Two-Phase Model of Multistep Forecasting of Traffic State Reliability
Multistep prediction of traffic state is a key technology for advanced transportation information system. The research results based on the principle of multistep prediction can provide more information about the traffic operating quality in advance. Considering that prediction error increases with...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2018/7650928 |
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| _version_ | 1832565019549630464 |
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| author | Jufen Yang Zhigang Liu Guiyan Jiang Lin Zhu |
| author_facet | Jufen Yang Zhigang Liu Guiyan Jiang Lin Zhu |
| author_sort | Jufen Yang |
| collection | DOAJ |
| description | Multistep prediction of traffic state is a key technology for advanced transportation information system. The research results based on the principle of multistep prediction can provide more information about the traffic operating quality in advance. Considering that prediction error increases with the increasing numbers of multistep predictions, this research proposes the concept of dynamic predictability that is related to the characteristic of historical traffic flow data used. The traffic flow is characterized by randomness, regularity, and volatility according to the traffic flow theory. Therefore, three key indexes are firstly calculated to measure the characteristics of reliability series. Then a two-phase model is established based on wavelet neural network optimized by particle swarm optimization. The upper phase is a model to estimate the number of predictable steps, and the lower phase is the multistep prediction model of reliability. Compared with that of backpropagation neural network and support vector machine, results show that the convergence time of the wavelet neural network optimized by particle swarm optimization is the lowest, which only costs 256 and 291 seconds in both two-phase models under the same conditions. The average relative error of multistep prediction reached the lowest value, 8.91% and 12.01%, respectively, for weekday and weekend data used. Moreover, the prediction performance based on weekday is better than that of weekend. The research results lay a decision-making basis for managers in determining the key parts of road network to develop future improvement measures. |
| format | Article |
| id | doaj-art-5669dc26ff794f12918509e9b237a27b |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-5669dc26ff794f12918509e9b237a27b2025-02-03T01:09:41ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2018-01-01201810.1155/2018/76509287650928Two-Phase Model of Multistep Forecasting of Traffic State ReliabilityJufen Yang0Zhigang Liu1Guiyan Jiang2Lin Zhu3Dr., College of Urban Railway Transportation, Shanghai University of Engineering Science, ChinaProf., College of Urban Railway Transportation, Shanghai University of Engineering Science, ChinaProf., Faculty of Maritime and Transportation, Ningbo University, ChinaDr., College of Urban Railway Transportation, Shanghai University of Engineering Science, ChinaMultistep prediction of traffic state is a key technology for advanced transportation information system. The research results based on the principle of multistep prediction can provide more information about the traffic operating quality in advance. Considering that prediction error increases with the increasing numbers of multistep predictions, this research proposes the concept of dynamic predictability that is related to the characteristic of historical traffic flow data used. The traffic flow is characterized by randomness, regularity, and volatility according to the traffic flow theory. Therefore, three key indexes are firstly calculated to measure the characteristics of reliability series. Then a two-phase model is established based on wavelet neural network optimized by particle swarm optimization. The upper phase is a model to estimate the number of predictable steps, and the lower phase is the multistep prediction model of reliability. Compared with that of backpropagation neural network and support vector machine, results show that the convergence time of the wavelet neural network optimized by particle swarm optimization is the lowest, which only costs 256 and 291 seconds in both two-phase models under the same conditions. The average relative error of multistep prediction reached the lowest value, 8.91% and 12.01%, respectively, for weekday and weekend data used. Moreover, the prediction performance based on weekday is better than that of weekend. The research results lay a decision-making basis for managers in determining the key parts of road network to develop future improvement measures.http://dx.doi.org/10.1155/2018/7650928 |
| spellingShingle | Jufen Yang Zhigang Liu Guiyan Jiang Lin Zhu Two-Phase Model of Multistep Forecasting of Traffic State Reliability Discrete Dynamics in Nature and Society |
| title | Two-Phase Model of Multistep Forecasting of Traffic State Reliability |
| title_full | Two-Phase Model of Multistep Forecasting of Traffic State Reliability |
| title_fullStr | Two-Phase Model of Multistep Forecasting of Traffic State Reliability |
| title_full_unstemmed | Two-Phase Model of Multistep Forecasting of Traffic State Reliability |
| title_short | Two-Phase Model of Multistep Forecasting of Traffic State Reliability |
| title_sort | two phase model of multistep forecasting of traffic state reliability |
| url | http://dx.doi.org/10.1155/2018/7650928 |
| work_keys_str_mv | AT jufenyang twophasemodelofmultistepforecastingoftrafficstatereliability AT zhigangliu twophasemodelofmultistepforecastingoftrafficstatereliability AT guiyanjiang twophasemodelofmultistepforecastingoftrafficstatereliability AT linzhu twophasemodelofmultistepforecastingoftrafficstatereliability |