Developing the composed probability model to predict household trip production (a case study of Isfahan city)
Household trip production is not a constant parameter and vary based on socio‐economic characteristics. Even households produce several numbers of trips in each category (households with constant socio‐economic characteristics). The purpose of the present study is to model a variation of household trip production rate in statistical societies. To achieve the purpose, a concept of Bayesian Inference has been used. The city of Isfahan was selected as a case study. First, the likelihood distribution function was determined for average household trip production. Then, likelihood distribution was determined for the numbers of household trips separating odd and even trips. In order to increase the precision of the function, the concept of Bayesian inference was utilized. To insert household socio‐economic variables in the function, the disaggregate model was calibrated for average household trip production. Statistical indices and χ2 test show that the likelihood distribution function of average household trip production follows the gamma distribution and the numbers of household trip production follows the poisson distribution. The final composed probability distribution was determined on the basis of Bayesian inference. The related function was created with a compilation of the mean parameter distribution function (gamma distribution) and the numbers of household trip production (poisson distribution). Finally, the disaggregate model was inserted to the final composed probability function instead of the mean parameter. The obtained results show that the use of Bayesian inference method would open up the possibility of modeling the variation of household trip production rate in statistical societies. Also it would be possible to insert socio‐economic characteristics in the model to predict the likelihood of real produced trips for each category of household.
First published online: 27 Oct 2010
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