Traffic Volatility Forecasting Using an Omnibus Family GARCH Modeling Framework
Traffic volatility modeling has been highly valued in recent years because of its advantages in describing the uncertainty of traffic flow during the short-term forecasting process. A few generalized autoregressive conditional heteroscedastic (GARCH) models have been developed to capture and hence f...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/10/1392 |
| _version_ | 1797473421070696448 |
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| author | Jishun Ou Xiangmei Huang Yang Zhou Zhigang Zhou Qinghui Nie |
| author_facet | Jishun Ou Xiangmei Huang Yang Zhou Zhigang Zhou Qinghui Nie |
| author_sort | Jishun Ou |
| collection | DOAJ |
| description | Traffic volatility modeling has been highly valued in recent years because of its advantages in describing the uncertainty of traffic flow during the short-term forecasting process. A few generalized autoregressive conditional heteroscedastic (GARCH) models have been developed to capture and hence forecast the volatility of traffic flow. Although these models have been confirmed to be capable of producing more reliable forecasts than traditional point forecasting models, the more or less imposed restrictions on parameter estimations may make the asymmetric property of traffic volatility be not or insufficiently considered. Furthermore, the performance of the models has not been fully evaluated and compared in the traffic forecasting context, rendering the choice of the models dilemmatic for traffic volatility modeling. In this study, an omnibus traffic volatility forecasting framework is proposed, where various traffic volatility models with symmetric and asymmetric properties can be developed in a unifying way by fixing or flexibly estimating three key parameters, namely the Box-Cox transformation coefficient <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>λ</mi></semantics></math></inline-formula>, the shift factor <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>b</mi></semantics></math></inline-formula>, and the rotation factor <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>c</mi></semantics></math></inline-formula>. Extensive traffic speed datasets collected from urban roads of Kunshan city, China, and from freeway segments of the San Diego Region, USA, were used to evaluate the proposed framework and develop traffic volatility forecasting models in a number of case studies. The models include the standard GARCH, the threshold GARCH (TGARCH), the nonlinear ARCH (NGARCH), the nonlinear-asymmetric GARCH (NAGARCH), the Glosten–Jagannathan–Runkle GARCH (GJR-GARCH), and the family GARCH (FGARCH). The mean forecasting performance of the models was measured with mean absolute error (MAE) and mean absolute percentage error (MAPE), while the volatility forecasting performance of the models was measured with volatility mean absolute error (VMAE), directional accuracy (DA), kickoff percentage (KP), and average confidence length (ACL). Experimental results demonstrate the effectiveness and flexibility of the proposed framework and provide insights into how to develop and select proper traffic volatility forecasting models in different situations. |
| first_indexed | 2024-03-09T20:14:16Z |
| format | Article |
| id | doaj.art-24be6d78db9e4bcb98562f77683991bc |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T20:14:16Z |
| publishDate | 2022-09-01 |
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| spelling | doaj.art-24be6d78db9e4bcb98562f77683991bc2023-11-24T00:02:53ZengMDPI AGEntropy1099-43002022-09-012410139210.3390/e24101392Traffic Volatility Forecasting Using an Omnibus Family GARCH Modeling FrameworkJishun Ou0Xiangmei Huang1Yang Zhou2Zhigang Zhou3Qinghui Nie4College of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, ChinaCollege of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, ChinaZachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77840, USACollege of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, ChinaCollege of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, ChinaTraffic volatility modeling has been highly valued in recent years because of its advantages in describing the uncertainty of traffic flow during the short-term forecasting process. A few generalized autoregressive conditional heteroscedastic (GARCH) models have been developed to capture and hence forecast the volatility of traffic flow. Although these models have been confirmed to be capable of producing more reliable forecasts than traditional point forecasting models, the more or less imposed restrictions on parameter estimations may make the asymmetric property of traffic volatility be not or insufficiently considered. Furthermore, the performance of the models has not been fully evaluated and compared in the traffic forecasting context, rendering the choice of the models dilemmatic for traffic volatility modeling. In this study, an omnibus traffic volatility forecasting framework is proposed, where various traffic volatility models with symmetric and asymmetric properties can be developed in a unifying way by fixing or flexibly estimating three key parameters, namely the Box-Cox transformation coefficient <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>λ</mi></semantics></math></inline-formula>, the shift factor <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>b</mi></semantics></math></inline-formula>, and the rotation factor <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>c</mi></semantics></math></inline-formula>. Extensive traffic speed datasets collected from urban roads of Kunshan city, China, and from freeway segments of the San Diego Region, USA, were used to evaluate the proposed framework and develop traffic volatility forecasting models in a number of case studies. The models include the standard GARCH, the threshold GARCH (TGARCH), the nonlinear ARCH (NGARCH), the nonlinear-asymmetric GARCH (NAGARCH), the Glosten–Jagannathan–Runkle GARCH (GJR-GARCH), and the family GARCH (FGARCH). The mean forecasting performance of the models was measured with mean absolute error (MAE) and mean absolute percentage error (MAPE), while the volatility forecasting performance of the models was measured with volatility mean absolute error (VMAE), directional accuracy (DA), kickoff percentage (KP), and average confidence length (ACL). Experimental results demonstrate the effectiveness and flexibility of the proposed framework and provide insights into how to develop and select proper traffic volatility forecasting models in different situations.https://www.mdpi.com/1099-4300/24/10/1392traffic volatilityasymmetric propertyomnibus family GARCH modelshort-term traffic flow forecastingtraffic reliability |
| spellingShingle | Jishun Ou Xiangmei Huang Yang Zhou Zhigang Zhou Qinghui Nie Traffic Volatility Forecasting Using an Omnibus Family GARCH Modeling Framework Entropy traffic volatility asymmetric property omnibus family GARCH model short-term traffic flow forecasting traffic reliability |
| title | Traffic Volatility Forecasting Using an Omnibus Family GARCH Modeling Framework |
| title_full | Traffic Volatility Forecasting Using an Omnibus Family GARCH Modeling Framework |
| title_fullStr | Traffic Volatility Forecasting Using an Omnibus Family GARCH Modeling Framework |
| title_full_unstemmed | Traffic Volatility Forecasting Using an Omnibus Family GARCH Modeling Framework |
| title_short | Traffic Volatility Forecasting Using an Omnibus Family GARCH Modeling Framework |
| title_sort | traffic volatility forecasting using an omnibus family garch modeling framework |
| topic | traffic volatility asymmetric property omnibus family GARCH model short-term traffic flow forecasting traffic reliability |
| url | https://www.mdpi.com/1099-4300/24/10/1392 |
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