Mikromobilite sistemleri son yıllarda dünyanın pek çok bölgesinde olduğu gibi ülkemizde de oldukça popüler olmaya başlamıştır. Mikromobilite sistemlerinin popülerliğinin artmasının ana nedenleri, artan araç paylaşım sistemleri, özellikle son kilometre ya da son mil olarak tanımlanan kısa mesafelerde ekonomik bir alternatif olması, trafik sıkışıklıklarından en alt düzeyde etkilenmesi ve bu sistemlerin çevre dostu olduğu algıları bulunmaktadır. Ancak bu düşünce ya da algıların ne kadar gerçeği yansıttığı üzerinde dikkatlice durulması gereken bir konudur. Mikromobilite sistemleri, özellikle zevkli bir kullanıma sahip olduğundan büyük oranda gençler tarafından oldukça popüler olmaya başlamış ulaşım türleridir. Ancak, güvenliği düşük, trafik sıkışıklıklarına çözüm sunamayan ve kamuoyunda oluşan genel algının aksine çevreye olumsuz etkileri olan bir alternatiftir. Yine de motorlu taşıtların yerine son kilometre ulaşımında tamamlayıcı bir ulaşım türü olarak kullanıldığında önemli ve olumlu bir ulaşım alternatifi sunmaktadır. Buna karşın, mikromobilite sistemlerinin etkinliğini belirlemek için sosyal fayda/maliyet analizleri gibi kapsamlı çalışmalar gerçekleştirilmeli, gerekirse bu sistemlerin kullanımına ilişkin önemli yasal düzenlemeler gerçekleştirilmelidir.

Pedestrians, as the most vulnerable road users, are more severely affected when involved in an accident. For this reason, it is important to reduce the risk of pedestrian accidents to increase the safety of highway traffic. The majority of accidents involving pedestrians occur during street crossings where motor vehicles are present. Hence, pedestrians are encouraged to cross safely at crosswalks that are either controlled by a signal or not. Accidents involving pedestrians can be reduced by analysing pedestrian crossing behaviours and designing crosswalks accordingly. Crosswalk design as well as traffic signal phasing need to give enough time for pedestrians to complete their crossing during the green signal phase. For this reason, pedestrian crossing speed and factors affecting this have been analysed in this study. The outcomes can be used to help decision makers and engineers to design ideal pedestrian facilities.

In this study, Marshall test results of hot mix asphalt samples having various Hydrated Lime (HL) content rates were modelled using Fuzzy Logic (FL) and Artificial Neural Networks (ANN). Test sets having various HL content were prepared in order to investigate the effect of HL. Marshall Stability test was performed on the samples to obtain the optimal Asphalt Content (AC) ratio. The results were evaluated in order to determine HL additives’ sensitivity on the mixture parameters. The Marshall Test results were used to develop the FL and ANN models. The models developed produced acceptable estimations of the mixture parameters.

Service quality is one of the main issues that today's world. Firms operating in the transportation sector are also trying to improve the quality of the service they provide to their passengers. It is crucial to determine the passengers' service quality perceptions and priorities to evaluate and improve the service in this context. In this study, Kocaeli's tram service users' service quality perceptions have been evaluated by applying a survey consisting of 20 questions and user satisfaction levels from different service dimensions. Later, an artificial neural network model was developed using the users' demographic data and their responses to the survey questions to mimic their service quality satisfaction. The artificial neural network model developed has been examined to understand the importance that tram users give to service quality. Using the developed the “change of score” method, how the changes to be made in the tram system will affect the quality of service and how the opinions of different user groups will be affected can be examined in detail. The artificial neural network model's prediction capability was compared with the multiple linear regression model and found superior. According to the developed Change of Score Method, the most frequent user attaches the highest importance to the service dimensions of the convenience to pay for the tram, getting his/her destination on time, and reducing environmental pollution.

Delays caused by motor vehicle traffic, accidents, and environmental pollution present considerable challenges to sustainable urban mobility. To address these issues, transportation system users are encouraged to adopt active transportation methods, micromobility options, and public transit. Electric scooters have become a notably popular micromobility choice, especially following the emergence of vehicle-sharing companies in 2018, a trend that gained further momentum during the COVID-19 pandemic. This study explored the demographic characteristics, attitudes, and behaviors of e-scooter users in Istanbul through an online survey conducted from 1 September 2023 to 1 May 2024. A total of 462 e-scooter users participated, providing valuable insights into their preferred modes of transportation across 24 different scenarios specifically designed for this research. The responses were analyzed using various machine learning techniques, including Artificial Neural Networks, Decision Trees, Random Forest, and Gradient Boosting methods. Among the models developed, the Decision Tree model exhibited the highest overall performance, demonstrating strong accuracy and predictive capabilities across all classifications. Notably, all models significantly surpassed the accuracy of discrete choice models reported in existing literature, underscoring the effectiveness of machine learning approaches in modeling transportation mode choices. The models created in this study can serve various purposes for researchers, central and local authorities, as well as e-scooter service providers, supporting their strategic and operational decision-making processes. Future research could explore different machine learning methodologies to create a model that more accurately reflects individual preferences across diverse urban environments. These models can assist in developing sustainable mobility policies and reducing the environmental footprint of urban transportation systems.

Bu çalışma kapsamında İzmir’de yer alan 4 ışıksız kavşaktan toplanan verilerden yararlanarak, farklı manevralar için bağıntılar geliştirilmeye çalışılmıştır. Çalışmada, kritik aralık kabul değerinin, düşük ana yol hacim değerleri altında önemli oranda değişkenlik gösterdiği; ancak ana yol trafik akımı yüksek değerlere ulaştığında sürücülerin daha kısa aralıkları kabul ettikleri görülmüştür. Aralık kabul değerinin belirlenmesinde, kavşağa giriş yapacak sürücünün manevrasından daha çok; uygun aralık bulmaya çalıştığı manevranın daha etkin olduğu görülmüştür. Çalışmanın bir diğer çıktısı da, özellikle düşük ana yol trafik akımı etkisi altında sürücü davranışlarının daha etkili olduğu ve davranış farklılıklarının modele katılmasıyla sahada gözlemlenen değerlere daha sağlıklı biçimde ulaşılabileceği anlaşılmıştır.