Space-Time Hybrid Model for Short-Time Travel Speed Prediction
Short-time traffic speed forecasting is a significant issue for developing Intelligent Transportation Systems applications, and accurate speed forecasting results are necessary inputs for Intelligent Traffic Security Information System (ITSIS) and advanced traffic management systems (ATMS). This pap...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2018/7696592 |
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| _version_ | 1832556737457029120 |
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| author | Qi Fan Wei Wang Xiaojian Hu Xuedong Hua Zhuyun Liu |
| author_facet | Qi Fan Wei Wang Xiaojian Hu Xuedong Hua Zhuyun Liu |
| author_sort | Qi Fan |
| collection | DOAJ |
| description | Short-time traffic speed forecasting is a significant issue for developing Intelligent Transportation Systems applications, and accurate speed forecasting results are necessary inputs for Intelligent Traffic Security Information System (ITSIS) and advanced traffic management systems (ATMS). This paper presents a hybrid model for travel speed based on temporal and spatial characteristics analysis and data fusion. This proposed methodology predicts speed by dividing the data into three parts: a periodic trend estimated by Fourier series, a residual part modeled by the ARIMA model, and the possible events affected by upstream or downstream traffic conditions. The aim of this study is to improve the accuracy of the prediction by modeling time and space variation of speed, and the forecast results could simultaneously reflect the periodic variation of traffic speed and emergencies. This information could provide decision-makers with a basis for developing traffic management measures. To achieve the research objective, one year of speed data was collected in Twin Cities Metro, Minnesota. The experimental results demonstrate that the proposed method can be used to explore the periodic characteristics of speed data and show abilities in increasing the accuracy of travel speed prediction. |
| format | Article |
| id | doaj-art-4103cc0126634377ae12a06b2027dc52 |
| 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-4103cc0126634377ae12a06b2027dc522025-02-03T05:44:30ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2018-01-01201810.1155/2018/76965927696592Space-Time Hybrid Model for Short-Time Travel Speed PredictionQi Fan0Wei Wang1Xiaojian Hu2Xuedong Hua3Zhuyun Liu4Jiangsu Key Laboratory of Urban ITS, Southeast University, Si Pai Lou #2, Nanjing 210096, ChinaSchool of Transportation, Southeast University, Si Pai Lou #2, Nanjing 210096, ChinaSchool of Transportation, Southeast University, Si Pai Lou #2, Nanjing 210096, ChinaSchool of Transportation, Southeast University, Si Pai Lou #2, Nanjing 210096, ChinaZhuhai Institute of Urban Planning & Design, Mei Hua Dong Road #302, Zhuhai 519000, ChinaShort-time traffic speed forecasting is a significant issue for developing Intelligent Transportation Systems applications, and accurate speed forecasting results are necessary inputs for Intelligent Traffic Security Information System (ITSIS) and advanced traffic management systems (ATMS). This paper presents a hybrid model for travel speed based on temporal and spatial characteristics analysis and data fusion. This proposed methodology predicts speed by dividing the data into three parts: a periodic trend estimated by Fourier series, a residual part modeled by the ARIMA model, and the possible events affected by upstream or downstream traffic conditions. The aim of this study is to improve the accuracy of the prediction by modeling time and space variation of speed, and the forecast results could simultaneously reflect the periodic variation of traffic speed and emergencies. This information could provide decision-makers with a basis for developing traffic management measures. To achieve the research objective, one year of speed data was collected in Twin Cities Metro, Minnesota. The experimental results demonstrate that the proposed method can be used to explore the periodic characteristics of speed data and show abilities in increasing the accuracy of travel speed prediction.http://dx.doi.org/10.1155/2018/7696592 |
| spellingShingle | Qi Fan Wei Wang Xiaojian Hu Xuedong Hua Zhuyun Liu Space-Time Hybrid Model for Short-Time Travel Speed Prediction Discrete Dynamics in Nature and Society |
| title | Space-Time Hybrid Model for Short-Time Travel Speed Prediction |
| title_full | Space-Time Hybrid Model for Short-Time Travel Speed Prediction |
| title_fullStr | Space-Time Hybrid Model for Short-Time Travel Speed Prediction |
| title_full_unstemmed | Space-Time Hybrid Model for Short-Time Travel Speed Prediction |
| title_short | Space-Time Hybrid Model for Short-Time Travel Speed Prediction |
| title_sort | space time hybrid model for short time travel speed prediction |
| url | http://dx.doi.org/10.1155/2018/7696592 |
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