Modeling transportation supply and demand forecasting using artificial intelligence parameters (bayesian model)
The growth of population has led to an increase in the number of motor vehicles. This increase has caused traffic congestions in metropolitan cities and higher fuel consumption besides other problems. A solution for dealing with the increase in the number of vehicles is creating more capacity for roads, parking lots, etc. thus aiming at controlling the traffi c problem. Providing proper approaches is on the condition that growth rate of vehicles in future years could be estimated by logical methods and so a relation between supply and demand at any time can be made. Considering this problem, in this paper one of the artifi cial intelligence parameters that is “Bayesian model” is introduced and by providing a model using this parameter, transportation situation (the ratio of capacity to demand) in future years can be forecasted. According to the results of this study, a model for supply and demand is suggested that can be used for traffi c design of different regions and sections. Additionally the necessary capacity for roads and other important parameters in transportation is estimated according to the estimated capacity.
The authors contributed to this work equally.
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