This research investigated the suitability of using sludge from the treatment of drinking water in hot mix asphalt (HMA) as a filler material. The storage and environmental impact of sludge is an enormous problem, especially for countries with large populations. Two different types of sludges, ferric chloride (FC) and aluminium sulphate (AS), were used as a filler material in HMA. The Hamburg Wheel Tracking (HWT) test, which correlates with rutting, and the Indirect Tensile Strength (ITS) test, which indicates the moisture sensitivity of HMA, were carried out at the optimum bitumen content of the mixes to investigate the usability of sludge in HMA. The test results indicate the usability of FC and AS in HMA compared to the reference mixes. However, the AS type of sludge has better rutting resistance than the FC type. Although the results support the usability of both sludges in HMA, it should be noted that the increased cost of the mix containing sludges due to the combustion process and the increased bitumen content during application should be considered.

Travel time of buses, a major part of the urban public transit system, is affected by various factors and foremost are the bus stops. In addition to the dwell time at the stop, the deceleration and acceleration zones reduce average speed, particularly in mixed traffic, and increase travel time. Several approaches are used in estimating the link-based travel time of public transit systems in transportation planning, but Bus Stop Influence Zones (BSIZs) are ignored. Moreover, fuel consumption and pollutant emissions increase in BSIZs, and this demonstrates the importance of BSIZs in measuring the performance of public transit systems as well as planning. Data obtained from the Automatic Vehicle Location system was used and visualized on Geographic Information System and data preprocessing steps were performed. Finally, changepoint detection method of Facebook Prophet (FBP-CDM) was exploited to identify changepoints in location-speed data on the selected route. Results were validated with real-life data and expert opinion, and then compared with the findings acquired by the K-means clustering. Based on the conclusions, FBP-CDM was found quite effective in detecting and predicting BSIZs accurately and the proposed methodology is useful for studies in transportation planning and operations.

Electric vehicles (EVs) are vital in achieving a sustainable and eco-friendly transportation system. However, the transition to EVs faces significant challenges, particularly range anxiety, which is the fear of running out of battery power without access to charging facilities. To address this issue and promote EV adoption, identifying locations for Electric Vehicle Charging Stations (EVCS) is crucial, especially for intercity travel. The study proposes a three-stage solution methodology utilizing the Variable Transportation Demand Model (VTDM) established for nationwide transportation planning. The proposed approach includes identifying EVCS locations, with path-based traffic demands derived from the VTDM’s generalized cost-based shortest path methodology for different trip purposes. The proposed approach was validated using real-world big data from the Türkiye National Transportation Master Plan (TNTMP). The results revealed that 2,161 charging stations are required to ensure uninterrupted travel throughout the network. Furthermore, the study identifies a discrepancy in the availability of charging station infrastructure across different regions, with the majority of new facilities concentrated in the eastern region of Turkey. Addressing this infrastructure gap is crucial for accelerating the adoption of electric vehicles (EVs), reducing carbon emissions, and promoting sustainable transportation systems at the national level.

Ulaştırmanın üç temel bileşeni; insan, taşıt ve altyapı şeklindedir. Ulaştırma sisteminin işletimi sırasında, bu bileşenlerin hem kendileri hem de birbirleri ile olan ilişkilerinde ortaya çıkan sorunlar “maliyet” olarak tanımlanmaktadır. Ulaştırma sisteminin planlanması aşamasında, işletim sırasında karşılaşılacak maliyetlerin tanımlanması ve analiz edilmesi gerekmektedir. Bu bağlamda, ülkemizde yoğun olarak kullanılan karayolu ulaşımı için de işletme maliyetlerinin ayrı ayrı belirlenmesi ve bunların etkilerinin değerlendirilmesi büyük önem arz etmektedir. Karayolu ulaşımında işletme maliyetleri; taşıt işletme maliyeti, emisyon maliyeti, zaman kaybı maliyeti ve yol güvenliği maliyeti olmak üzere 4 ana başlıkta toplanmaktadır. Bunlar arasında taşıt işletme maliyeti, en büyük maliyet oranına sahiptir. Yakıt tüketimi, bakım-onarım gibi doğrudan kullanıcıya yansıyan bu maliyetleri azaltmada yolun karakteristik özellikleri büyük önem arz etmektedir. Bu çalışmada, karayolu işletme maliyetlerinin neler olduğu ve maliyet hesaplamalarına ilişkin yöntemler detaylı olarak incelenmiştir. Dünya Bankası tarafından yol proje maliyetlerinin hesaplanması için geliştirilen HDM-4 yazılımı kullanılarak, yazılım parametrelerindeki değişimin işletme maliyetine etkisi analiz edilmiştir. Çalışma kapsamında, parametre değişiminin etkisi projelendirilen bir yol güzergahı için belirlenmiştir. Sonuç olarak; taşıt işletme, çevresel etkiler ve yol geometrik özelliklerine ilişkin kullanılan parametrelerin yol işletme maliyetine olan etkisi ortaya konulmuştur.

This study introduces the Sustainable Urban Mobility Maturity Model (SUM-MM) to assess and enhance the maturity of sustainable urban mobility in cities. The SUM-MM comprises 3 main dimensions (enablers, sustainability, and transport modes) and 11 sub-dimensions (strategic and spatial planning, organization and human resources, information and communication technologies, environment, economy, social, walking, micromobility, public transport, paratransit systems, and multimodal integration), evaluated at 5 levels (beginner, initial, integrated, managed, and mature). Developed through a literature review and validated using a questionnaire-based expert opinion method, the model was tested in Konya, Türkiye. The results show that Konya’s overall maturity falls between integrated and managed, with significant variability across sub-dimensions. The enablers dimension demonstrated the highest maturity, driven by strong organizational and technological capabilities, whereas the transport modes dimension had the lowest—particularly in paratransit systems. The SUM-MM serves as both a benchmarking tool and a policy guidance framework, facilitating targeted strategies for sustainable urban mobility improvements. Unlike existing smart city or transport maturity models, the SUM-MM specifically focuses on sustainable urban mobility, offering a structured, operational, and decision-oriented framework for policy-makers and city administrations. The results can be used by local and national authorities to support comparative benchmarking, strategic planning, and the prioritization of sustainable urban mobility investments.