Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Has files: YesPublisher: search.filters.publisher.Faculty of Transport and Traffic Sciences, University of Zagreb

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  • Article
    An Analysis of Vehicular Traffic Flow Using Langevin Equation
    (Faculty of Transport and Traffic Sciences, University of Zagreb, 2015) Koşun, Çağlar; Çelik, Hüseyin Murat; Özdemir, Serhan; Çelik, Hüseyin Murat; 02.03. Department of City and Regional Planning; 03.10. Department of Mechanical Engineering; 03. Faculty of Engineering; 02. Faculty of Architecture; 01. Izmir Institute of Technology
    Traffic flow data are stochastic in nature, and an abundance of literature exists thereof. One way to express stochastic data is the Langevin equation. Langevin equation consists of two parts. The first part is known as the deterministic drift term, the other as the stochastic diffusion term. Langevin equation does not only help derive the deterministic and random terms of the selected portion of the city of Istanbul traffic empirically, but also sheds light on the underlying dynamics of the flow. Drift diagrams have shown that slow lane tends to get congested faster when vehicle speeds attain a value of 25 km/h, and it is 20 km/h for the fast lane. Three or four distinct regimes may be discriminated again from the drift diagrams; congested, intermediate, and free-flow regimes. At places, even the intermediate regime may be divided in two, often with readiness to congestion. This has revealed the fact that for the selected portion of the highway, there are two main states of flow, namely, congestion and free-flow, with an intermediate state where the noise-driven traffic flow forces the flow into either of the distinct regimes. © 2015, Faculty of Transport and Traffic Engineering. All rights reserved.
  • Article
    Citation - WoS: 11
    Citation - Scopus: 12
    Passenger Flows Estimation of Light Rail Transit (lrt) System in Izmir, Turkey Using Multiple Regression and Ann Methods
    (Faculty of Transport and Traffic Sciences, University of Zagreb, 2012) Özuysal, Mustafa; Tayfur, Gökmen; Tayfur, Gökmen; Özuysal, Mustafa; 03.03. Department of Civil Engineering; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    Passenger flow estimation of transit systems is essential for new decisions about additional facilities and feeder lines. For increasing the efficiency of an existing transit line, stations which are insufficient for trip production and attraction should be examined first. Such investigation supports decisions for feeder line projects which may seem necessary or futile according to the findings. In this study, passenger flow of a light rail transit (LRT) system in Izmir, Turkey is estimated by using multiple regression and feed-forward back-propagation type of artificial neural networks (ANN). The number of alighting passengers at each station is estimated as a function of boarding passengers from other stations. It is found that ANN approach produced significantly better estimations specifically for the low passenger attractive stations. In addition, ANN is found to be more capable for the determination of trip-attractive parts of LRT lines.