Recognition of traffic signs by drivers is essential for ensuring road safety. Recently, with the growing demand for driver assistance systems and autonomous vehicles, traffic sign recognition has become increasingly important. In this study, Spatial Transformer Networks (STN) integrated with Convolutional Neural Networks (CNN) were used to classify traffic signs. STNs minimize the effects of geometric distortions by applying affine transformations to images, thereby improving classification performance. This study focuses on adapting and optimizing an STN-based CNN model specifically for the Russian Traffic Signs Dataset (RTSD) to achieve higher classification accuracy. The proposed model was trained and tested on the RTSD. First, the proposed CNN model was trained on the RTSD-R1 and RTSD-R3 datasets, achieving accuracy rates of 89.15% and 94.3%, respectively. Then, by integrating STN into the CNN model, the proposed model was trained on the RTSD-R1 and RTSD-R3 datasets, achieving accuracy rates of 93% and 95%, respectively. These results demonstrate that incorporating STNs into the CNNs is effective in improving traffic sign classification performance.

This study develops and validates an integrated model for evaluating passenger service quality (SQ) in Thailand’s rural railway system by embedding environmental and engineering perspectives within the RAILQUAL framework. Drawing upon SERVQUAL, Grönroos's model, and Servicescape theory, it introduces the Eco-Rail Atmosphere Quality (Eco-RAQ) construct, which incorporates sustainability attributes-greenhouse-gas reduction, waste management, traction-energy efficiency, and renewable energy efficiently-into the Rail Atmosphere Quality (RAQ) dimension. Survey data from 1,013 passengers were analyzed using covariance-based structural equation modeling (CBSEM). The final model exhibits excellent fit ($\chi^2$/df = 1.096, CFI = 1.000, RMSEA = 0.010) and explains 91.5% of variance in rail efficiency quality (REQ. RAQ demonstrates the strongest total effect on REQ ($\beta$ = 0.848, $p$ $<$ 0.001), while Eco-RAQ shows a meaningful but more modest total effect ($\beta$ = 0.257, $p$ $<$ 0.001), influencing REQ both directly and indirectly through rail perceived quality (RPQ). Validity diagnostics confirm discriminant validity (HTMT $<$ 0.85) and no substantive common-method bias. The findings advance service-quality theory by integrating sustainability cognition into the Stimulus-Organism-Response paradigm and by proposing Eco-RAQ as a socio-technical mechanism linking passenger perception with operational performance. The model offers actionable insights for achieving Sustainable Development Goals (SDGs) 9, 11, and 13 in rural rail contexts.

Efficient light rail transit (LRT) systems are crucial for sustainable urban mobility; however, unforeseen departure delays continue to be a major hurdle, undermining operational reliability and passenger satisfaction. This study establishes a data-driven framework for forecasting departure delays by combining static GTFS schedules with real-time GTFS operational data from the Canberra LRT system. The dataset included 15,538 records with 42 attributes, spanning from 28 August 2020 to 13 August 2022. A stringent preprocessing pipeline was implemented, encompassing temporal feature engineering and feature selection based on mutual information. The Random Forest regressor with feature engineering and selection (RFR-FEFS) attained the highest predictive performance on the test set ($R^2$ = 0.94, MAE = 2.93, MSE = 34.32). The high accuracy indicates the model’s efficacy, yet it necessitates careful evaluation of potential overfitting and its generalizability beyond the examined system. Ablation experiments were performed to assess the impact of various feature groups by omitting temporal, spatial, or operational attributes. The findings indicate that the exclusion of temporal features decreased $R^2$ to 0.90, the exclusion of spatial features reduced it to 0.93, and the exclusion of operational features resulted in the most significant decline to 0.23. These findings affirm that all three feature categories contribute distinctly and synergistically to model performance. This research illustrates the capability of integrating diverse GTFS data with sophisticated machine learning techniques to attain precise LRT delay forecasts. Nevertheless, the framework was validated solely on one system and time frame; future research should investigate its transferability to other cities and integrate supplementary contextual data, including meteorological conditions and incident reports, to improve robustness and practical applicability.

Maritime transportation safety is a strategic priority in Indonesia’s national logistics system as an archipelagic country. However, high shipping intensity and limited inspection resources pose serious challenges to the effectiveness of ship inspections. Ship Safety Inspectors (PPKK) play a crucial role in ensuring the seaworthiness of ships through inspections of nautical, technical, and communication (radio) aspects; however, their functional contributions have rarely been systematically examined. This study employs a quantitative approach using survey methods and Structural Equation Modeling–Partial Least Squares (SEM-PLS) analysis techniques. A sample of 64 CSOs from three strategic ports (Tanjung Priok, Soekarno-Hatta, Makassar, and Sorong-Pelra) was analyzed to examine the relationship between inspection functions, workload, and maritime safety. The results indicate that technical, nautical, and communication functions significantly influence workload, while technical functions and workload have a direct impact on maritime safety. Work volume acts as a mediating variable in the model. The implications of these findings emphasize the importance of enhancing the competence of PPKK and developing risk-based inspection policies. This study provides empirical contributions to the reform of the maritime safety supervision system and opens opportunities for cross-national validation to strengthen the generalizability of the model.

Aceh Province is a critical case for freight and infrastructure studies due to its geographic isolation, post-disaster recovery context, and heavy dependence on roads for over 95% of commodity transport. Despite its rich agricultural output, limited multimodal infrastructure hampers efficient distribution. This study aims to (1) analyze the effect of road network connectivity on commodity transportation and regional development, and (2) develop a forecasting model to predict future commodity transportation needs in Aceh Province. The Structural Equation Modeling (SEM) was applied to analyze the relationships among Road Network Connectivity (RNC), Freight Transport (FT), and Regional Development (RD), using data from 400 respondents across 23 districts. The SEM results show all latent variables are interconnected. FT plays a strong mediating role, linking connectivity improvements to development benefits. The study also develops forecasting models for commodity generation and attraction based on population, expressed as $Y$ = 2.209 $X_1$ and $Y$ = 2.807 $X_1$. These models highlight population as a reliable predictor of freight demand and can be generalized to other regions with similar geographic and infrastructure constraints. This research introduces a novel SEM-based framework for freight analysis in Indonesia and offers policy insights for integrating road infrastructure planning with regional development strategies.

Pavement distress is a critical factor in road maintenance planning, directly influencing transportation safety, serviceability, and infrastructure costs. While traditional mechanistic and statistical models provide limited accuracy, they often fail to capture the nonlinear and multi-factorial nature of pavement deterioration. This study addresses this gap by proposing an integrated machine learning (ML) framework that incorporates real-time traffic and climatic variables for predicting pavement roughness. The framework draws on multiple open-source datasets, Long-Term Pavement Performance (LTPP), Federal Highway Administration (FHWA) traffic volumes, and National Oceanic and Atmospheric Administration (NOAA) climate records, to construct a multidimensional feature space. Four predictive algorithms were benchmarked: Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP). Ensemble-based models achieved superior predictive accuracy, with Random Forest attaining R$^2 \approx$ 0.89 and Root Mean Square Error (RMSE) $\approx$ 0.61, outperforming traditional regression baselines. The findings highlight that ensemble learning can more effectively capture non-linear dependencies between structural, traffic, and climatic factors than alternative approaches. Beyond technical performance, the study illustrates the potential of integrating continuously updated environmental and traffic data into pavement management systems, offering a pathway to more cost-efficient, reliable, and sustainable maintenance planning.