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Khaki A.m., Moayedfar R.,
Volume 2, Issue 2 (6-2004)
Abstract

The purpose of the present study, is proposing a more flexible model comparing with linear regression model, to estimate the rate of household trip production and its prediction in the�project horizon. For this purpose, a combined model composed of poission distribution and the possible distribution of A. in the form of negative binominal distribution are used. Then the proposed model was conducted on a real case (Karaj City). Then the result of model processinghas been compared to .the real observation in the peak hours in Karaj city.
Ayati E.,
Volume 2, Issue 2 (6-2004)
Abstract

Government agencies and the medical, insurance and automotive industries all have an interest in understanding the socio-economic costs of road crashes. These costs are estimated in most countries, and their computation methodology are continuously progressing as more refined costing methods, are used. This paper outlines two recent studies in Iran and Australia in order to compare crash cost estimation approaches. The analytical approaches and the results of similar studies in some other countries are also discussed It is shown that different approaches to estimating human costs and its components have considerable effects on the results. In both studies, the contribution to the total costs are human costs (50 to 60 percent), vehicle costs (30 percent) with the remaining 10 to 20 percent covering general costs.
A. Mansour Khaki, Sh. Afandizadeh, R. Moayedfar,
Volume 7, Issue 3 (9-2009)
Abstract

Household trip production is not a constant parameter and vary based on socio-economic characteristics. Even households in each category (households with constant socio-economic characteristics) produce several numbers of trips. Purpose of present study is to model the variation of household trip production rate in urban societies. In order to do this, concept of the Bayesian Inference has been used. The city of Isfahan was selected as case study. First, likelihood distribution function was determined for number of household trips, separating odd and even trips. In order to increase precision of the function, the composed likelihood distribution function was utilized. To insert households’ socio-economic variables in the process, disaggregate 2 calibrated model were used at the likelihood distribution function. Statistical indices and 2 test show that likelihood distribution function of numbers of household trip production follows the Poisson distribution. The final composed likelihood distribution was determined based on Bayesian inference. Related function was created with compilation of mean parameter distribution function (Gamma distribution) and numbers of household trip production (Poisson distribution). Finally, disaggregate model was put at final composed probability function instead of mean parameter. Results show that with Bayesian inference method, it would be possible to model the variation of household trip production rate in urban societies. Also it would be possible to put socio-economic characteristics in the model to predict likelihood of real produced trips (not average produced trips) for each household's category.

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