Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models
Accurate prediction of traffic information (i.e., traffic flow, travel time, traffic speed, etc.) is a key component of Intelligent Transportation System (ITS). Traffic speed is an important indicator to evaluate traffic efficiency. Up to date, although a few studies have considered the periodic fea...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/9628957 |
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| _version_ | 1832566241614626816 |
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| author | Xiaoxue Yang Yajie Zou Jinjun Tang Jian Liang Muhammad Ijaz |
| author_facet | Xiaoxue Yang Yajie Zou Jinjun Tang Jian Liang Muhammad Ijaz |
| author_sort | Xiaoxue Yang |
| collection | DOAJ |
| description | Accurate prediction of traffic information (i.e., traffic flow, travel time, traffic speed, etc.) is a key component of Intelligent Transportation System (ITS). Traffic speed is an important indicator to evaluate traffic efficiency. Up to date, although a few studies have considered the periodic feature in traffic prediction, very few studies comprehensively evaluate the impact of periodic component on statistical and machine learning prediction models. This paper selects several representative statistical models and machine learning models to analyze the influence of periodic component on short-term speed prediction under different scenarios: (1) multi-horizon ahead prediction (5, 15, 30, 60 minutes ahead predictions), (2) with and without periodic component, (3) two data aggregation levels (5-minute and 15-minute), (4) peak hours and off-peak hours. Specifically, three statistical models (i.e., space time (ST) model, vector autoregressive (VAR) model, autoregressive integrated moving average (ARIMA) model) and three machine learning approaches (i.e., support vector machines (SVM) model, multi-layer perceptron (MLP) model, recurrent neural network (RNN) model) are developed and examined. Furthermore, the periodic features of the speed data are considered via a hybrid prediction method, which assumes that the data consist of two components: a periodic component and a residual component. The periodic component is described by a trigonometric regression function, and the residual component is modeled by the statistical models or the machine learning approaches. The important conclusions can be summarized as follows: (1) the multi-step ahead prediction accuracy improves when considering the periodic component of speed data for both three statistical models and three machine learning models, especially in the peak hours; (2) considering the impact of periodic component for all models, the prediction performance improvement gradually becomes larger as the time step increases; (3) under the same prediction horizon, the prediction performance of all models for 15-minute speed data is generally better than that for 5-minute speed data. Overall, the findings in this paper suggest that the proposed hybrid prediction approach is effective for both statistical and machine learning models in short-term speed prediction. |
| format | Article |
| id | doaj-art-f1e2d231764a4610b31237522cf22609 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-f1e2d231764a4610b31237522cf226092025-02-03T01:04:41ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/96289579628957Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning ModelsXiaoxue Yang0Yajie Zou1Jinjun Tang2Jian Liang3Muhammad Ijaz4Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, ChinaKey Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, ChinaSchool of Traffic and Transportation Engineering, Smart Transport Key Laboratory of Hunan Province, Central South University, Changsha, ChinaSchool of Traffic and Transportation Engineering, Smart Transport Key Laboratory of Hunan Province, Central South University, Changsha, ChinaKey Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, ChinaAccurate prediction of traffic information (i.e., traffic flow, travel time, traffic speed, etc.) is a key component of Intelligent Transportation System (ITS). Traffic speed is an important indicator to evaluate traffic efficiency. Up to date, although a few studies have considered the periodic feature in traffic prediction, very few studies comprehensively evaluate the impact of periodic component on statistical and machine learning prediction models. This paper selects several representative statistical models and machine learning models to analyze the influence of periodic component on short-term speed prediction under different scenarios: (1) multi-horizon ahead prediction (5, 15, 30, 60 minutes ahead predictions), (2) with and without periodic component, (3) two data aggregation levels (5-minute and 15-minute), (4) peak hours and off-peak hours. Specifically, three statistical models (i.e., space time (ST) model, vector autoregressive (VAR) model, autoregressive integrated moving average (ARIMA) model) and three machine learning approaches (i.e., support vector machines (SVM) model, multi-layer perceptron (MLP) model, recurrent neural network (RNN) model) are developed and examined. Furthermore, the periodic features of the speed data are considered via a hybrid prediction method, which assumes that the data consist of two components: a periodic component and a residual component. The periodic component is described by a trigonometric regression function, and the residual component is modeled by the statistical models or the machine learning approaches. The important conclusions can be summarized as follows: (1) the multi-step ahead prediction accuracy improves when considering the periodic component of speed data for both three statistical models and three machine learning models, especially in the peak hours; (2) considering the impact of periodic component for all models, the prediction performance improvement gradually becomes larger as the time step increases; (3) under the same prediction horizon, the prediction performance of all models for 15-minute speed data is generally better than that for 5-minute speed data. Overall, the findings in this paper suggest that the proposed hybrid prediction approach is effective for both statistical and machine learning models in short-term speed prediction.http://dx.doi.org/10.1155/2020/9628957 |
| spellingShingle | Xiaoxue Yang Yajie Zou Jinjun Tang Jian Liang Muhammad Ijaz Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models Journal of Advanced Transportation |
| title | Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models |
| title_full | Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models |
| title_fullStr | Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models |
| title_full_unstemmed | Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models |
| title_short | Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models |
| title_sort | evaluation of short term freeway speed prediction based on periodic analysis using statistical models and machine learning models |
| url | http://dx.doi.org/10.1155/2020/9628957 |
| work_keys_str_mv | AT xiaoxueyang evaluationofshorttermfreewayspeedpredictionbasedonperiodicanalysisusingstatisticalmodelsandmachinelearningmodels AT yajiezou evaluationofshorttermfreewayspeedpredictionbasedonperiodicanalysisusingstatisticalmodelsandmachinelearningmodels AT jinjuntang evaluationofshorttermfreewayspeedpredictionbasedonperiodicanalysisusingstatisticalmodelsandmachinelearningmodels AT jianliang evaluationofshorttermfreewayspeedpredictionbasedonperiodicanalysisusingstatisticalmodelsandmachinelearningmodels AT muhammadijaz evaluationofshorttermfreewayspeedpredictionbasedonperiodicanalysisusingstatisticalmodelsandmachinelearningmodels |