Development of pre-time intersection isolated signal using a fuzzy logic model

We investigate the development of pre-time signal intersection operating systems for isolated intersections using Fuzzy Logic models. The traffic signal system setting in Indonesia has been using the Indonesia Road Capacity Manual model 1997, for example it is installed at the intersection in large cities in Indonesia. The development of the Fuzzy Logic model is focused on improving the performance of the signaled intersection, using a combination of numerical variable analysis used by IRCM 1997, and the linguistic or traffic behavior variable as the basis of the Fuzzy Logic model study. The combination of the two variables in the Fuzzy Logic model analysis is expected to improve the intersection performance. The Fuzzy Logic model process involves the Membership Function theory as the basis for the confidence level of the traffic variable to be surveyed, and the Fuzzy Inference Engine to measure the choice of combinations of variables that will be selected to make the best performance of the intersection. The geometric of intersection must be control as it involves the input of research data, especially on the condition of the intersection legs and markers of motor cycle-special stopping places, which is a potential of a traffic violation by traveller. The model is verified with fuzzified data from 2017 traffic research survey in Bandung. As an illustration of the majority of intersection setting with an isolated pre-time operating system, there are 60 intersection points or 85% of the total 71 intersections available. This would be a potentially major problem when performance improvements is not carried out. The final analysis shows that the number of vehicles queues decreases while the traffic flows passing through the intersection increases, therefore fuzzy logic model is expected to contribute and to give alternative handling for intersection performance with pre-time operational.

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Underground Risk Index Assessment and Prediction Using a Simplified Hierarchical Fuzzy Logic Model and Kalman Filter

Processes ◽

10.3390/pr6080103 ◽

2018 ◽

Vol 6 (8) ◽

pp. 103 ◽

Cited By ~ 12

Author(s):

Muhammad Fayaz ◽

Israr Ullah ◽

Do-Hyeun Kim

Keyword(s):

Fuzzy Logic ◽

Kalman Filter ◽

Fuzzy Inference ◽

Risk Index ◽

Significant Risk ◽

Assessment Method ◽

Logic Model ◽

Membership Functions ◽

Inference System ◽

Fuzzy Logic Model

Normally, most of the accidents that occur in underground facilities are not instantaneous; rather, hazards build up gradually behind the scenes and are invisible due to the inherent structure of these facilities. An efficient inference system is highly desirable to monitor these facilities to avoid such accidents beforehand. A fuzzy inference system is a significant risk assessment method, but there are three critical challenges associated with fuzzy inference-based systems, i.e., rules determination, membership functions (MFs) distribution determination, and rules reduction to deal with the problem of dimensionality. In this paper, a simplified hierarchical fuzzy logic (SHFL) model has been suggested to assess underground risk while addressing the associated challenges. For rule determination, two new rule-designing and determination methods are introduced, namely average rules-based (ARB) and max rules-based (MRB). To determine efficient membership functions (MFs), a module named the heuristic-based membership functions allocation (HBMFA) module has been added to the conventional Mamdani fuzzy logic method. For rule reduction, a hierarchical fuzzy logic model with a distinct configuration has been proposed. In the simplified hierarchical fuzzy logic (SHFL) model, we have also tried to minimize rules as well as the number of levels of the hierarchical structure fuzzy logic model. After risk index assessment, the risk index prediction is carried out using a Kalman filter. The prediction of the risk index is significant because it could help caretakers to take preventive measures in time and prevent underground accidents. The results indicate that the suggested technique is an excellent choice for risk index assessment and prediction.

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Prediction of Risk of Wet-Pavement Accidents: Fuzzy Logic Model

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1717-05 ◽

2000 ◽

Vol 1717 (1) ◽

pp. 28-36 ◽

Cited By ~ 16

Author(s):

Jie Xiao ◽

Bohdan T. Kulakowski ◽

Moustafa EI-Gindy

Keyword(s):

Fuzzy Logic ◽

Nonlinear Regression ◽

Probabilistic Models ◽

Fuzzy Inference ◽

Logic Model ◽

Nonlinear Regression Models ◽

Fuzzy Logic Model ◽

Logic Models ◽

Fuzzy Clustering Method ◽

Accident Data

Researchers developed a fuzzy-logic model for predicting the risk of accidents that occur on wet pavements. Preventing wet-pavement accidents has been an extremely difficult and elusive task because they are stochastic events whose occurrence is related to a variety of factors, including vehicle, roadway, human, and environmental characteristics. Conventionally, researchers use linear or nonlinear regression models and probabilistic models to predict wet-pavement accidents. However, these models often are limited in their capability to fully explain the process when the underlying physical system possesses a degree of non-linearity. Therefore, the potential of applying fuzzy logic in this area might be promising. Two fuzzy-logic models were developed and evaluated using accident data and the corresponding traffic data collected from 123 sections of highway in Pennsylvania from 1984 to 1986. The models use skid number, posted speed, average daily traffic, percentage of wet time, and driving difficulty as input variables and the number of wet-pavement accidents as the output variable. The first model is based on Mamdani’s fuzzy-inference method, and the second is a Sugeno-type fuzzy-logic model using the fuzzy-clustering method. The two fuzzy-logic models show superiority over the probabilistic model and the nonlinear regression model. Results indicate that, in addition to predicting the risk of wet-pavement accidents, the fuzzy-logic model can be applied conveniently to determine specific corrective actions that should be undertaken to improve safety.

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A Fuzzy Logic Model to Predict Coral Reef Development under Nutrient and Sediment Stress

Conservation Biology ◽

10.1046/j.1523-1739.1998.96421.x ◽

1998 ◽

Vol 12 (5) ◽

pp. 957-965 ◽

Cited By ~ 41

Author(s):

Erik H. Meesters ◽

Rolf P. M. Bak ◽

Susie Westmacott ◽

Mark Ridgley ◽

Steve Dollar

Keyword(s):

Fuzzy Logic ◽

Coral Reef ◽

Logic Model ◽

Reef Development ◽

Fuzzy Logic Model

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A Fuzzy Logic Model for Hourly Electrical Power Demand Modeling

Electronics ◽

10.3390/electronics10040448 ◽

2021 ◽

Vol 10 (4) ◽

pp. 448

Author(s):

Marco Antonio Islas ◽

José de Jesús Rubio ◽

Samantha Muñiz ◽

Genaro Ochoa ◽

Jaime Pacheco ◽

...

Keyword(s):

Fuzzy Logic ◽

New England ◽

Electrical Power ◽

Logic Model ◽

Power Demand ◽

Demand Modeling ◽

Mapping Model ◽

Fuzzy Logic Model ◽

Radial Basis

In this article, a fuzzy logic model is proposed for more precise hourly electrical power demand modeling in New England. The issue that exists when considering hourly electrical power demand modeling is that these types of plants have a large amount of data. In order to obtain a more precise model of plants with a large amount of data, the main characteristics of the proposed fuzzy logic model are as follows: (1) it is in accordance with the conditions under which a fuzzy logic model and a radial basis mapping model are equivalent to obtain a new scheme, (2) it uses a combination of the descending gradient and the mini-lots approach to avoid applying the descending gradient to all data.

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