This paper presents a methodology for generating realistic driving cycles through a combination of Markov chain modeling, Monte Carlo simulation, and dynamic time warping. The study is focused on the construction of a representative driving cycle for the city of Iğdır in Turkey, taking into account its unique traffic characteristics. The methodology involves two main stages: first, determining reference segments partitioned from original driving datasets based on traffic conditions and road types, using the dynamic time warping technique based on the similarity between each segment time series. The second stage is to stochastically generate a representative driving cycle by employing a combination of Markov chain and Monte Carlo simulation, producing variability and randomness. In this stage, the best driving cycle segment of each segment group from among the generated driving segments utilizing Markov chain modeling and Monte Carlo simulation was selected using the dynamic time warping techniques, considering the reference segments. Finally, a representative driving cycle was constructed by stitching each segment. To assess the generated representative cycle, commonly used kinematic parameters were compared with real-world driving cycle data for Iğdır. The results show that the proposed methodology provides an advanced algorithm for generating a reasonable representative driving cycle, which can contribute to energy consumption analysis, vehicle performance, and emission evaluation. The comprehensive approach provided by the proposed methodology enables an accurate understanding of driving patterns, promoting the development of sustainable mobility solutions.

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.

Pavement repair has become an increasingly time-critical operation as traffic volumes grow and lane-closure windows shrink. This has driven demand for materials that gain full structural strength quickly, reopen to traffic within hours, and hold up longer than conventional patches. This study evaluates polymer concrete (PC), a thermosetting resin-bound aggregate system, through combined laboratory characterization and three-dimensional finite element analysis. Compressive strength, splitting tensile strength, unit weight, and apparent porosity were measured at 1, 3, 7, and 28 days of curing. PC reached 85.97 MPa in compression and 7.63 MPa in tension by day three, with near-zero porosity (0.15%) maintained throughout. These three-day values were used directly as material inputs in the three-dimensional finite element analysis (FEA), reflecting the early traffic reopening scenario that defines rapid repair practice. Structural performance was assessed through 36 static analyses in ANSYS 2024 R2, covering flexible (Hot Mix Asphalt, HMA) and rigid (Jointed Plain Concrete Pavement, JPCP) pavement types, three patch sizes (250 × 250 mm, 500 × 500 mm, and 1000 × 1000 mm), and nine load scenarios per configuration. Safety factors (SF) against internal cracking, interfacial debonding, and compressive failure were computed for both PC and traditional patches. PC consistently outperformed HMA and Portland cement concrete patches across all metrics. On rigid pavements, interfacial safety factors exceeded 22.0, confirming that standard surface preparation is sufficient. On flexible pavements, adopting 0.78 MPa as a conservative lower-bound estimate of PC-HMA interfacial bond strength, five scenarios exhibit debonding risk (250-C, 500-C, 500-D, 1000-C, and 1000-D; SF = 0.47–0.99), while the remaining four show high interfacial risk (SF = 1.11–1.30); primer application and mechanical scarification are required for all PC repairs on flexible pavements regardless of patch geometry. Taken together, the experimental and numerical evidence positions PC as a credible, high-performance option for highway repair.

This study presents the first regional validation of water surface elevation (WSE) measurements derived from the Surface Water and Ocean Topography (SWOT) satellite in Türkiye, focusing on three hydrologically distinct inland water bodies: Lake Burdur (a natural lake), Lake Van (a deep alkaline lake), and the Çatalan Dam Reservoir (a managed artificial reservoir). SWOT-derived WSE were assessed against State Hydraulic Works (DSİ) ground-based observations. Initial comparisons revealed substantial discrepancies, with correlation coefficients below 0.30 and root mean square errors (RMSEs) exceeding 0.77 m, highlighting the need for further refinement. To improve accuracy, data filtering based on SWOT quality flags and the exclusion of observations with high WSE standard deviations and uncertainties were applied. These steps enhanced the correlation coefficients above 0.51 and reduced RMSEs to below 1.27 m. Systematic underestimations were observed for Lake Van and Çatalan Dam, whereas both under- and overestimations occurred for Lake Burdur. Applying the median of residuals as a bias correction further decreased RMSEs to below 0.37 m across all study sites. These results underscore the potential of SWOT for monitoring inland water bodies in regions experiencing water scarcity, such as Türkiye, while emphasizing the importance of advanced data processing, integration of high-resolution geoid models, and continued cross validation with in situ measurements. The proposed methodology, both computationally efficient and straightforward, offers a transferable framework for improving SWOT-based WSE and ultimately SWOT derived river discharge estimations based on WSE values across diverse inland water systems worldwide.

This paper presents a methodology for generating realistic driving cycles using Markov chain modelling, Monte Carlo simulation and dynamic time warping. The study focuses on the construction of a representative driving cycle for the city of Igdir, taking into account its unique traffic characteristics. The methodology involves segmenting the original driving datasets based on traffic conditions and road types, creating a detailed representation of driving behaviour. Dynamic time warping is used to identify reference segments for each group of segments, ensuring accurate matching with the generated driving cycles. The integration of Markov chain-Monte Carlo simulation introduces variability and randomness. The proposed methodology provides an advanced algorithm for generating highly accurate representative driving cycles, which can contribute to energy consumption analysis, vehicle performance and emission evaluation. The comprehensive approach provided by the methodology enables an accurate understanding of driving patterns, promoting the development of sustainable mobility solutions and advancing transport research.