Artificial immune K-means grid-density clustering algorithm for real-time monitoring and analysis of urban traffic
A novel clustering algorithm is presented for monitoring and analysing traffic conditions in real-time and automatically. The existing methods concentrate on analysis of traffic flow based on historical information, and they cannot provide timely analysis of traffic conditions. Regarding the vehicle...
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| Veröffentlicht in: | Electronics letters Jg. 49; H. 20; S. 1272 - 1273 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Stevenage
The Institution of Engineering and Technology
26.09.2013
Institution of Engineering and Technology |
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| ISSN: | 0013-5194, 1350-911X, 1350-911X |
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| Abstract | A novel clustering algorithm is presented for monitoring and analysing traffic conditions in real-time and automatically. The existing methods concentrate on analysis of traffic flow based on historical information, and they cannot provide timely analysis of traffic conditions. Regarding the vehicles on the roads as data points, a K-means grid-density clustering algorithm is proposed based on an artificial immune network to partition the vehicles data into proper clusters, and marks the densities for monitoring and analysing the traffic conditions. Simulated experimental results show that the proposed algorithm obtains higher efficiency and stability than traditional methods. |
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| AbstractList | A novel clustering algorithm is presented for monitoring and analysing traffic conditions in real‐time and automatically. The existing methods concentrate on analysis of traffic flow based on historical information, and they cannot provide timely analysis of traffic conditions. Regarding the vehicles on the roads as data points, a K ‐means grid‐density clustering algorithm is proposed based on an artificial immune network to partition the vehicles data into proper clusters, and marks the densities for monitoring and analysing the traffic conditions. Simulated experimental results show that the proposed algorithm obtains higher efficiency and stability than traditional methods. |
| Author | Fang, Zhuoran Chen, Chuan Ming Pi, Dechang |
| Author_xml | – sequence: 1 givenname: Chuan Ming surname: Chen fullname: Chen, Chuan Ming email: ccm_0@163.com organization: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People's Republic of China – sequence: 2 givenname: Dechang surname: Pi fullname: Pi, Dechang organization: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People's Republic of China – sequence: 3 givenname: Zhuoran surname: Fang fullname: Fang, Zhuoran organization: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People's Republic of China |
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| Cites_doi | 10.1111/j.1468-0394.2010.00567.x 10.1016/j.trc.2012.02.003 10.1007/978-3-642-31205-2_4 |
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| Copyright | The Institution of Engineering and Technology 2020 The Institution of Engineering and Technology 2014 INIST-CNRS |
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| Keywords | artificial immune network traffic flow urban traffic conditions traffic engineering computing monitoring real time monitoring artificial immune systems timely analysis pattern clustering history information artificial immune k means grid density clustering algorithm vehicles data stability Cluster analysis Data analysis Density measurement Stability Urban traffic Cluster K means algorithm History Real time Experimental result Traffic flow Efficiency Traffic management Time analysis Monitoring Immune system |
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| Notes | Chuan Ming Chen: Also with the College of Mathematics and Computer Science, Anhui Normal University, Wuhu 241003, China |
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| References | Zargari, S.A.; Siabil, S.Z.; Alavi, A.H.; Gandomi, A.H. (C1) 2012; 29 Lin, S.; De Schutter, B.; Xi, Y. (C3) 2012; 24 2012; 29 2012 2012; 24 Shen W. (e_1_2_7_7_1) 2012 Zhang B. (e_1_2_7_3_1) 2012 e_1_2_7_5_1 e_1_2_7_4_1 e_1_2_7_2_1 Tseng P.J. (e_1_2_7_6_1) 2012 |
| References_xml | – volume: 24 start-page: 122 year: 2012 end-page: 140 ident: C3 article-title: Efficient network wide model-based predictive control for urban traffic networks publication-title: Transp. Res. C, Emerg. Technol. – volume: 29 start-page: 124 issue: 2 year: 2012 end-page: 142 ident: C1 article-title: A computational intelligence-based approach for short-term traffic flow prediction publication-title: Expert Syst. – start-page: 57 year: 2012 end-page: 72 – volume: 24 start-page: 122 year: 2012 end-page: 140 article-title: Efficient network wide model‐based predictive control for urban traffic networks publication-title: Transp. Res. C, Emerg. Technol. – start-page: 495 year: 2012 end-page: 503 – start-page: 1468 year: 2012 end-page: 1475 – start-page: 306 year: 2012 end-page: 310 – volume: 29 start-page: 124 issue: 2 year: 2012 end-page: 142 article-title: A computational intelligence‐based approach for short‐term traffic flow prediction publication-title: Expert Syst. – ident: e_1_2_7_2_1 doi: 10.1111/j.1468-0394.2010.00567.x – start-page: 495 volume-title: Traffic clustering and online traffic prediction in vehicle networks: a social influence perspective year: 2012 ident: e_1_2_7_3_1 – start-page: 1468 volume-title: Real‐time road traffic fusion and prediction with GPS and fixed‐sensor data year: 2012 ident: e_1_2_7_7_1 – start-page: 306 volume-title: Real‐time urban traffic sensing with GPS equipped probe vehicles year: 2012 ident: e_1_2_7_6_1 – ident: e_1_2_7_4_1 doi: 10.1016/j.trc.2012.02.003 – ident: e_1_2_7_5_1 doi: 10.1007/978-3-642-31205-2_4 |
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| Snippet | A novel clustering algorithm is presented for monitoring and analysing traffic conditions in real-time and automatically. The existing methods concentrate on... A novel clustering algorithm is presented for monitoring and analysing traffic conditions in real‐time and automatically. The existing methods concentrate on... |
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| SubjectTerms | Applied sciences artificial immune k means grid density clustering algorithm artificial immune network artificial immune systems Artificial intelligence Computer science; control theory; systems Data processing. List processing. Character string processing Exact sciences and technology Ground, air and sea transportation, marine construction history information Information and communications Memory organisation. Data processing monitoring pattern clustering real time monitoring Road transportation and traffic Software stability timely analysis traffic engineering computing traffic flow urban traffic conditions vehicles data |
| Title | Artificial immune K-means grid-density clustering algorithm for real-time monitoring and analysis of urban traffic |
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