Application of Clustering Algorithm in Intelligent Transportation Data Analysis

Abstract

Abstract.With the continuous development of data mining technology, to apply the data mining techniques to transportation sector will provide service to transportation scientifically and reasonably. In intelligent transportation, the analysis of traffic flow data is very important, how to analyze the traffic data intelligently is more difficult problem, so using a new data mining techniques to replace the traditional data analysis and interpretation methods is very necessary and meaningful, clustering algorithm is the collection of physical or abstracting objects into groups of similar objects from the multiple classes of processes. This paper describes all kinds of the data mining clustering algorithms, clustering algorithm is proposed in the method of dealing with traffic flow data, and applied to the actual traffic data processing, and finally the clustering algorithm is applied to each of highway toll station Various types of car traffic volume data analysis.

Demand clustering in freight logistics networks

“…Several vehicle routing problems (Bodin and Golden, 1981;Dondo and Cerdá, 2007;Özdamar and Demir, 2012;Schönberger, 2006;Simchi-Levi et al, 2005) take advantage of these methods by dividing the original network into subsets of geographically-close nodes where finding optimal routes is less cumbersome. Additionally, freight logistics problems have used clustering to understand the geographic distribution of demand and simplify logistics operations (Cao and Glover, 2010;Sharman and Roorda, 2011;Singh et al, 2007;Qiong et al, 2011). However, there are three limitations when proximity-based methods are used to cluster elements with an underlying network structure (Fortunato, 2010):…”

“…Additionally, geographic clustering has been used to reduce the computational complexity of vehicle routing problems (Bowerman et al, 1994;Bodin and Golden, 1981;Dondo and Cerdá, 2007;Özdamar and Demir, 2012;Schönberger, 2006;Simchi-Levi et al, 2005). Similarly, clustering has been used to understand the distribution of freight demand and simplify logistics operations (Cao and Glover, 2010;Sharman and Roorda, 2011;Singh et al, 2007;Qiong et al, 2011). However, these works present several limitations.…”

Development of method of multifactor classification of transport and logistic processes

“…The use of information technology and intelligent models in the problems of classification in various fields of people's activity is described in [21][22][23][24]. Analysis of neural network methods for solving the clas-sification problem is presented in [25][26][27][28][29]. Mathematical models of the classification problem presented in the form of discrete, non-smooth and multi-extreme optimization problems are considered in [18] for solving individual problems of recognition and analysis of data.…”

“…They are widely used at present in various fields. Analysis of neural network methods for solving the classification problem is presented in [25][26][27][28][29] and clustering algorithms in data mining are given in [30]. The essential shortcomings of the classification methods are as follows.…”

Cost aggregation in export logistics chain

“…Economic efficiency of logistics service providers has an important role in that journey. Freight logistics developers have used clustering to understand the geographic distribution of demand and simplify logistics operations (Cao and Glover 2010;Sharman and Roorda 2011;Singh et al 2007;Qiong et al 2011). Some researchers argue that competition has shifted from competition between companies towards competition produced through supply chain and practical logistics management (e.g., Ketchen and Hult 2007).…”