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.

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.

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.

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.

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.

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.

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.