Self Organizing Map of Artificial Neural Network for Defining Level of Service Criteria of Urban Streets

Abstract

Abstract:In India, Level of Service (LOS) is not well defined for urban streets. The analysis procedure followed in India is that developed by HCM 2000. Speed ranges of LOS categories for various urban Street Classes defined by HCM are appropriate for developed countries having homogenous type of traffic flow. India being a developing country its traffic is very much heterogeneous having vehicles of different operational characteristics. Therefore, LOS criteria in Indian context should be defined correctly considering the traffic and geometric characteristics of urban streets. Defining LOS is basically a classification problem and application of cluster analysis is found to be a suitable technique to solve the problem. Self Organizing Map (SOM) a type of Artificial Neural Network (ANN) used to solve this classification problem. For this study, lot of speed data is required for which GPS is found to be the most suitable method of data collection and hence extensively used. Free flow speed (FFS) and average travel speed during peak and off peak hours inventory of road segments are used in this study. FFS ranges for different urban Street Classes and speed ranges of LOS categories found to be lower than that mentioned in HCM-2000.

Unsupervised Neural Network-Naive Bayes Model for Grouping Data Regional Development Results

“…V1 is the variable for average growth, while V2 shows the average value of the contribution of the construction of the district, V3 and V4 respectively an average growth rate and the average contribution of the development owned by the province. Comparison of cluster results show, there are some results of the classification model dataset is built, showing the same classification number classification typology Klassen, as shown in8,9,16,17,18,21,22,24,32,33,35 dataset, 38,39,40,44 and 47. Comparison of the results showed similar clusters of 22.63%.The experimental results further demonstrate that the classification output of the model, determined by the amount of training data and the diversity of data held training data, especially related to the similarity and disimilarity data.…”

“…Both methods are combined to form a classification model that is capable of grouping the data on the development of an area. Some of previous studies using the concept of SOM for many grouping needs, for example for the grouping of digital image processing, both for segmentation [6], [7], compress the image without changing the quality of the input image while still producing good quality image compressed [8], clustering of documents [9], [10] the determination of a microbial taxonomy relation class [11], defining a strategy for grouping customer market share [12], analysis of strategic groups of construction companies to understand the strategic position of the company [13], predictive classification of computer network attacks [14], visualization of spatial data to find structure and pattern of data in order to obtain new information relationships between socio-economic indicator data of an area [15], the grouping capital road users based on peak and off peak time so it can be used as decision support in planning the construction of transportation facilities [16] can even be used to predict the possible locations of clarifying bedasarkan aftershock earthquake data trends in a region [17]. discusses the literature review and theory used in this study, the third part is the research methodology used and explains the research flow, the fourth section is a discussion of the results of research that contains a description of proposed model and simulation testing the model against the data sector GDP, and the last is the cover that contains conclusions and suggestions for further research.…”

TRAFFIC PERFORMANCE EVALUATION AND ANALYSIS OF Al-FALLAH INTERSECTION IN BAGHDAD CITY UTILIZING SYNCHRO.10 SOFTWARE

“…Lastly the existencemof services and government centers especially those that deal directly with the public, leads to the state of congestion, [2]. Determining the level of service (LOS) for urban street and intersections is very important, as the first step of analysis procedure, affects the planning, design, and operational phase of transportation projects as well as the allocation of limited financial resourcesmamong competing transportation projects, [3].…”