Transportation forecasting is the process of estimating the number of vehicles or people that will use a specific transportation facility in the future. Traffic forecasting begins with the collection of data on current traffic. This traffic data is combined with other known data, such as population, employment, trip rates, travel costs, etc., to develop a traffic demand model for the current situation. Feeding it with predicted data for population, employment, etc. results in estimates of future traffic, typically estimated for each segment of the transportation infrastructure in question, e.g., for each roadway segment or railway station.

Traffic forecasts are used for several key purposes in transportation policy, planning, and engineering: to calculate the capacity of infrastructure, e.g., how many lanes a bridge should have; to estimate the financial and social viability of projects, e.g., using cost-benefit analysis and social impact assessment; and to calculate environmental impacts, e.g., air pollution and noise.

In this study, an econometric analysis has been made for counting of people and counting of vehicles passing through the Istanbul Bosporus bridges. First, a latent variable is time to replace the established forecasting models using the classical regression method, and then taking into account the effects of time and the unit is applied to panel data econometrics to estimate the number of people were passing over the bridge.

Data crossing the bridge in Istanbul Istanbul Metropolitan Municipality Transportation Master Plan Performance Monitoring project has been provided. Consists of the number of people and vehicles based on data and tools to hours. With these data the number of people before the regression models as independent variables as the dependent variable was the number of vehicles.

Latent variables (as opposed to observable variables), are variables that are not directly observed but are rather inferred (through a mathematical model) from other variables that are observed (directly measured). Mathematical models that aim to explain observed variables in terms of latent variables are called latent variable models.

One advantage of using latent variables is that it reduces the dimensionality of data. A large number of observable variables can be aggregated in a model to represent an underlying concept, making it easier to understand the data.

Latent variables, as created by factor analytic methods, generally represent 'shared' variance, or the degree to which variables 'move' together. Variables that have no correlation cannot result in a latent construct based on the common factor model.

24 hours of the occurrence of so many people to this model, then at least one occurrence of the value 1 in a latent variables were added to the clock.

These variables were tested various model alternatives.

As a result: Rate of 2.77 persons per vehicle accessible classical regression models. For increasing values of the latent variable, the number of people per vehicle increased by 3:23 person, the number of people crossing the bridge it means that if weight increases 0.9 people are turning to public transportation.

Later on when the panel data econometrics tools and models were investigated.

In statistics and econometrics, the term panel data refers to multi-dimensional data frequently involving measurements over time. Panel data contain observations on multiple phenomena observed over multiple time periods for the same firms or individuals. In biostatistics, the term longitudinal data is often used instead, wherein a subject or cluster constitutes a panel member or individual in a longitudinal study.

Time series and cross-sectional data are special cases of panel data that are in one dimension only.

Panel data models for the time effect is used time, the dimensions effect (base effect) is used for the type of the vehicle.

Hausman test of the random effects model using panel data method selected. Following the analysis made in the light of this model car 1:27 per person, according to a latent effect on people was 86.3. Morning and evening peak hours of the day 7 (07:00 and 19:00) the maximum number of people pass over the bridge. 8:00 am, 18:00, 20:00 range of densities from behind.