Kısa Dönem Uzam-Zamansal Trafik Tahmini

Translated title of the contribution: Short-term spatio-temporal traffic forecasting

Akın Taşçıkaraoğlu*, Fatma Yildiz Tascikaraoglu, ibrahim Beklan Kucukdemiral

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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The studies carried out with the objective of minimizing the effects of congestion, delay and environment problems on the transportation network have gained increasing importance in the last years. Among these studies, short-term traffic flow and average vehicle speed forecasting methods have come into prominence due to their easy implementations, efficient usage on different areas and cost-effectiveness. A large number of studies have reported that these methods, in which the expected future values of link flows and average speeds are forecasted in desired points, can reduce the traffic congestion by anticipating the problems in traffic management. In this paper, a spatio-temporal approach accounted for historical traffic characteristics data collected from a large number of points is presented for average speed forecasts in a given link. The proposed approach includes an algorithm that enables to take into account the most informative data in an input set by determining them for each stage. It is aimed to increase the forecasting accuracy by using sparse matrices in the algorithm while decreasing the calculation times significantly compared to the similar methods presented in the literature.
Translated title of the contributionShort-term spatio-temporal traffic forecasting
Original languageOther
Title of host publicationTOK 2016 Otomatik Kontrol Ulusal Toplantısı
EditorsOsman Parlaktuna, Cihan Uyanik
Number of pages6
ISBN (Electronic)9786059975131
Publication statusPublished - 1 Oct 2016
EventTOK 2016 National Conference on Automatic Control - Osmangazi University, Eskisehir, Turkey
Duration: 29 Sep 20161 Oct 2016


ConferenceTOK 2016 National Conference on Automatic Control
Internet address


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