One of the solutions to reduce environmental emissions is related to the deployment of electric vehicles (EVs) with sustainable energy. In order to be able to increase the number of electric vehicles in circulation, it is important to implement optimal planning and design of the infrastructure, with particular reference to areas equipped with charging stations. The suitable analysis of the location of current electric vehicle charging stations (EVCSs) is the central theme of this document. The research focused on the actual location of the charging stations of five major EVCS companies in the province by selecting Istanbul as the study area. The study was conducted through a three-step approach and specifically (i) the application of the analytical hierarchy process (AHP) method for creating the weights of the 6 main and 18 secondary criteria that influence the location of EVCSs; (ii) a geospatial analysis using GIS considering each criterion and developing the suitability map for the locations of EVCSs, and (iii) application of the technique for order preference by similarity to ideal solution (TOPSIS) to evaluate the location performance of current EVCSs. The results show that the ratio between the most suitable and unsuitable areas for the location of EVCSs in Istanbul and the study area is about 5% and 4%, respectively. The results achieved means of improving sustainable urban planning and laying the basis for an assessment of other areas where EVCSs could be placed.

This study used the Taguchi method to determine optimum conditions for Gilsonite and glass fibers as modifier materials in hot mix asphalt (HMA) by conducting the mixed Mode I/III fracture test. These modifier materials were used under different experimental parameters. The parameters and their levels were angle of fracture ($\alpha =30°$ and 45°), nominal maximum aggregate size (9.5, 12.5, and 19 mm), air void content (4, 7, and 10%), Gilsonite content (6, 12, and 18%, as percentage of binder weight), glass fiber content (0.1, 0.2, and 0.3%, as percentage of aggregate weight), glass fiber length (3, 8, and 12 mm), and experimental temperature ($\Theta =0$ , $-15$ , and $-30°\mathrm{C}$ ). The optimum conditions for experimental parameters were fracture angle ($\alpha =45°$ ), air void content (4%), nominal maximum aggregate size (9.5 mm), Gilsonite content (6%), glass fiber weight (0.3%), glass fiber length (12 mm), and experimental temperature ($\Theta =-15°\mathrm{C}$ ).

Intersections are the most important regions in terms of urban traffic management. The intersection areas on the corridor should be analyzed together for consistency in traffic engineering. To do so, three intersections on the Vatan Street corridor in İstanbul, the most crowded city of Turkey, were examined. Various geometric and signal designs were performed for intersections and the most suitable corridor design was analyzed. The corridor designs were modeled with the PTV VISSIM microsimulation software. The most suitable corridor design was evaluated by using the results obtained from the microsimulation via analytical hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) from multi criteria decision analysis (MCDA) methods. The evaluation criteria in the study are vehicle delay, queue length, stopped delay, stops, travel time, vehicle safety, CO emission, fuel consumption, and construction cost. As a result, the current and the most suitable alternative corridors were compared according to the comparison parameters and up to 80% improvements were observed. Thus, some advantages were obtained in terms of energy, environment, time, and cost.

Reducing the use of private vehicles and promoting public transportation (PT) have always been the primary policy objectives of transport authorities. This study aims to model the factors affecting intentions and behaviors of employees to use PT for their commutes by creating an extended theory of planned behavior (ETPB). The ETPB model’s applicability was evaluated using the Partial Least Squares Structural Equation Model (PLS-SEM) technique on a total of 2048 employees in three distinct cities. Then, the Multigroup analysis (MGA) method was used to compare various cities, and demographic variables such as age, education, gender, household income, and walking time to nearest PT stop. The analysis revealed that attitude, perceived norm, and personal agency have a statistically positive influence on employees’ intention to use PT. Moreover, behavioral capability, intention, and habit have a positive effect on PT use, whereas environmental constraints have a negative effect. The results of the MGA analysis revealed significant differences between regions, particularly in terms of environmental factors, intention, and habit. Similarly, the article describes disparities that have emerged according to other demographic variables. The findings imply that interventions by decision makers have the potential to alter the mode of transportation chosen for commuting.

Accurate traffic flow data are crucial for effective transportation planning and management. Different vehicle types impact traffic flow variably, requiring distinct passenger car equivalency (PCE) factors for calculating intersection and road capacity. Headway and spacing data are essential to assess traffic density and service level. Conventional data collection methods are time-consuming and often inaccurate. Unlike existing studies, this study employed computer vision to measure mixed traffic stream volume in terms of passenger car equivalent and collect headway-spacing data with high accuracy. The vehicle detection and counting procedures provide the mandatory infrastructure for measuring mixed traffic stream volume and collecting headway and spacing data. Novel approaches were introduced to gather comprehensive traffic data, including passenger car equivalent values, headway, spacing, flow rate, vehicle speed and traffic volume, using a single system. A custom and comprehensive international dataset was collected to analyse these approaches. Our trained model achieved a mean average precision (mAP) of 97.4%, with accuracies of 95% for headway, 93% for spacing and 99% for PCE values. The dataset can be downloaded at https://github.com/burak-celik/atavehicledataset.

This study investigates the impact response and energy absorption performance of additively manufactured PLA–geopolymer hybrid composites for potential application in road safety barriers. Hybrid Charpy specimens were fabricated with three different infill densities (20%, 60%, and 100%), combining a 3D-printed PLA outer shell with a geopolymer core. Charpy impact tests were conducted in accordance with ISO 179-1 and ASTM D6110, and the absorbed energy, specific energy absorption, and mass efficiency were determined experimentally. A phase-based analytical model was also used to estimate elastic energy contributions, while fracture surfaces were examined to identify infill-dependent damage mechanisms. To extend the material-level findings to an engineering-scale application, the observed trends were transferred to a New Jersey-type road safety barrier model and evaluated using ANSYS Explicit Dynamics. The results showed that infill density strongly affects fracture behavior and energy dissipation performance, with 60% infill providing the most balanced response in terms of energy absorption and mass/material efficiency. The originality of the present study lies in going beyond a material-scale investigation of the impact behavior of additively manufactured PLA–geopolymer hybrid structures by integrally correlating the experimental Charpy results with a theoretical energy-based framework, fracture-surface observations, and explicit dynamic finite element analysis of a New Jersey-type road safety barrier model.