Article
SELF ORGANIZING MAP OF ARTIFICIAL NEURAL NETWORK FOR DEFINING LEVEL OF SERVICE CRITERIA OF URBAN STREETS
DOI: 10.7708/ijtte.2012.2(3).06
2 / 3 / 236-252 Pages
Author(s)
Smruti Sourava Mohapatra - Department of Civil Engineering, National Institute of Technology, Rourkela, India 769008 -
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.
Number of downloads: 3072
Keywords:
Self Organizing Map (SOM);
Artificial Neural Network (ANN);
Level of Service (LOS);
urban streets;
GPS;
clustering;
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