Soft Discretization in a Classification Model for Modeling Adaptive Route Choice with a Fuzzy ID3 Algorithm

Park, Kyounga; Bell, M.G.; Kaparias, Ioannis; Belzner, Heidrun

doi:10.3141/2076-03

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…Compared with the artificial neural network algorithm, this algorithm has an advantage, which can help researchers to determine the correlation between explanatory variables and choice. Park et al [113] further studied the sensitivity method of decision tree based on the work of Toshiyuki et al [112]. Soft discretization of continuous values in the optimized fuzzy decision tree can improve the classification of robustness, and the fuzzy decision tree assigns values by deciding on the degree of certainty of proposals generated with fuzzy reasoning.…”

Section: Decision Tree Modelmentioning

confidence: 99%

Vehicle Lane Change Models—A Historical Review

Liu,

Hong,

Lin

2023

Applied Sciences

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Lane changing is a complex operation that has a significant impact on traffic safety. The accurate identification and assessment of potential risks in the driving environment before lane changing is crucial for the safe and smooth completion of a lane change. In this paper, the research status of vehicle lane change models is reviewed. Firstly, various factors affecting lane change models are analyzed. Different drivers will be affected by vehicle dynamic parameters, vehicle driving states, and driver characteristics under various road environments. Secondly, the vehicle lane change models are divided into four types: the empirical model of lane changing, the physical model of lane changing, the cognitive model of lane changing, and the mixed model of lane changing. The advantages and disadvantages of different types of lane change models are analyzed, and the key problems to be solved by different lane change models are expounded, respectively, from the aspects of input variables and reasoning algorithms. Finally, according to the advantages and disadvantages of different lane change models, a future research direction is proposed.

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Section: Decision Tree Modelmentioning

confidence: 99%

Vehicle Lane Change Models—A Historical Review

Liu,

Hong,

Lin

2023

Applied Sciences

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“…Among them, one of the most widely used is the fuzzy ID3 algorithm as it is shown through its numerous variants, i.e. the ones given in 9,10,11 and its application to several real problems 46,47,48 . Furthermore, it provides a good trade-off between interpretability and accuracy with a small computation-effort 12 .…”

Section: Fuzzy Id3 Induction Processmentioning

confidence: 99%

Iivfdt: Ignorance Functions Based Interval-Valued Fuzzy Decision Tree With Genetic Tuning

Sanz

Bustince

Fernández

et al. 2012

Int. J. Unc. Fuzz. Knowl. Based Syst.

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The choice of membership functions plays an essential role in the success of fuzzy systems. This is a complex problem due to the possible lack of knowledge when assigning punctual values as membership degrees. To face this handicap, we propose a methodology called Ignorance functions based Interval-Valued Fuzzy Decision Tree with genetic tuning, IIVFDT for short, which allows to improve the performance of fuzzy decision trees by taking into account the ignorance degree. This ignorance degree is the result of a weak ignorance function applied to the punctual value set as membership degree. Our IIVFDT proposal is composed of four steps: (1) the base fuzzy decision tree is generated using the fuzzy ID3 algorithm; (2) the linguistic labels are modeled with Interval-Valued Fuzzy Sets. To do so, a new parametrized construction method of Interval-Valued Fuzzy Sets is defined, whose length represents such ignorance degree; (3) the fuzzy reasoning method is extended to work with this representation of the linguistic terms; (4) an evolutionary tuning step is applied for computing the optimal ignorance degree for each Interval-Valued Fuzzy Set. The experimental study shows that the IIVFDT method allows the results provided by the initial fuzzy ID3 with and without Interval-Valued Fuzzy Sets to be outperformed. The suitability of the proposed methodology is shown with respect to both several state-of-the-art fuzzy decision trees and C4.5. Furthermore, we analyze the quality of our approach versus two methods that learn the fuzzy decision tree using genetic algorithms. Finally, we show that a superior performance can be achieved by means of the positive synergy obtained when applying the well known genetic tuning of the lateral position after the application of the IIVFDT method.

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“…They analysed the route choice behaviours on Japanese highways with a decision tree. Following this work, Park et al [23] then proposed a modified decision tree to better capture the route choice behaviours and to generate more robust rules. Afterwards, the decision tree with the fuzzy method is proposed as well [24].…”

Section: Introductionmentioning

confidence: 99%

Understanding drivers' route choice behaviours in the urban network with machine learning models

Lai

et al. 2018

IET Intelligent Transport Systems

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Soft Discretization in a Classification Model for Modeling Adaptive Route Choice with a Fuzzy ID3 Algorithm

Cited by 3 publications

References 14 publications

Vehicle Lane Change Models—A Historical Review

Vehicle Lane Change Models—A Historical Review

Iivfdt: Ignorance Functions Based Interval-Valued Fuzzy Decision Tree With Genetic Tuning

Understanding drivers' route choice behaviours in the urban network with machine learning models

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