No abstract

The COVID-19 outbreak very quickly disrupted the order of human beings. While many sectors have been trying to cope with the ongoing COVID-19 process, they have also been trying to plan the new process for after the pandemic. Transport is one of the sectors most affected by the pandemic and it is necessary to produce the right political formulations for the post-pandemic period. For this reason, it is necessary to carefully examine the changing user demands in various segments of society due to COVID-19 and reveal effective post-pandemic transport policies. This study contributes to this requirement. Accordingly, this study investigated the transport mode preferences of university students in post-pandemic period in Istanbul, one of the important metropolises of the world, via the use of a survey. The reason for university students were focused on was that the mobility of university students is very high and in addition, they are more flexible than other age groups in using different transport modes. The main findings obtained from the study show that there will be a significant change in demand in transport modes after the pandemic. In particular, while a critical decrease may be observed in the travel demand for public buses, shared minibuses and LRT in public transport in post-pandemic period, a high increase in demand for private car use is highly probable. In addition, the research results indicate that COVID-19 can cause an increase in use of e-scooter/hoverboard and active travel modes. The results obtained through the statistical analysis and the discussions based on these results can make a significant contribution to the post-pandemic transport policies of cities with high university student populations and various transport modes, such as Istanbul.

No abstract

Accurate determination of average vehicle delays is significant for effective management of a signalized intersection. The vehicle delays can be determined by field studies, however, this approach is costly and time consuming. Analytical methods which are commonly utilized to estimate delay cannot generate accurate estimates, especially in oversaturated traffic flow conditions. Delay estimation models based on artificial intelligence have been presented in the literature in recent years to estimate the delay more accurately. However, the number of artificial/heuristic intelligence techniques utilized for vehicle delay estimation is limited in the literature. In this study, estimation models are developed using four different machine learning methods—support vector regression (SVR), random forest (RF), k nearest neighbor (kNN), and extreme gradient boosting (XGBoost)—that have not previously been applied in the literature for vehicle delay estimation at signalized intersections. The models were tested with data collected from 12 signalized intersections located in Ankara, the capital of Turkey, and the performance of the models was revealed. The models were furthermore compared with successful delay models from the literature. The developed models, in particular the RF and XGBoost models, showed high performance in estimating the delay at signalized intersections under different traffic conditions. The results indicate that the delay estimation models based on the RF and XGBoost techniques can significantly contribute to both the literature and practice.

Artan nüfus ve gelişen teknolojiye paralel olarak ortaya çıkan karayolu ihtiyacını karşılamak amacıyla inşa edilen esnek karayolu üstyapıların artması, bakım ve rehabilitasyon işlerinin de göz önünde tutulmasını zorunlu hale getirmiştir. Bu bağlamda, üstyapı bakım ve onarım maliyet analizleri ile ilgili bilimsel araştırmalar yapılmaktadır. Bugüne kadar karayolu üstyapısı bozulmaları ile ilgili yapılan maliyet araştırmalarında tahmin modellerine dayalı analizler gerçekleştirilmiştir. Bu çalışmada ise, yol üstyapısında oluşan bozulma örnekleri üzerinde incelemeler yapılarak maliyet analizleri yapılmıştır. Projelendirme safhasında dikkate alınan ön veriler; trafik, iklim ve bölge, malzeme ve üstyapı taban faktörlerine bağlı olarak üstyapıda etkili oldukları bozulmalar, bakım maliyetleri açısından incelenmiştir. Yapılan bu çalışmada, esnek karayolu üstyapılarının projelendirme aşamasında dikkate alınan parametrelerden, üstyapı hasarlarında en etkili kriterin malzeme faktörü olduğu görülmüştür. Bununla birlikte, esnek üstyapılarda iklim ve bölge etkisiyle meydana gelen kabarma oluşumunun, karayolları bakım maliyetleri üzerinde en etkili hasar olduğu belirlenmiştir. Ayrıca, agrega cilalanması, sökülme ve büzülme çatlaklarının bakım maliyetleri üzerindeki etkisi en düşük fiziksel deformasyonlar oldukları sonucuna ulaşılmıştır.