Thesis Type: Postgraduate
Institution Of The Thesis: Istanbul Technical University, Bilişim Enstitüsü, Bilişim Uygulamaları, Turkey
Approval Date: 2020
Thesis Language: Turkish
Student: BİLGE YILMAZ
Consultant: Uğur AlgancıAbstract:
As it well known, world’s population keep rises day by day and the citys’ land- use change becomes inevitable becuese of this population growth. This situation makes difficult to carry out planning studies systematically in cities which aims to establish healthy sustainable relations. It is nearly impossible to have estimations on travel demand and attraction that consist of socio-economic factors which have variable values and are affected by the social, environmental and economic activities in the city. Today’s studies stand on the methods that allows to make the most accurate estimation and these travel demand forecasting methods generally require data collection and data counting on the city scale or on the specific point in the city. The variability of the data set causes repeating the estimation steps repeatedly and this demands time, workforce and high budgets. In addition to the variable data set, there is no comprehensive study on the extent to which socio-economic factors associated with travel demands affect travel production and attraction, so it is not possible to estimate the extent to which changes in travel demands arising from an increase or decrease in these values. Because of these reasons, it needs to be developed new travel demand forecasting methods which will demand data and labor requirements less and can be easily updated. Additionally, it needs to be determined the significance levels of the travel demands for each point of the city, the parameters which effects these demands, and to create analysis maps of these travel demands. With the transportation planning activities, it is aimed to develop sustainable cities by evaluating the studies on the prediction of travel demand. Thanks to these methods, neighborhoods and avenues which have the most dense pedestrian and traffic volume, and many infrastructure facilities which are needed will be able to detected easiliy, therefore the cities will develop in acontrolled way.
The analytic hierarchy process, which is one the multi-criteria decision making methods, which is an increasingly popular application method, is used in studies which contain more than one parameter, and the significance levels of these parameters is determined by the intuitive or logical views of experts or non-experts in this field and the synthesis of these collected data. The inverse distance weighted method which is a deterministic procedure, and the ordinary kriging interpolation methods, which are adapted to the linear and gaussian theoretical variogram model, and also these are geostatistical procedure, make weight- dependent weighting based on the principle that the objects close to the each other will be more similar than to the objects are distant. It makes predictions abput unknown points by using the observation points whose values is known,and generates prediction maps for the entire surface of an area interpolated throgh geographic information systems. With these methods, which perform estimates with a lager number of sampling points, susccessful results were obtained in applications for different disciplines such as river water quality, regional landslide hazard, spatial precipitation distribution, estimates of soil variables and such optimization problems or reserve estimation. Regression analysis isan applications in which the relationships between dependent and independent variables are explained by a mathematical fuction with numeratical values, and the cross correlation technique is the application where the error rates of the criteria whose value estimates are made are determined. Two of them is frequently used in the verification steps.
Within the scope of this thesis, which aims to contribute to transpostation planning studies and to be open to dispute and advance, Istanbul has been chosen as a case study area because it is a metropolis where an changes occurs constantly.In the study, data sets consist of socio-economic factors related to Istanbul were classifies according to standard deviation values and analysis maps were produced by using geographical information systems. Thanks to broad literature review about on the travel demand and attraction estimation methods which has been studied, it is determined that the most used parameters that effects travel demand values are numbers of population, average household income, vehicles, schools, employees and students. To determine the significance level of these parameters, two hundred particitpants from different participator groups were brought together and their opinions gathered in logical manner through the analytic hierarchy process.It was determined that the significance level of these parameters on the age range is 25-59, 0-24, 60-75, 76-90+ respectively, and then other characteristics comes respectively, number of employees, and students, job opportunity, income status, population, number of vehicles and number of schools. Classifications that are made by the standard deviation values in the analysis maps and significance level of the parameters was used on the analysis of the trip demand model value table. Analysis on both the model and the travel demand values on 2012 were compared with the spatial interpolation methods.Similar images were obtained from the analyzes made with two different data sets, the disadvantages and the advantages of the methods in the analysis maps were also explained.In the verification step, 20 districts were selected among 39 districts of Istanbul,by using the cross correlation method. The values of 20 points are removed from the analysis maps.With the travel demand datas of 38 districts, spatial interpolation methods were analyzed for 20 observation points. The error ratio of the estimated values were calculated according to the actual values. In that way, it has been detected which method makes estimates with the least and most error rate for any district.Finally, regression analyzes were carried out to be able to explain the correlation between the predictive values and actual values of 20 observation points. It is observed that for which districts of Istanbul can be produced reliable values.
The data set used in this study were prepared according to datas of 2012, which are transportation network and property- line maps of the districts of Istanbul, trip demand and attraction values, average household income, number of vehicles, employees, employment volume, number of students, schools and 0-24, 25-59, 60-75, 76-90+ age groups of population.
As a result of this study, thanks to the opinions received from 200 people, it was determined that the weight of the travel demand factors determined by AHP approach directed the model correctly. It has been identified that the travel demand model which is obteained reflects the current station in 2012. And also, it has been identified that the value estimates made by interpolation methods have a very low error rate for some districts of Istanbul.So it is likely to say that the method can be used, and developed by considering future suggestions to move this study forward.