{"title":"从亚洲到欧洲:大陆间的短期交通流量预测","authors":"Sevgi Kaya, Necati Kilic, T. Koçak, V. C. Gungor","doi":"10.1109/ICT.2014.6845123","DOIUrl":null,"url":null,"abstract":"Modelling the traffic flow and predicting the near-future traffic status are two challenging problems of the smart transportation on roads. The difficulty is particularly pronounced in forecasting the complex non-linear dynamics of flow. Most of the state-of-the-art work on traffic flow prediction determine the parameters based on the fundamental relationship between flow, density and speed without considering its influence to the consecutive one. However, these approaches tend to fail in real life scenarios due to the negligence of the spatio-temporal dependence of parameters within road segments. In this paper, we propose a new traffic flow model to predict the arterial travel time using probe data. We then evaluate our model under various traffic conditions to determine its feasibility for near-future traffic flow prediction. The proposed method presents promising results by outperforming the state-of-the-art in predicting near-future traffic flow on roads in case of sparse data and high flow density.","PeriodicalId":154328,"journal":{"name":"2014 21st International Conference on Telecommunications (ICT)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"From asia to europe: Short-term traffic flow prediction between continents\",\"authors\":\"Sevgi Kaya, Necati Kilic, T. Koçak, V. C. Gungor\",\"doi\":\"10.1109/ICT.2014.6845123\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Modelling the traffic flow and predicting the near-future traffic status are two challenging problems of the smart transportation on roads. The difficulty is particularly pronounced in forecasting the complex non-linear dynamics of flow. Most of the state-of-the-art work on traffic flow prediction determine the parameters based on the fundamental relationship between flow, density and speed without considering its influence to the consecutive one. However, these approaches tend to fail in real life scenarios due to the negligence of the spatio-temporal dependence of parameters within road segments. In this paper, we propose a new traffic flow model to predict the arterial travel time using probe data. We then evaluate our model under various traffic conditions to determine its feasibility for near-future traffic flow prediction. The proposed method presents promising results by outperforming the state-of-the-art in predicting near-future traffic flow on roads in case of sparse data and high flow density.\",\"PeriodicalId\":154328,\"journal\":{\"name\":\"2014 21st International Conference on Telecommunications (ICT)\",\"volume\":\"71 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 21st International Conference on Telecommunications (ICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICT.2014.6845123\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 21st International Conference on Telecommunications (ICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICT.2014.6845123","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
From asia to europe: Short-term traffic flow prediction between continents
Modelling the traffic flow and predicting the near-future traffic status are two challenging problems of the smart transportation on roads. The difficulty is particularly pronounced in forecasting the complex non-linear dynamics of flow. Most of the state-of-the-art work on traffic flow prediction determine the parameters based on the fundamental relationship between flow, density and speed without considering its influence to the consecutive one. However, these approaches tend to fail in real life scenarios due to the negligence of the spatio-temporal dependence of parameters within road segments. In this paper, we propose a new traffic flow model to predict the arterial travel time using probe data. We then evaluate our model under various traffic conditions to determine its feasibility for near-future traffic flow prediction. The proposed method presents promising results by outperforming the state-of-the-art in predicting near-future traffic flow on roads in case of sparse data and high flow density.