scholarly journals Floating Car Data and Fuzzy Logic for classifying congestion indexes in the city of Shanghai

2019 ◽  
Vol 2 ◽  
pp. 1-7
Author(s):  
Maja Kalinic ◽  
Jukka M. Krisp
Keyword(s):  
Fuzzy Logic ◽  
Traffic Congestion ◽  
Fuzzy Inference ◽  
Inference Model ◽  
Floating Car Data ◽  
The Individual ◽  
The City

<p><strong>Abstract.</strong> In this paper, we use Floating Car Data from the city of Shanghai and Fuzzy Inference model to detect congestion indexes throughout the city. We aim to investigate to which extent traffic congestion is severe during afternoon rush hour. Additionally, we compare our results to the ones obtained by calculating congestion indexes on conventional way. Although we do not argue that our model is the best measure of congestion, it does allow the mechanism to combine different measures and to incorporate the uncertainty in the individual measures so that the compound picture of congestion can be reproduced.</p>

scholarly journals Determining traffic congestion utilizing a fuzzy logic model and Floating Car Data (FCD)

2021 ◽  
Vol 4 ◽  
pp. 1-8
Author(s):  
Maja Kalinic ◽  
Jukka M. Krisp
Keyword(s):  
Fuzzy Logic ◽  
Traffic Congestion ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Linear Functions ◽  
Inference System ◽  
Floating Car Data ◽  
Federal Highway ◽  
Almost All

Abstract. Traffic congestion is a dynamic spatial and temporal process and as such might not be possible to model with linear functions of various dependent variables. That leaves a lot of space for non-linear approximates, such as neutral networks and fuzzy logic. In this paper, the focus is on the fuzzy logic as a possible approach for dealing with the problems of measuring traffic congestion. We investigate the application of this framework on a selected case study, and use floating car data (FCD) collected in Augsburg, Germany. A fuzzy inference system is built to detect degrees of congestion on a federal highway B17. With FCD, it is possible to obtain local speed information on almost all parts of the network. This information, together with collected vehicle location, time and heading, can be further processed and transformed into valuable information in the form of trip routes, travel times, etc. Initial results are compared with traditional method of expressing levels of congestion on a road network e.g. Level of Service – LOS. The fuzzy model, with segmented mean speed and travel time parameters, performed well and showed to be promising approach to detect traffic congestions. This approach can be further improved by involving more input parameters, such as density or vehicle flow, which might reflect traffic congestion event even more realistically.

scholarly journals DEVELOPMENT OF A SYSTEM FOR ASSESSING PASSENGER TRAFFIC BASED ON FUZZY LOGIC

2020 ◽  
Vol 4 ◽  
pp. 18-21
Author(s):  
Olha Pronina
Keyword(s):  
Mathematical Model ◽  
Fuzzy Logic ◽  
Public Transport ◽  
Fuzzy Inference ◽  
Technical Condition ◽  
Time Of Day ◽  
Output Variable ◽  
Passenger Traffic ◽  
Transportation Service ◽  
The City

A methodology has been developed for assessing public transport passenger traffic in the city. A mathematical model based on fuzzy logic is presented. The main criteria for assessing the attractiveness of passenger traffic are: the interval between vehicles, technical condition of the vehicle, route length, time of day. In the mathematical model, all input linguistic variables and output variable, their terms and membership functions are described. A fragment of a fuzzy knowledge base presented in the form of production rules is presented. At the exit, the dispatcher receives an output variable – the degree of confidence in the attractiveness of the route. Based on this assessment, the dispatcher can make a number of necessary changes to improve the functioning of the route. The software is implemented as a web service. This software will be convenient for dispatchers to use for planning public transport routes. Fifteen selected routes were taken for research, which are the most popular in the city. These routes were proposed for evaluation by three controllers. The results obtained from dispatchers were compared with the results of the fuzzy inference implemented in the software. The main advantage of using this software product is the ability to build a dynamic schedule based on the analysis of the dispatcher. This, in turn, will allow passengers to receive a better transportation service within the city

scholarly journals Crop Recommender System for the Farmers using Mamdani Fuzzy Inference Model

2018 ◽  
Vol 7 (4.15) ◽  
pp. 277 ◽  
Author(s):  
Madhusree Kuanr ◽  
Bikram Kesari Rath ◽  
Sachi Nandan Mohanty
Keyword(s):  
Fuzzy Logic ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Recommender System ◽  
Fuzzy Inference ◽  
Weather Condition ◽  
Cosine Similarity ◽  
Inference Model ◽  
Large Pool ◽  
Cosine Similarity Measure

Recommender systems provide suggestions to the users for choosing particular items from a large pool of items. The purpose of this study is to design a collaborative recommender system for the farmers for recommending giving prior idea regarding a crop which is suitable according to the location of the farmer based on weather condition of the previous months. The proposed system also recommends other seeds, pesticides and instruments according to the preferences in farming and location of the farmers while purchasing the seeds through online. It uses cosine similarity measure to find the similar user according the location of the farmer and fuzzy logic for predicting the yield of rice crop for Kharif season in state Odisha, India. The proposed system is implemented in Mamdani Fuzzy Inference model. The results reveal that it provides prior idea regarding a crop before sowing of seeds.  

scholarly journals A Mamdani Type Fuzzy Inference System to Calculate Employee Susceptibility to Phishing Attacks

2021 ◽  
Vol 11 (19) ◽  
pp. 9083
Author(s):  
Yahya Lambat ◽  
Nick Ayres ◽  
Leandros Maglaras ◽  
Mohamed Amine Ferrag
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Human Error ◽  
Fuzzy Inference ◽  
Social Engineering ◽  
Rule Base ◽  
Inference System ◽  
Phishing Attacks ◽  
Future System ◽  
The Individual

It is a well known fact that the weakest link in a cyber secure system is the people who configure, manage or use it. Security breaches are persistently being attributed to human error. Social engineered based attacks are becoming more sophisticated to such an extent where they are becoming increasingly more difficult to detect. Companies implement strong security policies as well as provide specific training for employees to minimise phishing attacks, however these practices rely on the individual adhering to them. This paper explores fuzzy logic and in particular a Mamdani type fuzzy inference system to determine an employees susceptibility to phishing attacks. To negate and identify the susceptibility levels of employees to social engineering attacks a Fuzzy Inference System FIS was created through the use of fuzzy logic. The utilisation of fuzzy logic is a novel way in determining susceptibility due to its ability to resemble human reasoning in order to solve complex inputs, or its Interpretability and simplicity to be able to compute with words. This proposed fuzzy inference system is based on a number of criteria which focuses on attributes relating to the individual employee as well as a companies practices and procedures and through this an extensive rule base was designed. The proposed scoring mechanism is a first attempt towards a holistic solution. To accurately predict an employees susceptibility to phishing attacks will in any future system require a more robust and relatable set of human characteristics in relation to the employee and the employer.

scholarly journals Implementation of Fuzzy Mamdani Method for Traffic Lights Smart City in Rangkasbitung, Lebak Regency, Banten Province (Case Study of the Traffic Light T-junction, Cibadak, By Pas Sukarno Hatta Street)

KOMTEKINFO ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 176-185
Author(s):  
Dentik Karyaningsih ◽  
Robby Rizky
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Traffic Congestion ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Light Control ◽  
Traffic Light ◽  
Traffic Lights ◽  
Traffic Jams ◽  
Traffic Light Control

Traffic jams are a common sight that can be seen in almost all major cities in Indonesia. One of them is in Rangkasbitung City, Lebak Regency. This happens because the number of vehicles continues to increase. The traffic light control system implemented in Indonesia is a static preset time because the time of each phase is predetermined. This type of control system is still not effective in overcoming traffic congestion, especially at certain peak traffic jams. By using the Mamdani fuzzy logic system, it is possible to implement the human mindset into a system. Some rules can be set out in the fuzzy logic controller. The purpose of this study is to design a traffic light control system using fuzzy inference that regulates traffic based on its density. The data used are observations made at the research site. The conclusion of this study is to explain that the fuzzy mamdani method can solve existing problems in traffic congestion in Rangkasbitung City, Lebak Regency, Banten Province

Developing a Measure of Traffic Congestion: Fuzzy Inference Approach

2002 ◽  
Vol 1802 (1) ◽  
pp. 77-85 ◽  
Author(s):  
Khaled Hamad ◽  
Shinya Kikuchi
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Fuzzy Inference ◽  
Supply And Demand ◽  
Travel Speed ◽  
Flow Speed ◽  
Inference Process ◽  
Real World Data ◽  
Total Travel Time ◽  
The Individual

Many measures have been proposed to represent the status of traffic conditions on arterial roadways in urban areas. The debate about what is the most appropriate measure continues. In a contribution to the debate, another approach was offered. Traditionally, two general approaches exist. One is based on the relationship between supply and demand. The other is a measure relative to the most acceptable status of service quality. The latter measure allows the public to relate to their travel experience. In either case, however, derivation of measures of congestion involves uncertainty because of imprecision of the measurement, the traveler’s perception of acceptability, variation in sample data, and the analyst’s uncertainty about causal relations. A measure is proposed that is a composite of two traditional measures, travel speed and delay. In recognition of the uncertainty, a fuzzy inference process was proposed. The inputs are travel speed, free-flow speed, and the proportion of very low speed in the total travel time. These values were processed through fuzzyrule-based inference. The outcome was a single congestion index value between 0 and 1, where 0 is the best condition and 1 is the worst condition. The process was demonstrated using real-world data. The results were compared with those of the Highway Capacity Manual. Although no conclusion can be drawn about the best measure of congestion, the proposed inference process allows the mechanism to combine different measures and also to incorporate the uncertainty in the individual measures so that the composite picture of congestion can be reproduced.

scholarly journals Factory Location Decision Making Based on the FUZZY Inference Model

2019 ◽  
Vol 7 (10) ◽  
pp. 968-978
Author(s):  
Alexandre Vieira de Oliveira ◽  
David Barbosa de Alencar ◽  
Alexandra Priscilla Tregue Costa ◽  
Manoel Henrique Reis Nascimento
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Case Studies ◽  
Fuzzy Inference ◽  
Location Decision ◽  
Production Engineering ◽  
Inference Model ◽  
The Subject

This paper introduces the concept of fuzzy logic, some terms used in this kind of logic, and uses it to evaluate and choose where to deploy factories and other enterprises. In addition, a model is made using the InFuzzy program to evaluate a choice of a location within the Manaus Industrial Pole - PIM, using objective and subjective criteria within the fuzzy logic. This article aims to present the fuzzy logic in the context of production engineering, select the parameters that define the best location, develop models that represent the subject in the study and verify the applicability by simulating other case studies and comparing results.

scholarly journals Market-neutral trading with fuzzy inference, a new method for the pairs trading strategy

2019 ◽  
Vol 30 (4) ◽  
pp. 411-421
Author(s):  
Mehmet Bayram ◽  
Muzaffer Akat
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Trading Strategy ◽  
Pairs Trading ◽  
Inference Model ◽  
Relative Pricing ◽  
Arbitrage Opportunities ◽  
Human Decision ◽  
Arbitrage Strategy

Financial pricing and prediction of stock markets is a specific and relatively narrow field, which have been mainly explored by mathematicians, economists and financial engineers. Prediction with the purpose of making profits in a martingale domain is a hard task. Pairs trading, a market neutral arbitrage strategy, attempts to resolve the drawback of unpredictability and yield market independent returns using relative pricing idea. If two securities have similar characteristics, so should their prices. Deviation from the acceptable similarity range in prices is considered an anomaly, and whenever noticed, trading is executed assuming the anomaly will correct itself.This work proposes a fuzzy inference model for the market-neutral pairs trading strategy. Fuzzy logic lets mimicking human decision-making in a complex trading environment and taking advantage of arbitrage opportunities that the crisp models may miss to acquire for the trade decision-making. Spread between two co-integrated stocks and volatility of the spread is used as decision-making inputs. Spread is a measure of the distance between two stocks and volatility is an indicator of how soon the spread would disappear. We conclude that fuzzy engine contributes to the profitability and efficiency of pairs trading type of strategies.

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