Data mining and classification for traffic systems using genetic network programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@PhdThesis{HuiyuZhou:thesis,
  author =       "Huiyu Zhou",
  title =        "Data mining and classification for traffic systems
                 using genetic network programming",
  school =       "Waseda University",
  year =         "2011",
  address =      "Japan",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, genetic
                 network programming",
  URL =          "http://jairo.nii.ac.jp/0069/00020480/en",
  URL =          "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/36316/3/Honbun-5575_00.pdf",
  URL =          "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/36316/1/Gaiyo-5575.pdf",
  URL =          "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/36316/2/Shinsa-5575.pdf",
  URL =          "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/36316/4/Honbun-5575_01.pdf",
  size =         "141 pages",
  abstract =     "Since the increase of the road traffic in modern
                 metropolis, the need for traffic prediction systems
                 becomes significant, while the traffic prediction aims
                 at an accurate estimate of the traffic flow as an
                 important item in recent traffic control systems.
                 Concretely, the traffic prediction system analyses
                 data, especially real-time traffic data, predicts
                 traffic situations, and its major role is to forecast
                 the congestion levels in advance of hours and even
                 days. Therefore, the traffic prediction system is
                 becoming the key issue in the advanced traffic
                 management and information systems, which reduces
                 traffic congestion and improve traffic mobility.

                 Vast amount of traffic data are currently available
                 using various components of the intelligent
                 transportation system(ITS). Satellite-based automatic
                 vehicle location technologies such as Global
                 Positioning System (GPS) and cellular phones can
                 determine the vehicle positions at frequent time
                 intervals. These equipments collect the information on
                 the vehicle positions and speeds, which are archived in
                 a large amount of databases, enabling further analysis
                 of the data about the traffic situations such as
                 traffic density patterns.

                 The evolutionary computation method named Genetic
                 Network Programming (GNP) has been proposed as an
                 extension of typical evolutionary computation methods,
                 such as Genetic Algorithm (GA) and Genetic Programming
                 (GP). GNP-based data mining has been already proposed
                 to deal with high density databases with large amount
                 of attributes. In order to further extend the proposed
                 data mining method using GNP to the real-time traffic
                 system, time related association rule mining methods
                 have been proposed and studied in this thesis. The
                 extracted time related rules are stored though
                 generations in a rule pool and analysed to build a
                 classifier, based on which the future traffic density
                 information can be provided to the optimal route search
                 algorithm of the navigation systems. Simulation studies
                 on the prediction accuracy of extracted rules and the
                 average travelling time of the optimal route using the
                 future traffic information are carried out to verify
                 the efficiency and effectiveness of the proposed
                 mechanisms. Some analyses of the proposed methods are
                 studied based on these simulation results comparing to
                 the conventional methods.

                 Unlike the other traffic density prediction methods,
                 the main task of GNP based time related data mining is
                 to allow the GNP individuals to self evolve and extract
                 association rules as many as possible. What's more, GNP
                 uses evolved individuals (directed graphs of GNP) just
                 as a tool to extract candidate association rules. Thus,
                 the structure of GNP individuals does not necessarily
                 represent the association relations of the database.
                 Instead, the extracted association rules are stored
                 together in the rule pool separated from the
                 individuals. As a result, the structures of GNP
                 individuals are less restricted than the structures of
                 GA and GP, thus GNP-based data mining becomes capable
                 of producing a large number of association rules.",
  abstract =     "In chapter 2, a method of association rule mining
                 using Genetic Network Programming (GNP) with time
                 series processing mechanism and attributes accumulation
                 mechanism was proposed in order to find time related
                 sequence rules efficiently in association rule
                 extraction systems. In this chapter, GNP is applied to
                 generate candidate association rules using the database
                 consisting of a large number of time related
                 attributes. In order to deal with a large number of
                 attributes, GNP individual accumulates fitter
                 attributes gradually during rounds, and the rules of
                 each round are stored in a Small Rule Pool using a hash
                 method, then the rules are finally stored in a Big Rule
                 Pool after the check of the overlap at the end of each
                 round. The aim of this chapter is to propose a method
                 to better handle association rule extraction of the
                 databases in a variety of time-related applications,
                 especially in the traffic prediction problems. The
                 algorithm which can find the important time related
                 association rules is described and several experimental
                 results are presented considering a traffic prediction
                 problem.

                 In chapter 3, an algorithm capable of finding important
                 time related association rules is proposed, where
                 Genetic Network Programming (GNP) with not only
                 Attribute Accumulation Mechanism (AAM) but also
                 Extraction Mechanism at Stages (EMS) is used. Then, the
                 classification system imitating the public voting
                 process based on extracted time related association
                 rules in the rule pool is proposed to estimate to which
                 class the current traffic data belong. Using this kind
                 of classification mechanism, the traffic prediction is
                 available since the extracted rules are based on time
                 sequences. Furthermore, the experimental results on the
                 traffic prediction problem using the proposed mechanism
                 are presented by the simple traffic simulator.

                 In chapter 4, further improvements have been proposed
                 for the time related association rule mining using
                 generalised GNP with Multi-Branches and Full-Paths
                 (MBFP) algorithm. For fully using the potential ability
                 of GNP structure, the mechanism of Generalised GNP with
                 MBFP is studied. The aim of this algorithm is to better
                 handle association rule extraction from the databases
                 with high efficiency in variety of time-related
                 applications, especially in the traffic density
                 prediction problems. The generalised algorithm which
                 can find the important time related association rules
                 is described and experimental results are presented
                 considering the traffic prediction problem.

                 Chapter 5 is devoted to a further advanced method for
                 extracting important time related association rules
                 using evolutionary algorithm named Genetic Network
                 Programming (GNP), where Accuracy Validation algorithm
                 is applied to further improve the prediction accuracy.
                 The proposed method provides more useful mean to
                 investigate the future traffic density of traffic
                 networks and hence further help to develop traffic
                 navigation systems. The aim of this algorithm is to
                 better handle association rule extraction using
                 prediction accuracy as one of the criteria and guide
                 the whole evolution process more efficiently, then the
                 adaptability of the proposed mechanism is studied
                 considering the real-time traffic situations using a
                 large scale simulator SOUND/4U. The experiments deal
                 with a traffic density prediction problem using the
                 database provided by the large scale simulator.",
  abstract =     "Chapter 6 describes a methodology for extracting
                 important time related association rules using an
                 evolutionary algorithm named fixed step GNPbased
                 association rule mining. And based on the rule pool of
                 the fixed prediction step, it is also proposed that the
                 prediction of the future traffic is combined with a
                 classical routing algorithm. The routing algorithm and
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Genetic Programming entries for Huiyu Zhou

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