Solution of urban mass transport network design problem with improved intelligent water drop algorithm

Bu çalışma, şehir içi toplu taşıma sistemlerinin yolculuk talebini konforlu, güvenli, dakik vb. şekilde karşılayabilmesi için güzergâh tasarımını ele almaktadır. Bu amaçla, toplu taşıma güzergahlarının belirlenmesi ve tasarımının yapılabilmesi için Akıllı Su Damlası (ASD) çözüm algoritması ve ASD algoritması ile toplu taşıma kullanıcılarını, toplu taşıma işletmesini ve çevre faktörlerini dikkate alan güzergâh tasarımının elde edilmesini hedefleyen Toplu Taşıma Rota Optimizasyonu (ToTaRO) modeli geliştirilmiştir. Ayrıca, toplu taşıma planlaması sürecinde teknik metotların geliştirilmesi ve toplu taşıma ağ tasarımının karar verme aşamalarından faydalanılmış, mevcut toplu taşıma sisteminin iyileştirilmesinde, aktarma ölçütünden verimlilik göstergesi olarak yararlanılmıştır. ToTaRO modelinin validasyonu, literatürde oldukça yaygın olarak kullanılan Mandl İsviçre toplu taşıma ağı ile gerçekleştirilmiş ve Isparta toplu taşıma ağı üzerine uygulanarak cesaret verici sonuçlar elde edilmiştir. Sonuç olarak uygulama alanı olarak seçilen Isparta toplu taşıma ağında; yolcu, işletme ve çevre maliyetlerinin mevcut otobüs rotasına göre sırası ile yaklaşık %9 ve %6 oranında azaltılabileceği hesaplanmıştır. Ayrıca, ToTaRO modeli mevcut ağa göre yolculuk talebini, aktarmasız seyahatler için yaklaşık %17, aktarmalı seyahatler için %16 oranında iyileştirme ile karşılamıştır.This study deals with the route design that can meet the travel demand of urban public transport systems that makes comfortable, safe, punctual, etc. For this purpose, Intelligent Water Drop (IWD) solution algorithm has been improved to determine and design public transportation routes. With the IWD algorithm, the Public Transportation Route Optimization (ToTaRO) model has been developed, which aims to achieve a route design that takes public transport users, operations and environmental factors into account. In addition, the development of technical methods and the decision-making stages of public transport network design were utilized in the public transport planning process. Transfer criterion was used as an indicator of efficiency to the improvement of the existing public transportation system.The validation of the ToTaRO model was carried out with the Mandl Swiss public transport network, which is widely used in the literature, and encouraging results were obtained by applying it on the Isparta public transport network. As a result, it has been calculated that passenger, operating and environmental costs can be reduced by approximately 9% and 6% respectively,compared to the current bus route in Isparta public transport network selected as an application area. In addition, ToTaRO model meets the travel demand with an improvement of about 17% for the percentage of demand satisfied without any transfers and about 16% for the percentage of demand satisfied with one transfer compared to the current network

Marshall Stability Estimating Using Artificial Neural Network with Polyparaphenylene Terephtalamide Fibre Rate

International Symposium on Innovations in Intelligent Systems and Applications (INISTA) -- AUG 02-05, 2016 -- Sinaia, ROMANIASaltan, Mehmet/0000-0001-6221-4918; terzi, serdal/0000-0002-4776-824X; Eriskin, Ekinhan/0000-0002-0087-0933; Eriskin, Ekinhan/0000-0002-0087-0933; Karahancer, Sebnem/0000-0001-7734-2365WOS: 000386824000029Due to the complex behaviour of asphalt pavement materials under various loading conditions, pavement structure, and environmental conditions, accurately predicting stability of asphalt pavement is difficult. To predict, it is required to find the mathematical relation between the input and output data by an accurate and simple method. In recent years, artificial neural networks (ANNs) have been used to model the properties and behaviour of materials, and to find complex relations between different properties in many fields of civil engineering applications, because of their ability to learn and to adapt. In the present study, laboratory data are obtained from an experimental study that was used to develop an ANN model. For predicting the Marshall Stability value of mixture using ANN models, an appropriate selection of input parameters (neurons) is essential. There are four nodes in the input layer corresponding to four variables: Polyparaphenylene Terephtalamide fibre (PTF) rate, binder rate, flow, volume of the specimen. The result indicates that the proposed model can be applied in predicting Marshall Stability of asphalt mixtures. The model is further applied to evaluate the effect of different rates of Polyparaphenylene Terephtalamide on Marshall Stability.IEEE, IDS Res Grp, Fac Automat Comp & Elect, Dept Comp & Informat Technol, Fac Econ & Business Adm, Dept Stat & Business Informat, Fac Math & Nat Sci, Dept Informat, Univ Craiov