Forecasting Day-Ahead Traffic Flow Using Functional Time Series Approach

Ismail Shah (), Izhar Muhammad, Sajid Ali, Saira Ahmed, Mohammed M. A. Almazah and A. Y. Al-Rezami
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Ismail Shah: Department of Statistics, Quaid-i-Azam University, Islamabad 45320, Pakistan
Izhar Muhammad: Department of Statistics, Quaid-i-Azam University, Islamabad 45320, Pakistan
Sajid Ali: Department of Statistics, Quaid-i-Azam University, Islamabad 45320, Pakistan
Saira Ahmed: United Nations Industrial Development Organization, Islamabad 1051, Pakistan
Mohammed M. A. Almazah: Department of Mathematics, College of Sciences and Arts (Muhyil), King Khalid University, Muhyil 61421, Saudi Arabia
A. Y. Al-Rezami: Mathematics Department, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia

Mathematics, 2022, vol. 10, issue 22, 1-16

Abstract: Nowadays, short-term traffic flow forecasting has gained increasing attention from researchers due to traffic congestion in many large and medium-sized cities that pose a serious threat to sustainable urban development. To this end, this research examines the forecasting performance of functional time series modeling to forecast traffic flow in the ultra-short term. An appealing feature of the functional approach is that unlike other methods, it provides information over the whole day, and thus, forecasts can be obtained for any time within a day. Within this approach, a Functional AutoRegressive (FAR) model is used to forecast the next-day traffic flow. For empirical analysis, the traffic flow data of Dublin airport link road, Ireland, collected at a fifteen-minute interval from 1 January 2016 to 30 April 2017, are used. The first twelve months are used for model estimation, while the remaining four months are for the one-day-ahead out-of-sample forecast. For comparison purposes, a widely used model, namely AutoRegressive Integrated Moving Average (ARIMA), is also used to obtain the forecasts. Finally, the models’ performances are compared based on different accuracy statistics. The study results suggested that the functional time series model outperforms the traditional time series models. As the proposed method can produce traffic flow forecasts for the entire next day with satisfactory results, it can be used in decision making by transportation policymakers and city planners.

Keywords: traffic flow forecasting; autoregressive; functional time series; Dublin airport link road; short-term prediction; functional data analysis; ARIMA (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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