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Modelling trip distribution with fuzzy and genetic fuzzy systems
Get PDFThis paper explores the potential capabilities of fuzzy and genetic fuzzy system approaches in urban trip distribution modelling with some new features. First, a simple fuzzy rule-based system (FRBS) and a novel genetic fuzzy rule-based system [GFRBS: a fuzzy system improved by a knowledge base learning process with genetic algorithms (GAs)] are designed to model intra-city passenger flows for Istanbul. Subsequently, their accuracy, applicability and generalizability characteristics are evaluated against the well-known gravity- and neural network (NN)-based trip distribution models. The overall results show that: traditional doubly constrained gravity models are still simple and efficient; NNs may not show expected performance when they are forced to satisfy trip constraints; simply-designed FRBSs, learning from observations and expertise, are both efficient and interpretable even if the data are large and noisy; and use of GAs in fuzzy rule-based learning considerably increases modelling performance, although it brings additional computation cost
A genetic-fuzzy system modeling of trip distribution
Get PDFThesis (Doctoral)--Izmir Institute of Technology, City and Regional Planning, Izmir, 2010Includes bibliographical references (leaves: 89-96)Text in English; Abstract: Turkish and Englishix, 141 leavesTrip distribution modelling is one of the most active parts of travel demand analysis. In recent years, use of soft computing techniques has introduced effective modelling approaches to the trip distribution problem. Fuzzy Rule-Based System (FRBS) and Genetic Fuzzy Rule-Based System (GFRBS: fuzzy system improved by a knowledge base learning process with genetic algorithms) modelling of trip distribution are two of these new approaches. However, much of the potential of these techniques has not been demonstrated so far. The present study explores the potential capabilities of these approaches in an urban trip distribution problem with some new features. For this purpose, a simple FRBS and a novel GFRBS were designed to model Istanbul intra-city passenger flows. Subsequently, their accuracy, applicability, and generalizability characteristics were evaluated against the well-known gravity and neural networks based trip distribution models. The overall results show that: i) traditional doubly constrained gravity models are still simple and efficient; ii) neural networks may not show expected performance when they are forced to satisfy production-attraction constraints; iii) simply-designed FRBSs, learning from observations and expertise, are both interpretable and efficient in forecasting trip interchanges even if the data is large and noisy; and iv) use of genetic algorithms in fuzzy rule base learning considerably increases modelling performance, although it brings additional computation costs
A Genetic-Fuzzy System Modeling of Trip Distribution
No full textTrip distribution modelling is one of the most active parts of travel demand analysis. In recent years, use of soft computing techniques has introduced effective modelling approaches to the trip distribution problem. Fuzzy Rule-Based System (FRBS) and Genetic Fuzzy Rule-Based System (GFRBS: fuzzy system improved by a knowledge base learning process with genetic algorithms) modelling of trip distribution are two of these new approaches. However, much of the potential of these techniques has not been demonstrated so far. The present study explores the potential capabilities of these approaches in an urban trip distribution problem with some new features. For this purpose, a simple FRBS and a novel GFRBS were designed to model Istanbul intra-city passenger flows. Subsequently, their accuracy, applicability, and generalizability characteristics were evaluated against the well-known gravity and neural networks based trip distribution models. The overall results show that: i) traditional doubly constrained gravity models are still simple and efficient; ii) neural networks may not show expected performance when they are forced to satisfy production-attraction constraints; iii) simply-designed FRBSs, learning from observations and expertise, are both interpretable and efficient in forecasting trip interchanges even if the data is large and noisy; and iv) use of genetic algorithms in fuzzy rule base learning considerably increases modelling performance, although it brings additional computation costs
Modeling Retail Structural Change of Izmir Using a Dynamic Spatial Interaction Model
No full textRetailing, one of the most important sectors in all developed economies, has always been the prominent element of urban morphology, and evolves as the city evolves and expands. The last two decades have witnessed considerable changes in retailing throughout developed countries such as, the emergence of new store formats, the increased prevalence of retail chains, the development of out-of-town and edge-of-town retail parks accompanying with the changing conditions of globalized world. Since the sector has undergone major changes in scale, organization, and geography, the urban spaces have been the scene of these ongoing changes.Under the influence of global economic transformation after 80's, there have also been dramatic changes in retail industry and retail environment in Turkey. Despite the sector in Turkey is still dominated by large number of small, independent, and single location retailers, market share and spatial prevalence of large-scale retailers' have been increasing rapidly. Especially in major cities of the country, both international and domestic retail chains have been imposing a transformation and restructuring the urban retail environment. Among all the areas of retailing, food retailing stands out as having seen the most profound changes in Turkey. With respect of this, the study explores the spatial consequences of the structural change of food retailing system in Izmir. The prevalence of large-scale food retailers such as hypermarkets and supermarkets has negative effects on the survival of many small-independent retailers. The increasing competition has led to a changing retail structure with the dominance of organized retailers where the number of small-scale retailers and their total size are decreasing. As the trends continue, this will have important and unpredictable spatial influences on urban retail environment and urban geography. Obviously, there is a strong need for a study exploring changes in retail structure and its influences in urban spaces so that policy makers and planners could take into consideration and help restructuring of this transformation process better. For this purpose, the study explores if the ongoing restructuring process of retailing and its possible geographical consequences can be modeled using a dynamic spatial interaction model as a device to be able to predict the future transformations
Modeling retail structural change of İzmir using a dynamic spatial interaction model
No full textThesis (Master)--Izmir Institute of Technology, City and Regional Planning, Izmir, 2004Includes bibliographical references (leaves: 104)Text in English; Abstract: Turkish and Englishvii, 106 leavesRetailing, one of the most important sectors in all developed economies, has always been the prominent element of urban morphology, and evolves as the city evolves and expands. The last two decades have witnessed considerable changes in retailing throughout developed countries such as, the emergence of new store formats, the increased prevalence of retail chains, the development of out-of-town and edge-of-town retail parks accompanying with the changing conditions of globalized world. Since the sector has undergone major changes in scale, organization, and geography, the urban spaces have been the scene of these ongoing changes.Under the influence of global economic transformation after 80's, there have also been dramatic changes in retail industry and retail environment in Turkey. Despite the sector in Turkey is still dominated by large number of small, independent, and single location retailers, market share and spatial prevalence of large-scale retailers' have been increasing rapidly. Especially in major cities of the country, both international and domestic retail chains have been imposing a transformation and restructuring the urban retail environment. Among all the areas of retailing, food retailing stands out as having seen the most profound changes in Turkey. With respect of this, the study explores the spatial consequences of the structural change of food retailing system in Izmir. The prevalence of large-scale food retailers such as hypermarkets and supermarkets has negative effects on the survival of many small-independent retailers. The increasing competition has led to a changing retail structure with the dominance of organized retailers where the number of small-scale retailers and their total size are decreasing. As the trends continue, this will have important and unpredictable spatial influences on urban retail environment and urban geography. Obviously, there is a strong need for a study exploring changes in retail structure and its influences in urban spaces so that policy makers and planners could take into consideration and help restructuring of this transformation process better. For this purpose, the study explores if the ongoing restructuring process of retailing and its possible geographical consequences can be modeled using a dynamic spatial interaction model as a device to be able to predict the future transformations
A genetic-fuzzy system modeling of trip distribution
No full textThesis (Doctoral)--Izmir Institute of Technology, City and Regional Planning, Izmir, 2010Includes bibliographical references (leaves: 89-96)Text in English; Abstract: Turkish and Englishix, 141 leavesTrip distribution modelling is one of the most active parts of travel demand analysis. In recent years, use of soft computing techniques has introduced effective modelling approaches to the trip distribution problem. Fuzzy Rule-Based System (FRBS) and Genetic Fuzzy Rule-Based System (GFRBS: fuzzy system improved by a knowledge base learning process with genetic algorithms) modelling of trip distribution are two of these new approaches. However, much of the potential of these techniques has not been demonstrated so far. The present study explores the potential capabilities of these approaches in an urban trip distribution problem with some new features. For this purpose, a simple FRBS and a novel GFRBS were designed to model Istanbul intra-city passenger flows. Subsequently, their accuracy, applicability, and generalizability characteristics were evaluated against the well-known gravity and neural networks based trip distribution models. The overall results show that: i) traditional doubly constrained gravity models are still simple and efficient; ii) neural networks may not show expected performance when they are forced to satisfy production-attraction constraints; iii) simply-designed FRBSs, learning from observations and expertise, are both interpretable and efficient in forecasting trip interchanges even if the data is large and noisy; and iv) use of genetic algorithms in fuzzy rule base learning considerably increases modelling performance, although it brings additional computation costs
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Modelling trip distribution with fuzzy and genetic fuzzy systems
No full textThis paper explores the potential capabilities of fuzzy and genetic fuzzy system approaches in urban trip distribution modelling with some new features. First, a simple fuzzy rule-based system (FRBS) and a novel genetic fuzzy rule-based system [GFRBS: a fuzzy system improved by a knowledge base learning process with genetic algorithms (GAs)] are designed to model intra-city passenger flows for Istanbul. Subsequently, their accuracy, applicability and generalizability characteristics are evaluated against the well-known gravity- and neural network (NN)-based trip distribution models. The overall results show that: traditional doubly constrained gravity models are still simple and efficient; NNs may not show expected performance when they are forced to satisfy trip constraints; simply-designed FRBSs, learning from observations and expertise, are both efficient and interpretable even if the data are large and noisy; and use of GAs in fuzzy rule-based learning considerably increases modelling performance, although it brings additional computation cost.JRC.J.1 - Economics of Climate Change, Energy and Transpor
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