points.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Hourly Ozone Concentration Forecasting Using Functional Data Approach

Shah,

Gul,

Ali

et al. 2024

Econometrics

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Air pollution, especially ground-level ozone, poses severe threats to human health and ecosystems. Accurate forecasting of ozone concentrations is essential for reducing its adverse effects. This study aims to use the functional time series approach to model ozone concentrations, a method less explored in the literature, and compare it with traditional time series and machine learning models. To this end, the ozone concentration hourly time series is first filtered for yearly seasonality using smoothing splines that lead us to the stochastic (residual) component. The stochastic component is modeled and forecast using a functional autoregressive model (FAR), where each daily ozone concentration profile is considered a single functional datum. For comparison purposes, different traditional and machine learning techniques, such as autoregressive integrated moving average (ARIMA), vector autoregressive (VAR), neural network autoregressive (NNAR), random forest (RF), and support vector machine (SVM), are also used to model and forecast the stochastic component. Once the forecast from the yearly seasonality component and stochastic component are obtained, both are added to obtain the final forecast. For empirical investigation, data consisting of hourly ozone measurements from Los Angeles from 2013 to 2017 are used, and one-day-ahead out-of-sample forecasts are obtained for a complete year. Based on the evaluation metrics, such as R2, root mean squared error (RMSE), and mean absolute error (MAE), the forecasting results indicate that the FAR outperforms the competitors in most scenarios, with the SVM model performing the least favorably across all cases.

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Forecasting Day-Ahead Traffic Flow Using Functional Time Series Approach

Cited by 23 publications

References 44 publications

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

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

Experimental Analysis of Time Series Models for Traffic Flow Prediction

Short-Term Hourly Ozone Concentration Forecasting Using Functional Data Approach

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