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.

Driver drowsiness is one of the major reasons behind road accidents, emphasizing the need for accurate and efficient fatigue detection systems that can help monitor practical in-vehicle environments. While significant progress has been made in visual fatigue detection based on deep learning, many previous studies have been performed using a single dataset for training or controlled environments for testing. In this paper, we examine the reliability of lightweight driver-monitoring architectures for vision-based driver drowsiness detection based on three heterogeneous public datasets, i.e., Yawning Detection Dataset (YawDD), Driver Drowsiness Dataset (DDD), and National Tsing Hua University Drowsy Driving Dataset (NTHU-DDD), which cover different lighting conditions, facial characteristics, and head poses as encountered in driving scenarios. Among the considered architectures, Single Shot Detector (SSD)-MobileNetV2 was the most consistent, yielding an accuracy of 92%, precision of 93%, recall of 92%, and F1-score of 92% while also being computationally lighter than the other considered architectures. Reliability of the proposed architecture was statistically validated using the McNemar Test and 95% Confidence Intervals (CI). Our results show that SSD-MobileNetV2 could be a promising baseline for future lightweight driver-monitoring systems for heterogeneous driving environments.

This study develops an integrated planning and operational framework for a next-generation electric bus with high level of service (Electric-BHLS) corridor along the Najaf–Al-Manathira–Al-Meshkhab axis in Iraq. The corridor represents a strategically important urban–rural mobility corridor characterized by rapid demographic growth, fragmented public transport services, congestion, environmental degradation, and increasing dependence on informal low-capacity vehicles. Unlike conventional electric bus operations, the proposed Electric-BHLS model combines high-service operational characteristics—including adaptive service frequency, intelligent transport systems (ITS)-based fleet control, hybrid priority lanes, opportunity charging systems, and real-time operational management—with full electric propulsion and regional accessibility planning. The methodological framework integrates engineering analysis, Geographic Information System (GIS)-based spatial accessibility assessment, operational modeling, and generalized cost optimization. Empirical calibration is based on 2024 field surveys, passenger interviews, Global Positioning System (GPS) based travel-time measurements, institutional datasets, and corridor infrastructure assessments. The proposed system includes articulated electric buses, pantograph opportunity-charging infrastructure, centralized Operations Control Center (OCC) management, smart passenger information systems, and a hierarchical station structure designed to improve operational reliability and multimodal integration. The results demonstrate substantial operational, environmental, and spatial improvements compared with the existing transport system. The optimized Electric-BHLS configuration reduces generalized transport cost by 27%, decreases average passenger waiting time by 61%, and lowers carbon dioxide (CO$_2$) emissions by approximately 29%. Corridor passenger capacity increases from approximately 15,000 to 36,000 passengers per day, while average operating speed improves from 22 km/h to 35 km/h through ITS-supported operational control and selective priority measures. GIS analysis further indicates accessibility gains of 24% in urban areas and 38% in rural catchment zones, improving access to employment, education, healthcare, and regional services. Beyond technical performance, the study evaluates governance, financial feasibility, operational risk, and long-term implementation constraints within the Iraqi context. A phased Design–Build–Operate–Maintain (DBOM) Public–Private Partnership (PPP) framework and a unified corridor governance authority are proposed to support institutional coordination and long-term operational sustainability. The study concludes that Electric-BHLS represents a scalable and economically viable mobility model capable of supporting sustainable regional development and transport modernization in Iraq and comparable developing-country contexts.

River transport remains central to mobility in Banjarmasin, Indonesia, where riverine settlements and uneven road access continue to shape everyday travel and regional connectivity. This study examines the factors associated with water-bus use on the Banjarmasin–Muara Teweh corridor and evaluates how these factors relate to sustainable fluvial mobility. A quantitative survey was conducted with 60 passengers of the Pancar Mas water bus at Banjar Raya Pier, supported by brief interviews on travel motives. The questionnaire covered economic, regional, and social indicators. Data adequacy was tested using the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s test, followed by principal component extraction, Varimax rotation, and confirmatory factor analysis (CFA). The results showed acceptable sampling adequacy (KMO = 0.614) and significant inter-variable correlations (Bartlett’s test, $p <$ 0.001). Three factors were retained and together explained 69.55% of the variance. Economic conditions formed the strongest factor, with income opportunity (loading = 0.879), occupation type (0.800), and job availability (0.735) as the main indicators. Regional characteristics were represented by transport availability (0.913) and accessibility (0.838), while the social dimension was reflected in housing ownership status (0.851). The CFA results also showed acceptable model fit, with $\chi^2$/$df$ = 2.15, goodness of fit index (GFI) = 0.91, comparative fit index (CFI) = 0.93, and root mean square error of approximation (RMSEA) = 0.062. The findings indicate that water-bus use in this corridor is shaped by livelihood opportunities, transport access, and settlement security. The study provides empirical evidence for maintaining river transport as part of regional connectivity and sustainable transport planning in riverine areas.

Advanced driver assistance systems (ADAS) rely heavily on robust object tracking to ensure safe and autonomous navigation, especially in complex outdoor environments. Traditional Kalman filter (KF)-based methods, while effective in ideal conditions, often fall short in scenarios with high noise, asynchronous sensor data, occlusions, and varying environmental conditions. The existing tracking techniques do not adequately address the challenges of multi-object tracking under low Signal-to-Noise Ratio (SNR) or nonlinear dynamics. To bridge this gap, this work proposes Radar and Sensor-Based Tracking with Adaptive Spatial-Temporal Analysis (RASTA), a modified KF-based architecture designed to enhance multi-object tracking using mmWave radar in ADAS. The primary objective of this work was to improve tracking accuracy, handle sensor uncertainty, and enable robust performance in dynamic and noisy conditions. The methodology involved simulating ADAS motion using a discrete Langevin process with bistable dynamics, converting Cartesian trajectories to polar coordinates, and introducing noise to emulate real-world radar behavior. Experimental validation using a mmWave dataset showed that RASTA achieved up to 12.4% improvement in azimuth estimation and 10.7% in radial distance accuracy over baseline methods. The results show RASTA’s effectiveness in delivering reliable, accurate tracking.

Traffic noise has become an increasingly important environmental concern due to rapid urbanisation and growing vehicular activity in residential areas. This study aims to identify the factors influencing traffic noise and develop a predictive framework using partial least squares structural equation modelling (PLS-SEM). Traffic noise measurements were conducted across four residential sections of Shah Alam (Seksyen 7, 9, 20, and 27) using a sound level meter (SLM) at three observation periods: morning (08:00–11:00), afternoon (12:00–15:00), and evening (16:00–19:00). Data collection included traffic volume observations, road geometry measurements, and climatic variables obtained from secondary environmental sources. A total of 504 observations were analysed using SmartPLS 4.0. The measurement model assessment demonstrated that the reflective constructs—traffic volume, road geometry, and the equivalent traffic noise level (i.e., the A-weighted equivalent continuous sound level, $L_{A\mathrm{eq}}$)—achieved acceptable reliability and validity. In contrast, climate conditions were evaluated as a formative construct to better represent the multidimensional contribution of temperature, humidity, and wind speed across observation periods. Structural model results indicated that Climate Condition exhibited the strongest influence within the model and contributed significantly to both traffic volume and $L_{A\mathrm{eq}}$, while road geometry showed a positive relationship with traffic volume. Traffic volume did not demonstrate a statistically significant direct relationship with $L_{A\mathrm{eq}}$, suggesting that residential traffic noise may be influenced by interactions among environmental and roadway conditions rather than vehicle quantity alone. The model demonstrated acceptable explanatory capability, with coefficient of determination ($R^2$) values of 0.727 for $L_{A\mathrm{eq}}$ and 0.552 for traffic volume. These findings highlight the importance of integrating climatic and roadway variables into residential traffic noise assessment and support more context-sensitive approaches for urban transport planning and environmental noise management. Future studies are recommended to incorporate additional operational traffic variables and advanced predictive techniques to improve model generalisability and prediction performance.

The rapid growth of religious tourism has intensified the demand for supporting transport infrastructure, particularly an efficient shared parking system integrated with on-site traffic circulation and pedestrian flow management at sacred sites. This study defines the shared parking scheme as the temporal and spatial allocation of a common facility among buses (for organized pilgrim tours) and passenger cars (for individual visitors), managed by the mosque authority across distinct worship-time windows. Three research questions are addressed: (i) whether visitor groups differ in acceptable walking distance to parking; (ii) whether a digital parking guidance system is suitable across age cohorts; and (iii) how vehicle type influences parking capacity planning. A questionnaire survey was administered to 505 respondents at the Sheikh Zayed Grand Mosque in Surakarta, Indonesia. The Pearson Chi-Square Test examined associations between categorical variables; where more than 20% of cells had expected frequencies below five, Fisher's Exact Test with Monte Carlo approximation was applied, and Cramer’s V was reported as the effect-size measure. Age was significantly associated with nearly all parking-preference variables ($p <$ 0.01), with the 17–32-year cohort showing higher receptivity to digital parking information systems. Vehicle type exhibited significant associations with five of six preference variables ($p <$ 0.05) and the largest mean Cramer’s V, indicating the most consistent though not causal demographic correlate. Travel purpose was significantly associated with visiting duration ($p$ = 0.007) and acceptable walking distance ($p$ = 0.043). Findings yield four operational recommendations: (i) segregated bus and passenger-car zones with dedicated bus-reservation slots; (ii) tiered short-stay/long-stay zoning aligned with prayer-time peaks; (iii) age-differentiated wayfinding combining digital guidance and on-site human assistance; and (iv) temporary traffic control during peak worship hours.