An Extensive Review on Data Mining Methods and Clustering Models for Intelligent Transportation System

被引:7
作者
Anand, Sesham [1 ]
Padmanabham, P. [2 ]
Govardhan, A. [3 ]
Kulkarni, Rajesh H. [4 ]
机构
[1] Maturi Venkata Subba Rao Engn Coll, Dept Comp Sci & Engn, Hyderabad, Andhra Pradesh, India
[2] Bharat Inst Engn & Technol, Dept Comp Sci & Engn, Hyderabad, Andhra Pradesh, India
[3] JNTU Hyderabad, JNTUH Coll Engn, Hyderabad, Telangana, India
[4] Dept Comp Engn, JSPM Narhe Tech Campus, Pune, Maharashtra, India
关键词
Data mining; ITS; clustering; evolutionary computation; unsupervised;
D O I
10.1515/jisys-2016-0159
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Data mining techniques support numerous applications of intelligent transportation systems (ITSs). This paper critically reviews various data mining techniques for achieving trip planning in ITSs. The literature review starts with the discussion on the contributions of descriptive and predictive mining techniques in ITSs, and later continues on the contributions of the clustering techniques. Being the largely used approach, the use of cluster analysis in ITSs is assessed. However, big data analysis is risky with clustering methods. Thus, evolutionary computational algorithms are used for data mining. Though unsupervised clustering models are widely used, drawbacks such as selection of optimal number of clustering points, defining termination criterion, and lack of objective function also occur. Eventually, various drawbacks of evolutionary computational algorithm are also addressed in this paper.
引用
收藏
页码:263 / 273
页数:11
相关论文
共 70 条
[1]   Robust Inference of Principal Road Paths for Intelligent Transportation Systems [J].
Agamennoni, Gabriel ;
Nieto, Juan I. ;
Nebot, Eduardo M. .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2011, 12 (01) :298-308
[2]   Traffic congestion estimation service exploiting mobile assisted positioning schemes in GSM networks [J].
Andreas, Waadt ;
Shangbo, Wang ;
Guido, Bruck H. ;
Peter, Jung .
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MINING SCIENCE & TECHNOLOGY (ICMST2009), 2009, 1 (01) :1385-1392
[3]   An evolutionary algorithm for a new multi-objective location-inventory model in a distribution network with transportation modes and third-party logistics providers [J].
Arabzad, S. Mohammad ;
Ghorbani, Mazaher ;
Tavakkoli-Moghaddam, Reza .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2015, 53 (04) :1038-1050
[4]   Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction [J].
Asif, Muhammad Tayyab ;
Dauwels, Justin ;
Goh, Chong Yang ;
Oran, Ali ;
Fathi, Esmail ;
Xu, Muye ;
Dhanya, Menoth Mohan ;
Mitrovic, Nikola ;
Jaillet, Patrick .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2014, 15 (02) :794-804
[5]   Behaviour theory and soft transport policy measures [J].
Bamberg, Sebastian ;
Fujii, Satoshi ;
Friman, Margareta ;
Garling, Tommy .
TRANSPORT POLICY, 2011, 18 (01) :228-235
[6]  
Bhuyan P. K., 2011, TRANSPORT, V27, P149
[7]   The potential for the clustering of the maritime transport sector in the Greater Dublin Region [J].
Brett, Valerie ;
Roe, Michael .
MARITIME POLICY & MANAGEMENT, 2010, 37 (01) :1-16
[8]   Real-Time Transportation Mode Detection via Tracking Global Positioning System Mobile Devices [J].
Byon, Young-Ji ;
Abdulhai, Baher ;
Shalaby, Amer .
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS, 2009, 13 (04) :161-170
[9]   Prediction of parking space availability in real time [J].
Caicedo, Felix ;
Blazquez, Carola ;
Miranda, Pablo .
EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (08) :7281-7290