Ulaştırma Türü Seçiminde Esnek Hesaplama Yöntemleri

The aim of this study is to use soft computing methods for modelling the mode-choice in urban passenger transportation. For this purpose four softcomputing models were developed and compared with the conventional binary logit model by using the data collected in the Transportation Master Plan of Eskisehir (EUAP) in 2002. Travel survey data were classified according to the income level of the households: high, medium and low income. For each income group two types of mode-choice models were developed. 1. Conventional binary logit model. 2. Soft computing models such as artificial neural networks (YSA), pure fuzzy logic (BM) and neuro-fuzzy logic (SB). Travel time and travel cost were used as the two main parameters in all models. Mode-choice was made between car and public transport. The analysis covered the following issues; 1) Identifying the best common performance measures to compare the binary logit and soft computing mode-choice models. 2) Calculation of performance measures. It has been shown that soft computing models; especially Sugeno type neuro-fuzzy models give better estimates for predicting the mode-choice of the sample data. It has been also shown that R2 could be a sufficient measure to compare the performance of the conventional and soft computing mode-choice models. Finally, it has been shown that threshold values that are not equal to 0.50 could improve the performance of some mode-choice models. Production and demand processes are fundamental elements of economics. After industrial revolution, human beings started to supply their needs instead of self-agricultural production. In this dynamics, minor and major choices shaped demand and production phases. At the side of the goods or service providers (car company or a municipality which gives transportation service), prediction of choices or factors affecting these choices are very important. The ability to accurately predict the future is fundamental to many decision processes in planning such as scheduling, purchasing, strategy formulation, policy making, and supply chain operations. Most firms seek to maximize their market share. For that, they need consumers that are on target, their behavior structure. All operational models for predicting individuals (travellers) choices are based on behavioral principle called "utility maximization." According to utility maximization principle, there is a mathematical function U that is called utility function, whose numerical value depends on attributes of the available options and the individual (causal relationship). The utility function has the property that its value for one option exceeds its value for another, if and only if the consumer prefers the first option to the second. The consumer chooses the most preferred option, which is the one with the highest utility-function value (rational man). The correct utility function may differ from that used by the analyst due to the omission of a variable that influences mode choice, measurement error, variations in preferences among individuals, or all of these. In each case the correct utility function U, can be written as the sum of the utility function specified by the analyst, V and an error term,ε . Soft computing techniques such as neural networks (ANNs), fuzzy logic (FL) and genetic algorithms (GA) are very powerful modelling tools for transportation. Zadeh (1965) introduced fuzzy set theory as a general approach to express the different types of uncertainty inherent in human systems. Artificial neural networks (ANNs) are computing models for information processing and pattern identification. They grow out of research interest in modeling biological neural systems, especially human brains. Data for modelling was collected from Eskisehir City in Turkey. Data sample was splited into three parts according to income level such as high, medium and low. For medium income group, two types of modelling were structured. The first is a traditional mode choice model consisting of binary logit models. The second are soft computing models consisting of artificial neural networks, pure fuzzy logic and neuro-fuzzy models. Two main parameters were used in all models are travel time and travel cost. Choice set was formed of two choices, mass transit and car. End of the analysis; neuro-fuzzy models have the best prediction scores. In small sample size, logit models do not give acceptable prediction scores. Anyway, all logit models fail in all statistics tests.