Urbanisation across Indonesia’s metropolitan regions has intensified pressure on transport systems, manifesting in persistent congestion, environmental degradation, and structural dependence on private vehicles. Addressing these challenges requires coordinated alignment between transport policy frameworks and the deployment of emerging mobility technologies. This study investigates how policy–technology integration shapes sustainable public transport use within metropolitan transport systems, with particular attention to the role of Urban Density in conditioning behavioural responses. A cross-sectional dataset was collected from 500 public transport users across eleven officially designated metropolitan regions in Indonesia. Structural relationships among key constructs were examined using partial least squares structural equation modelling (PLS-SEM). The analysis demonstrates that both transport policy instruments and digital mobility adoption exert significant influences on perceived service quality and user disposition towards public transport. Among these factors, perceived service quality emerges as the most direct determinant of sustained usage behaviour. In addition, Urban Density is found to significantly moderate the linkage between user disposition and actual behaviour, indicating that high-density metropolitan contexts strengthen the translation of preferences into consistent transport choices. The findings highlight the importance of integrating regulatory measures with digital mobility infrastructures to improve system-level performance and user experience in public transport networks. From a policy perspective, the study underscores the need for metropolitan authorities to adopt coordinated governance strategies that align technological deployment with service provision and spatial planning conditions. These insights contribute to ongoing discussions on sustainable urban mobility by situating behavioural outcomes within a broader transport system and policy integration framework.

Rural electric mobility in Indonesia remains constrained by limited charging infrastructure and unreliable access to grid electricity, particularly in remote areas where motorcycles are the dominant mode of daily transport. At the same time, Indonesia has strong year-round solar energy potential due to its equatorial location. Although solar photovoltaic (PV) charging has been widely recognised as a promising option for off-grid mobility, limited research has examined its suitability for hybrid electric motorcycles (HEM) under actual rural operating conditions. This study combines field measurements and simulation-based modelling to evaluate the daily energy demand of HEM and to assess the feasibility of PV-assisted off-grid charging in rural Central Java, Indonesia. The analysis shows that daily energy demand ranges from 1.2–1.5 kWh, depending on terrain, payload, and travel speed. Simulation results indicate that a system consisting of a 200 Wp PV module, a 1.5 kWh battery, and regenerative braking support can satisfy approximately 87% of daily energy demand during the rainy season and 97% during the dry season. These findings demonstrate the technical potential of solar-assisted HEM for rural transport and provide practical reference values for the design of decentralised off-grid charging systems.

This study develops an interpretable forecasting framework for container throughput with a specific focus on supporting integrated port operations and transport system coordination. Using monthly operational data from Mwani, Qatar (2017–2023), the proposed approach captures trend evolution, seasonal patterns, and calendar-related variations to generate short- and medium-term forecasts of container flows. Beyond predictive accuracy, the framework is designed to provide transparent insights into the operational drivers of throughput dynamics. The analysis identifies vessel call frequency as the dominant factor influencing throughput fluctuations, while trade-related indicators contribute consistent explanatory signals across time. The resulting forecasts show strong agreement with observed values, achieving a mean absolute error (MAE) of 3.84%, which demonstrates the reliability of the approach for operational planning. From a transport integration perspective, the forecasting outputs are directly linked to key decision-making processes within port systems, including quay crane deployment, yard allocation, automated vehicle scheduling, and truck gate coordination. Scenario-based analysis under simulated trade disruptions reveals temporary degradation in forecasting performance, followed by gradual recovery as system conditions stabilize, highlighting the sensitivity of port operations to external shocks. By combining predictive modelling with interpretable analysis, this study provides a practical tool for enhancing coordination between maritime flows and landside logistics. The findings contribute to the development of data-informed strategies for port operation management and offer a scalable approach for improving decision support in integrated transport systems.

Road traffic accidents (RTAs) are a complex crisis created by the combination of infrastructure, drivers, and varying traffic demand factors. While locating clusters of hotspots has been of prime importance in public safety, a research gap still exists in understanding the spatiotemporal evolution of accident severity in administrative hubs. This study fills this gap by focusing on the severity of RTAs in Missouri between 2020 and 2023. In a three-phased methodology, this research assesses sustained efficiency by leveraging a Geographic Information System (GIS)-based framework, involving systematic data integration, calculation of an Accident Severity Index (ASI), and sophisticated spatiotemporal statistics. For the assurance of statistical significance in the detection of clusters, the Getis-Ord Gi$^*$ (G$_i^*$) was used for the localized detection of both hot and cold spots. The methodology outcomes depicted a precipitous decline in the number of accidents in April 2020, which was regarded as a direct consequence of the coronavirus impact. Besides, adults accounted for most fatalities (59%), while speeding was a contributing factor with 29%. Some variations in the occurrence of RTAs were identified during substantial seasons by indicating an optimum persistent occurrence throughout the fall months. Besides, over the main metropolitan areas, robust clustering of RTA density was observed, such as St. Louis and Jackson counties, whereas rural areas exhibited lower densities. The G$_i^*$ identified persistent, high-confidence severity hot spots, indicating progressively clustered, temporally consistent, and persistent patterns of RTA severity in Missouri. The revealed outcomes reflected a granular, evidence-based foundation for urban planners and law-enforcement authorities to implement targeted safety interventions and optimise emergency response allocation.

The Public Utility Vehicle Modernization Program (PUVMP) is a key national reform in the Philippines’ mass transportation subsector. However, its application at the local level, island-provinces, has received limited attention. This study addresses that gap by evaluating Guimaras province’s Local Public Transport Route Plan (LPTRP). A questionnaire survey and transport modeling were used to assess travel behavior, accessibility, and network performance. Results show that many essential facilities, such as schools and health centers, are not adequately served by formal PUV routes. As a result, residents rely on informal modes that are often unsafe and expensive. The analysis also revealed issues with route overlap and inefficient area coverage. To address these local concerns, the study recommends redesigning routes, establishing transfer hubs, and adopting coordinated fleet management. These strategies aim to improve safety, accessibility, and system reliability for commuters. Overall, the findings offer a model for context-sensitive public transport planning in rural and island settings across the Philippines.