Paper Information

Journal:   URBAN MANAGEMENT   FALL 2014 , Volume 13 , Number 36; Page(s) 145 To 154.
 
Paper: 

USING ARTIFICIAL NEURAL NETWORK FOR FLOAT PRICING OF TEHRAN TRAFFIC TOLLS TO IMPROVE URBAN MANAGEMENT FOCUSED ON DECREASING AIR POLLUTION

 
 
Author(s):  ESHTEHARDIAN EHSAN*, FAEZIRAD MOHAMMAD ALI
 
* 
 
Abstract: 

Air pollution of Tehran is one the main urban concerns of the municipality and daily statistics of this pollution confirm the hazardous and unhealthy state of air contaminants of the city. A temporary tool of reliving air pollution through decreasing traffic jam is to develop traffic zones which their licenses may be sold for yearly, monthly or weekly periods. Currently, the prices are fixed whole throughout the period and this needs to be modified in order to take into account the varying prices in accordance with the varying level of air pollution.
So, this paper suggests a multi-price model for non-annual traffic toll by clustering months based on statistics of air contaminants of Tehran from 1390 to 1392. This clustering by SOM method is based on contaminants such as nitrogen dioxide (N02), sulfur dioxide (C02), carbon monoxide (CO), ozone (03) and suspended particulate matters (PMs). Exploiting this pricing method results in increase of the toll price during periods that level of pollution is high. This leads to decrease of traffic jam in that period which in turn decreases the level of air pollution.

 
Keyword(s): ARTIFICIAL NEURAL NETWORK (ANN), CLUSTERING, SELF-ORGANIZING MAP (SOM), AIR POLLUTANT, PRICING
 
References: 
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