City traffic flow breakdown prediction based on fuzzy rough set
In city traffic management, traffic breakdown is a very important issue, which is defined as a speed drop of a certain amount within a dense traffic situation. In order to predict city traffic flow breakdown accurately, in this paper, we propose a novel city traffic flow breakdown prediction algorit...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2017-05-01
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| Series: | Open Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/phys-2017-0032 |
| _version_ | 1818719200281100288 |
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| author | Yang Xu Da-wei Hu Bing Su Duo-jia Zhang |
| author_facet | Yang Xu Da-wei Hu Bing Su Duo-jia Zhang |
| author_sort | Yang Xu |
| collection | DOAJ |
| description | In city traffic management, traffic breakdown is a very important issue, which is defined as a speed drop of a certain amount within a dense traffic situation. In order to predict city traffic flow breakdown accurately, in this paper, we propose a novel city traffic flow breakdown prediction algorithm based on fuzzy rough set. Firstly, we illustrate the city traffic flow breakdown problem, in which three definitions are given, that is, 1) Pre-breakdown flow rate, 2) Rate, density, and speed of the traffic flow breakdown, and 3) Duration of the traffic flow breakdown. Moreover, we define a hazard function to represent the probability of the breakdown ending at a given time point. Secondly, as there are many redundant and irrelevant attributes in city flow breakdown prediction, we propose an attribute reduction algorithm using the fuzzy rough set. Thirdly, we discuss how to predict the city traffic flow breakdown based on attribute reduction and SVM classifier. Finally, experiments are conducted by collecting data from I-405 Freeway, which is located at Irvine, California. Experimental results demonstrate that the proposed algorithm is able to achieve lower average error rate of city traffic flow breakdown prediction. |
| first_indexed | 2024-12-17T20:03:10Z |
| format | Article |
| id | doaj.art-543ad643f5494f9fbc77d571ca239019 |
| institution | Directory Open Access Journal |
| issn | 2391-5471 |
| language | English |
| last_indexed | 2024-12-17T20:03:10Z |
| publishDate | 2017-05-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Physics |
| spelling | doaj.art-543ad643f5494f9fbc77d571ca2390192022-12-21T21:34:24ZengDe GruyterOpen Physics2391-54712017-05-0115129229910.1515/phys-2017-0032phys-2017-0032City traffic flow breakdown prediction based on fuzzy rough setYang Xu0Da-wei Hu1Bing Su2Duo-jia Zhang3School of Automobile, Chang’an University, Xi’an710064, ChinaSchool of Automobile, Chang’an University, Xi’an710064, ChinaSchool of Economics & Management, Xi’an Technological University, Xi’an710021, ChinaSchool of Automobile, Chang’an University, Xi’an710064, ChinaIn city traffic management, traffic breakdown is a very important issue, which is defined as a speed drop of a certain amount within a dense traffic situation. In order to predict city traffic flow breakdown accurately, in this paper, we propose a novel city traffic flow breakdown prediction algorithm based on fuzzy rough set. Firstly, we illustrate the city traffic flow breakdown problem, in which three definitions are given, that is, 1) Pre-breakdown flow rate, 2) Rate, density, and speed of the traffic flow breakdown, and 3) Duration of the traffic flow breakdown. Moreover, we define a hazard function to represent the probability of the breakdown ending at a given time point. Secondly, as there are many redundant and irrelevant attributes in city flow breakdown prediction, we propose an attribute reduction algorithm using the fuzzy rough set. Thirdly, we discuss how to predict the city traffic flow breakdown based on attribute reduction and SVM classifier. Finally, experiments are conducted by collecting data from I-405 Freeway, which is located at Irvine, California. Experimental results demonstrate that the proposed algorithm is able to achieve lower average error rate of city traffic flow breakdown prediction.https://doi.org/10.1515/phys-2017-0032traffic managementtraffic flow breakdownfuzzy rough setsvmfuzzy decision table06.20.dk |
| spellingShingle | Yang Xu Da-wei Hu Bing Su Duo-jia Zhang City traffic flow breakdown prediction based on fuzzy rough set Open Physics traffic management traffic flow breakdown fuzzy rough set svm fuzzy decision table 06.20.dk |
| title | City traffic flow breakdown prediction based on fuzzy rough set |
| title_full | City traffic flow breakdown prediction based on fuzzy rough set |
| title_fullStr | City traffic flow breakdown prediction based on fuzzy rough set |
| title_full_unstemmed | City traffic flow breakdown prediction based on fuzzy rough set |
| title_short | City traffic flow breakdown prediction based on fuzzy rough set |
| title_sort | city traffic flow breakdown prediction based on fuzzy rough set |
| topic | traffic management traffic flow breakdown fuzzy rough set svm fuzzy decision table 06.20.dk |
| url | https://doi.org/10.1515/phys-2017-0032 |
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