Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model
Short-term traffic speed prediction is a key component of proactive traffic control in the intelligent transportation systems. The objective of this study is to investigate the short-term traffic speed prediction under different data collection time intervals. Traffic speed data was collected from a...
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| Veröffentlicht in: | PloS one 2019-06, Vol.14 (6), p.e0218626-e0218626 |
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| creator | Song, Zhanguo Guo, Yanyong Wu, Yao Ma, Jing |
| description | Short-term traffic speed prediction is a key component of proactive traffic control in the intelligent transportation systems. The objective of this study is to investigate the short-term traffic speed prediction under different data collection time intervals. Traffic speed data was collected from an urban freeway in Edmonton, Canada. A seasonal autoregressive integrated moving average plus seasonal discrete grey model structure (SARIMA-SDGM) was proposed to perform the traffic speed prediction. The model performance of SARIMA-SDGM model was compared with that of the seasonal autoregressive integrated moving average (SARIMA) model, seasonal discrete grey model (SDGM), artificial neural network (ANN) model, and support vector regression (SVR) model. The results showed that SARIMA-SDGM model performs best with the lowest mean absolute error (MAE), mean absolute percentage error (MAPE), and the root mean square error (RMSE). The traffic speed prediction accuracy under different time intervals were compared based on the SARIMA-SDGM model. The results showed that the prediction accuracy improves with the increase in time interval. In addition, when the time interval is greater than 10 min, the prediction results yield stable prediction accuracy. |
| doi_str_mv | 10.1371/journal.pone.0218626 |
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The objective of this study is to investigate the short-term traffic speed prediction under different data collection time intervals. Traffic speed data was collected from an urban freeway in Edmonton, Canada. A seasonal autoregressive integrated moving average plus seasonal discrete grey model structure (SARIMA-SDGM) was proposed to perform the traffic speed prediction. The model performance of SARIMA-SDGM model was compared with that of the seasonal autoregressive integrated moving average (SARIMA) model, seasonal discrete grey model (SDGM), artificial neural network (ANN) model, and support vector regression (SVR) model. The results showed that SARIMA-SDGM model performs best with the lowest mean absolute error (MAE), mean absolute percentage error (MAPE), and the root mean square error (RMSE). The traffic speed prediction accuracy under different time intervals were compared based on the SARIMA-SDGM model. The results showed that the prediction accuracy improves with the increase in time interval. In addition, when the time interval is greater than 10 min, the prediction results yield stable prediction accuracy.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0218626</identifier><identifier>PMID: 31242226</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Alberta ; Analysis ; Artificial neural networks ; Biology and Life Sciences ; Cities ; Computer and Information Sciences ; Data Collection ; Engineering and Technology ; Highways ; Humans ; Intelligent transportation systems ; Intervals ; Linear Models ; Machine Learning ; Models, Statistical ; Motor Vehicles - statistics & numerical data ; Neural networks ; Neural Networks, Computer ; Performance prediction ; Physical Sciences ; Prediction models ; Regression Analysis ; Regression models ; Research and Analysis Methods ; Root-mean-square errors ; Seasons ; Support Vector Machine ; Support vector machines ; Time Factors ; Traffic congestion ; Traffic control ; Traffic engineering ; Traffic flow ; Traffic models ; Traffic speed ; Transportation - statistics & numerical data</subject><ispartof>PloS one, 2019-06, Vol.14 (6), p.e0218626-e0218626</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Song et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2019 Song et al 2019 Song et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-3ef458fadfb5f51d2520f12370e84d232dcffe1c6868b67e19febcada18f7c373</citedby><cites>FETCH-LOGICAL-c692t-3ef458fadfb5f51d2520f12370e84d232dcffe1c6868b67e19febcada18f7c373</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6594624/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6594624/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2095,2914,23846,27903,27904,53769,53771,79346,79347</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31242226$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Tang, Jinjun</contributor><creatorcontrib>Song, Zhanguo</creatorcontrib><creatorcontrib>Guo, Yanyong</creatorcontrib><creatorcontrib>Wu, Yao</creatorcontrib><creatorcontrib>Ma, Jing</creatorcontrib><title>Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Short-term traffic speed prediction is a key component of proactive traffic control in the intelligent transportation systems. The objective of this study is to investigate the short-term traffic speed prediction under different data collection time intervals. Traffic speed data was collected from an urban freeway in Edmonton, Canada. A seasonal autoregressive integrated moving average plus seasonal discrete grey model structure (SARIMA-SDGM) was proposed to perform the traffic speed prediction. The model performance of SARIMA-SDGM model was compared with that of the seasonal autoregressive integrated moving average (SARIMA) model, seasonal discrete grey model (SDGM), artificial neural network (ANN) model, and support vector regression (SVR) model. The results showed that SARIMA-SDGM model performs best with the lowest mean absolute error (MAE), mean absolute percentage error (MAPE), and the root mean square error (RMSE). The traffic speed prediction accuracy under different time intervals were compared based on the SARIMA-SDGM model. The results showed that the prediction accuracy improves with the increase in time interval. In addition, when the time interval is greater than 10 min, the prediction results yield stable prediction accuracy.</description><subject>Alberta</subject><subject>Analysis</subject><subject>Artificial neural networks</subject><subject>Biology and Life Sciences</subject><subject>Cities</subject><subject>Computer and Information Sciences</subject><subject>Data Collection</subject><subject>Engineering and Technology</subject><subject>Highways</subject><subject>Humans</subject><subject>Intelligent transportation systems</subject><subject>Intervals</subject><subject>Linear Models</subject><subject>Machine Learning</subject><subject>Models, Statistical</subject><subject>Motor Vehicles - statistics & numerical data</subject><subject>Neural networks</subject><subject>Neural Networks, Computer</subject><subject>Performance prediction</subject><subject>Physical Sciences</subject><subject>Prediction models</subject><subject>Regression Analysis</subject><subject>Regression models</subject><subject>Research and Analysis Methods</subject><subject>Root-mean-square errors</subject><subject>Seasons</subject><subject>Support Vector Machine</subject><subject>Support vector machines</subject><subject>Time Factors</subject><subject>Traffic congestion</subject><subject>Traffic control</subject><subject>Traffic engineering</subject><subject>Traffic flow</subject><subject>Traffic models</subject><subject>Traffic speed</subject><subject>Transportation - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Song, Zhanguo</au><au>Guo, Yanyong</au><au>Wu, Yao</au><au>Ma, Jing</au><au>Tang, Jinjun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2019-06-26</date><risdate>2019</risdate><volume>14</volume><issue>6</issue><spage>e0218626</spage><epage>e0218626</epage><pages>e0218626-e0218626</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Short-term traffic speed prediction is a key component of proactive traffic control in the intelligent transportation systems. The objective of this study is to investigate the short-term traffic speed prediction under different data collection time intervals. Traffic speed data was collected from an urban freeway in Edmonton, Canada. A seasonal autoregressive integrated moving average plus seasonal discrete grey model structure (SARIMA-SDGM) was proposed to perform the traffic speed prediction. The model performance of SARIMA-SDGM model was compared with that of the seasonal autoregressive integrated moving average (SARIMA) model, seasonal discrete grey model (SDGM), artificial neural network (ANN) model, and support vector regression (SVR) model. The results showed that SARIMA-SDGM model performs best with the lowest mean absolute error (MAE), mean absolute percentage error (MAPE), and the root mean square error (RMSE). The traffic speed prediction accuracy under different time intervals were compared based on the SARIMA-SDGM model. The results showed that the prediction accuracy improves with the increase in time interval. In addition, when the time interval is greater than 10 min, the prediction results yield stable prediction accuracy.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>31242226</pmid><doi>10.1371/journal.pone.0218626</doi><tpages>e0218626</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Alberta Analysis Artificial neural networks Biology and Life Sciences Cities Computer and Information Sciences Data Collection Engineering and Technology Highways Humans Intelligent transportation systems Intervals Linear Models Machine Learning Models, Statistical Motor Vehicles - statistics & numerical data Neural networks Neural Networks, Computer Performance prediction Physical Sciences Prediction models Regression Analysis Regression models Research and Analysis Methods Root-mean-square errors Seasons Support Vector Machine Support vector machines Time Factors Traffic congestion Traffic control Traffic engineering Traffic flow Traffic models Traffic speed Transportation - statistics & numerical data |
| title | Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model |
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