Comparative Evaluation of Fuzzy Inference System, Support Vector Machine and Multilayer Feed-Forward Neural Network in Making Discretionary Lane Changing Decisions

Balal, Esmaeil; Cheu, Ruey Long

doi:10.14311/nnw.2018.28.021

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…In terms of lane-changing behaviour modelling, in addition to traditional lane-changing behaviour models based on the cellar automata model [4], survival model [5], fuzzy logic theory [6], hidden Markov model [7], etc., machine-learning methods have been widely used in drivers' lane-changing behaviour modelling in recent years [8][9][10]. For example, Balal [11] compared the applicability of the fuzzy inference system, support vector machine (SVM) and multilayer feedforward (MLF) neural network to freeway drivers' lane-changing behaviour. Research on the influencing factors of lane-changing behaviour mainly divides the influencing factors into subjective factors of drivers and objective factors of the external environment.…”

Section: Literature Reviewmentioning

confidence: 99%

Choice of Lane-Changing Point in an Urban Intertunnel Weaving Section Based on Random Forest and Support Vector Machine

Zhao

Wang

et al. 2023

PROMTT

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Urban intertunnel weaving (UIW) section is a special type of weaving section, where various lane-changing behaviours occur. To gain insight into the lane-changing behaviour in the UIW section, in this paper we attempt to analyse the decision feature and model the behaviour from the lane-changing point selection perspective. Based on field-collected lane-changing trajectory data, the lane-changing behaviours are divided into four types. Random forest method is applied to analyse the influencing factors of choice of lane-changing point. Moreover, a support vector machine model is adopted to perform decision behaviour modelling. Results reveal that there are significant differences in the influencing factors for different lane-changing types and different positions in the UIW segment. The three most important factor types are object vehicle status, current-lane rear vehicle status and target-lane rear vehicle status. The precision of the choice of lane-changing point models is at least 82%. The proposed method could reveal the detailed features of the lane-changing point selection behaviour in the UIW section and also provide a feasible choice of lane-changing point model.

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Section: Literature Reviewmentioning

confidence: 99%

Choice of Lane-Changing Point in an Urban Intertunnel Weaving Section Based on Random Forest and Support Vector Machine

Zhao

Wang

et al. 2023

PROMTT

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“…Principle of Support Vector Machine. Support Vector Machine is a learning algorithm proposed based on problem classification, and its basic definition is to separate the two types of samples to the greatest extent [7]. Generally speaking, all classification problems can be transformed into two-category problems.…”

Section: Basic Calculation Principle Of Fuzzy Support Vector Machinementioning

confidence: 99%

Simulation Evaluation of Calligraphy Art Style Relying on Fuzzy Support Vector Machine Scientific Calculation Algorithm

Cai

2022

Scientific Programming

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With the comprehensive follow-up of the information age and the rise of digital technology, the collection of calligraphy art can be preserved in the form of image data. As a testimony to the Chinese 5,000-year history, calligraphy has formed its own genre in the long river of time and its style prevailed in different periods. Nowadays, the recognition technology of characters has become advanced, but there is no better method for the recognition and classification of the artistic style of calligraphy. In addition, there are more and more counterfeiting methods, and computers alone cannot completely replace human identification; therefore, the two need to be integrated and cooperated in the identification work. This article proposes an improved fuzzy support vector machine algorithm based on the aggregation algorithm, which uses the skeleton extraction of calligraphic fonts as the main method. It uses the extracted font style and font morphological characteristics to identify the style of the work and whether it is genuine. In the article, the improvement strategy of the traditional fuzzy support vector machine is proposed, and its detection accuracy is compared with that of the traditional fuzzy support vector machine. The experimental results of this article show that the discrimination rate of authenticity cannot reach 100% and there is a certain degree of error. Through repeated experiments for many times, the value has a certain change, and when the step size is 0.1, calculating the average value can get the value of the false rejection rate and the false recognition rate.

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“…As a next enhancement of the algorithm, nonlinear characteristics will be modeled using a fuzzy inference system mechanism [32,33], which will be tailored-made for specific tunnel construction and its traffic features.…”

Section: New Road Urban Tunnel Traffic Controlmentioning

confidence: 99%

Interdisciplinary Urban Tunnel Control within Smart Cities

2021

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Nowadays, urban road tunnels are considered to be independent entities within a city. Their interactions with the rest of the city and vice versa are usually not considered and, if they are, are only considered in a limited way (for example, through the nearest traffic controller). Typically, only the traffic parameters and not the environmental impacts are considered. This paper has two major objectives. First, we provide a systemic view on a road urban tunnel. The major focus is on the interfaces between the tunnel and the rest of the city and the way they will be managed. We are providing a tool to take into consideration a sustainable development of a tunnel (i.e., not only traffic flow parameters such as average speed, but also environmental and societal characteristics). This model expresses the actual traffic situation in a monetary form (i.e., cost of congestions). The second objective is to provide a new road urban tunnel control approach that follows the original methodology and systemic view described in the paper. If the tunnel is controlled autonomously, which corresponds to the current state-of-the-art in many cities, the algorithm decides to close it based on only local parameters. However, the proposed new algorithm takes into consideration not only the traffic situation in the tunnel (expressed by the parameter traffic density), but also the actual traffic situation within the city (expressed by its level of service (LOS)). This allows more environmentally, socially and economically sustainable oriented road urban tunnel management. The described algorithm is demonstrated on a specific example of the tunnel complex Blanka in Prague.

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Comparative Evaluation of Fuzzy Inference System, Support Vector Machine and Multilayer Feed-Forward Neural Network in Making Discretionary Lane Changing Decisions

Cited by 10 publications

References 16 publications

Choice of Lane-Changing Point in an Urban Intertunnel Weaving Section Based on Random Forest and Support Vector Machine

Choice of Lane-Changing Point in an Urban Intertunnel Weaving Section Based on Random Forest and Support Vector Machine

Simulation Evaluation of Calligraphy Art Style Relying on Fuzzy Support Vector Machine Scientific Calculation Algorithm

Interdisciplinary Urban Tunnel Control within Smart Cities

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