scholarly journals PEDESTRIAN LEVEL OF SERVICE CRITERIA FOR URBAN OFF-STREET FACILITIES IN MID-SIZED CITIES

Transport ◽  
2014 ◽  
Vol 32 (2) ◽  
pp. 221-232 ◽  
Author(s):  
Rima Sahani ◽  
Prasanta Kumar Bhuyan
Keyword(s):  
Fuzzy Clustering ◽  
Flow Rate ◽  
Clustering Analysis ◽  
Classification Problem ◽  
Optimal Number ◽  
Level Of Service ◽  
Self Organizing Map ◽  
Clustering Methods ◽  
Indian Context ◽  
Pedestrian Level Of Service

Levels Of Service (LOS) evaluation criteria for off-street pedestrian facilities are not well defined in urban Indian context; hence an in-depth research is carried out in this regard. Defining Pedestrian Level of Service (PLOS) criteria is basically a classification problem; therefore a comparative study is made using three methods of clustering i.e. Affinity Propagation (AP), Self-Organizing Map (SOM) in Artificial Neural Network (ANN) and Genetic AlgorithmFuzzy (GA-Fuzzy) clustering. Pedestrian data are used on validation measure of clustering method to obtain optimal number of cluster used in defining PLOS categories. To decide the most suitable algorithm applicable in defining PLOS criteria for urban off-street facilities in Indian context, Wilk’s Lambda is used on results of the three clustering methods. It is observed from the analysis that GA-Fuzzy is the most suitable clustering analysis among the three methods. With the help of GA-Fuzzy clustering analysis the ranges of the four measuring parameters (average pedestrian space, flow rate, speed of pedestrian and volume to capacity ratio) are defined by using the data collected from two mid-sized cities located in the state of Odisha, India. It is also observed that at >16.53 m2/ped average space, ≤0.061 ped/sec/m flow rate, >1.21 speed and ≤0.34 v/c ratio pedestrians can move in their desired path at LOS ‘A’ without changing movements and it is the best condition for off-street facilities. But in the pedestrian facility having ≤4.48 m2/ped average space, >0.146 ped/sec/m flow rate, ≤0.62 average speed and >1.00 v/c ratio, pedestrian movement is severely restricted and frequent collision among users occurs. The ranges of the parameters used for LOS categories found in this study for Indian cities are different from that mentioned in HCM (Highway Capacity Manual 2010) because of differences in population density, traffic flow condition, geometric structure and some other factors.

Research on Fuzzy Clustering Validity

2010 ◽  
Vol 40-41 ◽  
pp. 174-182
Author(s):  
Wei Jin Chen ◽  
Huai Lin Dong ◽  
Qing Feng Wu ◽  
Ling Lin
Keyword(s):  
Cluster Analysis ◽  
Fuzzy Clustering ◽  
Clustering Analysis ◽  
Optimal Number ◽  
Number Of Clusters ◽  
Fuzzy Partition ◽  
Geometry Structure ◽  
Clustering Validity ◽  
Optimal Number Of Clusters

The evaluation of clustering validity is important for clustering analysis, and is one of the hottest spots of cluster analysis. The quality of the evaluation of clustering is that optimal number of clusters is reasonable. For fuzzy clustering, the paper surveys the widely known fuzzy clustering validity evaluation based on the methods of fuzzy partition, geometry structure and statistics.

scholarly journals Pattern Recognition Using Clustering Analysis to Support Transportation System Management, Operations, and Modeling

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Rajib Saha ◽  
Mosammat Tahnin Tariq ◽  
Mohammed Hadi ◽  
Yan Xiao
Keyword(s):  
Clustering Analysis ◽  
Principal Component ◽  
Transportation System ◽  
Optimal Number ◽  
Mixed Data ◽  
Limited Information ◽  
Clustering Methods ◽  
Traffic Patterns ◽  
Traffic Pattern ◽  
Result Analysis

There has been an increasing interest in recent years in using clustering analysis for the identification of traffic patterns that are representative of traffic conditions in support of transportation system operations and management (TSMO); integrated corridor management; and analysis, modeling, and simulation (AMS). However, there has been limited information to support agencies in their selection of the most appropriate clustering technique(s), associated parameters, the optimal number of clusters, clustering result analysis, and selecting observations that are representative of each cluster. This paper investigates and compares the use of a number of existing clustering methods for traffic pattern identifications, considering the above. These methods include the K-means, K-prototypes, K-medoids, four variations of the Hierarchical method, and the combination of Principal Component Analysis for mixed data (PCAmix) with K-means. Among these methods, the K-prototypes and K-means with PCs produced the best results. The paper then provides recommendations regarding conducting and utilizing the results of clustering analysis.

scholarly journals Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics Data

2009 ◽  
Vol 2009 ◽  
pp. 1-16 ◽  
Author(s):  
David J. Miller ◽  
Carl A. Nelson ◽  
Molly Boeka Cannon ◽  
Kenneth P. Cannon
Keyword(s):  
Fuzzy Clustering ◽  
Real World ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Optimum Number ◽  
Clustering Methods ◽  
Real World Data ◽  
Data Set ◽  
World Data

Fuzzy clustering algorithms are helpful when there exists a dataset with subgroupings of points having indistinct boundaries and overlap between the clusters. Traditional methods have been extensively studied and used on real-world data, but require users to have some knowledge of the outcome a priori in order to determine how many clusters to look for. Additionally, iterative algorithms choose the optimal number of clusters based on one of several performance measures. In this study, the authors compare the performance of three algorithms (fuzzy c-means, Gustafson-Kessel, and an iterative version of Gustafson-Kessel) when clustering a traditional data set as well as real-world geophysics data that were collected from an archaeological site in Wyoming. Areas of interest in the were identified using a crisp cutoff value as well as a fuzzyα-cut to determine which provided better elimination of noise and non-relevant points. Results indicate that theα-cut method eliminates more noise than the crisp cutoff values and that the iterative version of the fuzzy clustering algorithm is able to select an optimum number of subclusters within a point set (in both the traditional and real-world data), leading to proper indication of regions of interest for further expert analysis

A New Algorithm for Fuzzy Clustering Handling Incomplete Dataset

2014 ◽  
Vol 23 (04) ◽  
pp. 1460012 ◽  
Author(s):  
Balkis Abidi ◽  
Sadok Ben Yahia
Keyword(s):  
Fuzzy Clustering ◽  
A Priori ◽  
Real Life ◽  
Optimal Number ◽  
Statistical Criterion ◽  
Clustering Methods ◽  
Number Of Clusters ◽  
Missing Value ◽  
Value Estimation ◽  
Optimal Number Of Clusters

One of the most difficult problems in cluster analysis is the identification of the number of groups in a dataset especially in the presence of missing value. Since traditional clustering methods assumed the real number of clusters to be known. However, in real world applications the number of clusters is generally not known a priori. Also, most of clustering methods were developed to analyse complete datasets, they cannot be applied to many practical problems, e.g., on incomplete data. This paper focuses, first, on an algorithm of a fuzzy clustering approach, called OCS-FSOM. The proposed algorithm is based on neural network and uses Optimal Completion Strategy for missing value estimation in incomplete dataset. Then, we propose an extension of our algorithm, to tackle the problem of estimating the number of clusters, by using a multi level OCS-FSOM method. The new algorithm called Multi-OCSFSOM is able to find the optimal number of clusters by using a statistical criterion, that aims at measuring the quality of obtained partitions. Carried out experiments on real-life datasets highlights a very encouraging results in terms of exact determination of optimal number of clusters.

Analysis of Fuzzy Clustering for the Adoption in Data Mining

2014 ◽  
Vol 989-994 ◽  
pp. 2047-2050
Author(s):  
Ying Jie Wang
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Mathematical Method ◽  
Big Data ◽  
Fuzzy Clustering ◽  
Clustering Analysis ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Clustering Methods ◽  
Fuzzy Clustering Methods

Data mining is the general methodology for retrieving useful information from big data. Clustering analysis is a mathematical method of classification for unsupervised machine learning. It can be adopted for data classification in Data mining. This paper combines the clustering process by fuzzy way and then deduces a special clustering algorithm with fast fuzzy c-means (FFCM) method. In summary, the paper illustrates the adoption of a series of fuzzy clustering methods in Data Mining. These methods have improved the computational efficiency with learning as the convergence speed is fast. The methodology of this paper presents significantly meaningful for information retrieval of big data.

scholarly journals Application of Cluster Analysis to Define Level of Service Criteria of U-turns at Median Openings

2021 ◽  
Vol 81 ◽  
pp. 1-17
Author(s):  
Smruti Sourava Mohapatra
Keyword(s):  
Clustering Algorithm ◽  
Microscopic Analysis ◽  
Optimal Number ◽  
Level Of Service ◽  
Clustering Methods ◽  
Traffic Sign ◽  
Service Delay ◽  
Validation Parameters ◽  
Merging Process ◽  
Median Opening

Defining Level of Service (LOS) criteria of U-turns is important for proper planning, design of transportation projects and also allocating resources. The present study attempts to establish a framework to define LOS criteria of U-turns keeping in mind the peculiar behavior of drivers and heterogeneity in urban Indian context. The U-turns at uncontrolled (no traffic sign, no signal, no traffic personnel) median openings are very risky. Upon arrival at the median opening, the U-turning vehicle looks for a suitable gap in the approaching traffic stream before initiating the merging process. While waiting for a suitable gap the U-turning vehicle experiences service delay. This service delay has been studied to quantify the delay ranges for different LOS categories. In this study, service delay data were collected from 7 different sections and microscopic analysis procedure was adopted to extract data from the recorded video. Subsequently, clustering technique has been utilized to defining delay ranges of different level of service categories. Four clustering methods, namely; K-mean, K-medoid, Affinity Propagation (AP), and Fuzzy C-means (FCM) are used. Four validation parameters are applied to determine most suitable clustering algorithm for the study and to determine the optimal number of cluster. AP was found to be the most suitable clustering method and 6 was found to be the optimal number and accordingly the collected delay data were clustered into 6 categories using AP. The delay range is found to be less than 4 s for LOS A is greater than 35 s for LOS F.

scholarly journals Optimal pressure management in water distribution networks through district metered area creation based on machine learning

RBRH ◽  
2019 ◽  
Vol 24 ◽  
Author(s):  
Bernardo Novarini ◽  
Bruno Melo Brentan ◽  
Gustavo Meirelles ◽  
Edevar Luvizotto Junior
Keyword(s):  
Water Supply ◽  
Water Distribution ◽  
Integrated Management ◽  
Distribution Networks ◽  
Optimal Number ◽  
Water Supply Systems ◽  
Self Organizing Map ◽  
Clustering Methods ◽  
Pipe Length ◽  
Demand Node

ABSTRACT Integrated management of water supply systems with efficient use of natural resources requires optimization of operational performances. Dividing the water supply networks into small units, so-called district metered areas (DMAs), is a strategy that allows the development of specific operational rules, responsible for improving the network performance. In this context, clustering methods congregate neighboring nodes in groups according to similar features, such as elevation or distance to the water source. Taking into account hydraulic, operational and mathematical criteria to determine the configuration of DMAs, this work presents the k-means model and a hybrid model, that combines a self-organizing map (SOM) with the k-means algorithm, as clustering methods, comparing four mathematical criteria to determine the number of DMAs, namely Silhouette, GAP, Calinski-Harabasz and Davies Bouldin. The influence of three clustering topological criteria is evaluated: the water demand, node elevation and pipe length, in order to determine the optimal number of clusters. Furthermore, to identify the best DMA configuration, the particle swarm optimization (PSO) method was applied to determine the number, cost, pressure setting of Pressure Reducing Valves and location of DMA entrances.

Clustering Count-based RNA Methylation Data Using a Nonparametric Generative Model

2018 ◽  
Vol 14 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Lin Zhang ◽  
Yanling He ◽  
Huaizhi Wang ◽  
Hui Liu ◽  
Yufei Huang ◽  
...  
Keyword(s):  
Clustering Analysis ◽  
Methylation Level ◽  
Optimal Number ◽  
Generative Model ◽  
Methylation Data ◽  
Sequencing Data ◽  
Number Of Clusters ◽  
Rna Methylation ◽  
Clustering Effect ◽  
Optimal Number Of Clusters

Background: RNA methylome has been discovered as an important layer of gene regulation and can be profiled directly with count-based measurements from high-throughput sequencing data. Although the detailed regulatory circuit of the epitranscriptome remains uncharted, clustering effect in methylation status among different RNA methylation sites can be identified from transcriptome-wide RNA methylation profiles and may reflect the epitranscriptomic regulation. Count-based RNA methylation sequencing data has unique features, such as low reads coverage, which calls for novel clustering approaches. <P><P> Objective: Besides the low reads coverage, it is also necessary to keep the integer property to approach clustering analysis of count-based RNA methylation sequencing data. <P><P> Method: We proposed a nonparametric generative model together with its Gibbs sampling solution for clustering analysis. The proposed approach implements a beta-binomial mixture model to capture the clustering effect in methylation level with the original count-based measurements rather than an estimated continuous methylation level. Besides, it adopts a nonparametric Dirichlet process to automatically determine an optimal number of clusters so as to avoid the common model selection problem in clustering analysis. <P><P> Results: When tested on the simulated system, the method demonstrated improved clustering performance over hierarchical clustering, K-means, MClust, NMF and EMclust. It also revealed on real dataset two novel RNA N6-methyladenosine (m6A) co-methylation patterns that may be induced directly by METTL14 and WTAP, which are two known regulatory components of the RNA m6A methyltransferase complex. <P><P> Conclusion: Our proposed DPBBM method not only properly handles the count-based measurements of RNA methylation data from sites of very low reads coverage, but also learns an optimal number of clusters adaptively from the data analyzed. <P><P> Availability: The source code and documents of DPBBM R package are freely available through the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/DPBBM/.

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