Trip generation is the first phase in the travel forecasting process. It involves the estimation of the
total number of trips entering or leaving a parcel of land per time period (usually on a daily basis);
as a function of the socioeconomic, locational, and land-use characteristics of the parcel.
The objective of this study is to develop statistical models to predict trips production volumes for a
proper target year. Non-motorized trips are considered in the modeling process. Traditional method
to forecast the trip generation volume according to trip rate, based on family type is proposed in
this study. Families are classified by three characteristics of population social class, income, and
number of vehicle ownership. The study area is divided into 10 sectors. Each sector is subdivided
into number of zones so; the total number of zones is 45 zones based on the administrative
divisions. The trip rate for the family is determined by sampling. A questionnaire is designed and
interviews are implemented for data collection from selected zones at Al-Karkh side of Baghdad
city. Two techniques have been used, full interview and home questionnaire. The questionnaire
forms are distributed in many institutes, intermediate, secondary and, commercial schools. The
developed models are total person trips /household, work trips /household, education trips/household, shopping and social/recreational trips/household and, person trips/person. These models are developed by using stepwise regression technique after the collected data being fed to SPSS software.
Results show that total persons trips/household are related to family size and structure variables
such as number of person more than 6 year age, number of male, total number of workers, total
number of students in the household, number of private vehicles. This model has coefficient of
determination equal to 0.669 for the whole study area. Also the results show that the home-based
work trips are related to number of worker in the household, number of male workers in the
household, number of female workers in the household and number of persons of (25-60) year age;
this model has coefficient of determination equal to 0.82 for the whole study area. Home-based
education trips are strongly related to number of students in the household and this model has
coefficient of determination equal to 0.90 for the whole study area
This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback
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... Show MoreEstimations of average crash density as a function of traffic elements and characteristics can be used for making good decisions relating to planning, designing, operating, and maintaining roadway networks. This study describes the relationships between total, collision, turnover, and runover accident densities with factors such as hourly traffic flow and average spot speed on multilane rural highways in Iraq. The study is based on data collected from two sources: police stations and traffic surveys. Three highways are selected in Wassit governorate as a case study to cover the studied locations of the accidents. Three highways are selected in Wassit governorate as a case study to cover the studied locations of the accidents. The selection
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