A Fuzzy Set Approach to Estimating Od Matrices in Congested Brazilian Traffic Networks (original) (raw)

A Variable Fuzzy Sets Model for Evaluating Urban Public Traffic Network

2011

Abstract: Evaluating urban public traffic network is a multi-criteria problem with interval numbers as eigenvalue indexes. Therefore, a variable fuzzy sets model for is proposed in this paper. In this model, the interval-number-based relative deference degree function is used to determine the relative degree and global relative membership degree firstly. Then the category eigenvalue is used to reflect the statement of urban public traffic. Finally, a case study is given by the proposed model. The results show that the proposed model is a reasonable and effective method for multi-criteria and interval-number evaluation problems.

Fuzzy-Based Approach for Assessing Traffic Congestion in Urban Areas

Image and Signal Processing, 2020

The very rapid evolution of urban areas leads to a reflection on the citizens’ mobility inside the cities. This mobility problem is highlighted by the increase in terms of time, distance and social and economic costs, whereas the congestion management approach implemented rarely meets the road users’ expectations. To overcome this problem, a novel approach for evaluating urban traffic congestion is proposed. Factors such as the imprecision of traffic records, the user’s perception of the road’s level of service provided and variation in sample data are mandatory to describe the real traffic condition. To respond to these requirements, a fuzzy inference-based method is suggested. It combines three independent congestion measures which are: speed ratio, volume to capacity ratio and decreased speed ratio into a single composite measure which is the congestion index. To run the proposed fuzzy model, the traffic dataset of Austin-Texas is used. Although it is still not possible to determ...

Modeling of traffic congestion on urban road network using fuzzy inference system

Traffic congestion is a complex issue which most of metro cities are experiencing. The degree of congestion on urban links is not always measured & treated uniformly as it is not well defined phenomenon. The traditionalapproaches are unable to represent realistic& true traffic condition and leads to deviation in congestion measurement because of various factors such as imprecision of the measurement, the traveller's perception of acceptability, variation in sample data, and the analyst's uncertainty about causal relations. To overcome this, fuzzy inference approach is proposed in which, three input parameter i.e. speed reduction rate, proportion of time traveling at very low speed (below 5 kmph) compared with total travel time and traffic volume to roadway capacity ratio are combined to get single output in term of congestion index. The proposed model is demonstrated by considering real time traffic data on major road network of Nagpur city, India. This study allows the process to combine different measures and also to incorporate the uncertainty in the individual measures so that the composite picture of congestion can be reproduced with greater accuracy & low error margin.

Multi Model Criteria for the Estimation of Road Traffic Congestion from Traffic Flow Information Based on Fuzzy Logic

Journal of Transportation Technologies, 2012

In this study, the road traffic congestion of Dehradun city is evaluated from traffic flow information using fuzzy techniques. Three different approaches namely Sugeno, Mamdani models which are manually tuned techniques, and an Adaptive Neuo-Fuzzy Inference System (ANFIS) which an automated model decides the ranges and parameters of the membership functions using grid partition technique, based on fuzzy logic. The systems are designed to human's feelings on inputs and output levels. There are three levels of each input namely high, medium and low for input density, fast, medium and slow for input speed, and five levels of output namely free flow, slow moving, mild congestion, heavy congestion and serious jam for the road traffic congestion estimation. The results, obtained by fuzzy based techniques show that the manually tuned Sugeno type technique achieves 72.05% accuracy, Mamdani type technique achieves 83.82% accuracy, and Adaptive Neuro-Fuzzy Inference System technique achieves 88.23% accuracy. ANFIS technique appears better than the manually tuned fuzzy technique, and also the manually tuned fuzzy technique gives good accuracy which leads that the fuzzy inference system can capture the human perception better through manual adjustment of input/output membership functions.

A Practical O-D Matrix Estimation Model Based on Fuzzy Set Theory for Large Cities

In this paper, a new fuzzy O-D matrix estimation model (FODMEM) is proposed to estimate the O-D matrix from traffic count. A gradient-based algorithm, containing a fuzzy rule based approach to control the estimated O-D matrix changes, is proposed to solve FODMEM. Since link data only represents a snapshot situation, resulting in inconsistency of data and poor quality of the estimated O-D's, the proposed method considers link data as fuzzy values that vary within a certain bandwidth. An equilibrium based fuzzy assignment method is proposed to assign the estimated O-D matrix, which causes the assigned volumes to be fuzzy numbers. The shortest path algorithm of the proposed method is similar to the Floyd-Warshall algorithm, and we call it the Fuzzy Floyd-Warshall Algorithm (FFWA). We introduce a new fuzzy comparing index to compare and estimate the distance between the assigned and observed link volumes and the model is formulated based on this index. FODMEM is implemented in Mashhad city in Iran. Real data obtained from Mashhad Comprehension Transportation Study (MCTS) are used in this study and results are presented to show high capability of FODMEM to estimate O-D matrix in large networks.

Estimation of Origin – Destination Matrix from Traffic Counts Based On Fuzzy Logic

Determining trip demand matrix is among the basic data in transportation planning. This matrix is derived by surveys, interviews with citizens or questionnaires that required time, money and manpower. Thus, in recent years, demand estimation methods based on network information is taken into consideration. In these methods with the information including: volume, travel time, capacity of the links and initial demand matrix it is possible to estimate the demand matrix. In this paper, we removed the additional parameters in previous studies and used a simple solution to estimate the matrix. This paper proposes a Fuzzy-PFE estimation method that allows to improve the estimation performances of PFE estimator. The objective function presented based on the reduction of travel time and travel time of routs in networks is uncertain. The method is developed by fuzzy sets theory and fuzzy programming that seems to be convenient theoretical framework to represent uncertainty in the available data. The new model is the removal of iterative process of origin-destination matrix estimation using travel time and increase convergence of the model for the large-scale and congested networks by applying little changes in the basic model. In this paper we used TRANSCAD Software to determine the shortest path in the network and optimization of objective function is performed by CPLEX.

A fuzzy multicriteria method for ranking the factors that influence the settlement of Brazilian highway speed limits

TRANSPORTES

O processo de definição da velocidade limite em rodovias envolve uma série de fatores, não claramente definidos em termos de uma ordem de relevância que possibilite o especialista – responsável por tomar decisão – optar pela melhor escolha. Como o estabelecimento de um limite de velocidade se caracteriza pela subjetividade, sendo um processo regido pela incerteza e pela imprecisão, a lógica fuzzy mostra-se como alternativa para a solução deste problema. Assim, especialistas da área foram questionados quanto à influência de fatores pré-determinados na velocidade limite de rodovias e, em seguida, foi aplicado um método fuzzy multicritério, otimizado por meio de um algoritmo genético, para que as variáveis fossem hierarquizadas em termos de pesos e, consequentemente, relevância. O método convergiu a resultados satisfatórios, possibilitando não somente a concepção de um sistema especialista para o estabelecimento de velocidades limites, mas também aplicações em outras áreas.

Assessment of traffic congestion with ORESTE method under double hierarchy hesitant fuzzy linguistic environment

Applied Soft Computing, 2019

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Mathematical Formulation for Managing Traffic Congestion for a Sector of a Route using Fuzzy Logic

Traffic management has been a major problem in all the developing as well as developed countries. Governments have been spending hefty amounts to manage various aspects of traffic management using different methodologies. In this paper, a mathematical method is proposed to formulate congestion on a section of a main road having n number of possible alternate routes. This mathematical formulation would allow the decision makers to manage and give signals from far off control units. The article further tries to propose a possible solution involving the derived mathematical formulae and the concepts of fuzzy logic.