This study investigates the integration of the Transports Internationaux Routiers (TIR) system into Iraq’s Development Road Project and evaluates its implications for transport performance and regional connectivity. Based on data obtained through coordination with the Najaf Directorate of Transport and the Ministry of Construction and Housing, the analysis assesses how the adoption of TIR System procedures can reduce border delays, lower freight costs, and reinforce Iraq’s emerging role as a land-based transit bridge between the Gulf and Europe. Employing a mixed-method design that combines field observation, institutional assessment, and a calibrated cost–time model, the study estimates potential reductions of approximately 45–50% in transport time and 25–30% in operating costs. The findings underline the importance of coordinated governance, digital customs processes, and effective inter-agency collaboration in achieving these efficiency gains. The paper further argues that aligning the TIR System framework with the Development Road supports balanced spatial development, attracts foreign investment, and advances sustainable logistics planning in accordance with Sustainable Cities and Communities (SDG) 9 (Industry, Innovation and Infrastructure), SDG 11, and SDG 13 (Climate Action). The results provide policymakers with a data-driven basis for extending similar corridor models to routes such as Najaf–Karbala and Basra–Al-Faw.

Damage to asphalt roads is frequently caused by waterlogging and overloading. While asphalt pavement remains an economical choice, Indonesia imports 75% of its supply, coinciding with a growing crisis of low-value plastic waste e.g., Low-Density Polyethylene (LDPE), Polystyrene (PS), and Polypropylene (PP) that is economically challenging to sort and recycle. This study proposes a novel solution by utilizing a blended mixture of these plastics (40% LDPE, 30% PP, 30% PS) to simulate unsorted waste streams for modifying Asphalt Concrete-Wearing Course (AC-WC) pavement. The dry mixing process was employed to substitute asphalt at dosages of 0%, 8%, 10%, 12%, and 14% by weight. The research methodology encompassed material characterization, aggregate gradation design, and Marshall testing to determine the Optimum Asphalt Content (OAC) and Optimum Plastic Content (OPC). The durability of the optimal mix was subsequently rigorously assessed through prolonged water immersion at 60 $^{\circ}\mathrm{C}$ for durations of 30 minutes, 24, 48, 72, and 96 hours. Results indicated that a 10% plastic substitution at an OAC of 6.3% yielded the highest Marshall stability, with all volumetric parameters within specified tolerance limits. The mixture exhibited exceptional resistance to moisture damage, evidenced by an Index of Retained Stability (IRS) of 94.64% after 24 hours, surpassing the 90% requirement. Furthermore, the Retained Marshall Stability was 87.40% after 96 hours. Additional durability metrics, including the First Durability Index (FDI) and Second Durability Index (SDI), were analyzed to comprehensively evaluate the performance degradation over time. The findings conclusively demonstrate that modifying asphalt with this blended, unsorted plastic composition is not only feasible but also enhances mechanical properties and durability, offering a viable and sustainable strategy for large-scale plastic waste management in infrastructure development.

Accurate shipboard waste prediction is essential for MARPOL compliance, yet maritime research has predominantly relied on fleet-wide aggregated models that may obscure vessel-specific patterns. The occurrence of statistical paradoxes in hierarchical maritime data has not been systematically examined. This study provides the first systematic documentation of Simpson’s Paradox in maritime operational environmental data, using shipboard waste generation as a case study. By analyzing engine running hours and waste generation from six Indonesian training ships, we demonstrate the risks of data aggregation in maritime predictive analytics. We compared fleet-wide Generalized Linear Models with individual vessel regression approaches using 66 observations over 11 days. Simpson’s Paradox emerged in Auxiliary Engine data: strong individual-level correlations ($r$ = 0.993) were masked by weak fleet-wide correlation ($r$ = 0.416), demonstrating how aggregation can fundamentally misrepresent underlying relationships. Individual ship models achieved substantially higher predictive performance (97.38% and 98.60%) than fleet-wide models (89.5% and 17.3%), with cross-validation (CV) confirming robustness. The findings reveal that fleet-wide aggregation can produce misleading predictions with significant operational consequences for waste storage planning and regulatory compliance. This study establishes the necessity of vessel-specific modeling in maritime environmental management and provides methodological guidance for analyzing hierarchical operational data.

Accurate road roughness prediction is essential for sustainable transportation planning and cost-effective maintenance strategies. This study develops a systematic algorithm to optimize Artificial Neural Networks (ANN) for predicting International Roughness Index (IRI) values using Equivalent Standard Axle (ESA) and road age as primary inputs. The methodology employs comprehensive parameter space exploration across four optimization stages, evaluating various ANN configurations to identify the most effective architecture. Rigorous statistical validation through Analysis of Variance (ANOVA) and cross-validation ensures model reliability. Data quality assessment with outlier detection using the Interquartile Range method was implemented, retaining 94.3% of original observations. The optimized 6-30-25-20-1 ANN configuration, employing logsig and purelin transfer functions, achieved strong performance metrics, including $R$ = 0.9554, $R^2$ = 0.9020, MSE = 0.0153, RMSE = 0.1236, and MAPE = 0.0285. Statistical validation confirmed significant model improvements with an F-statistic of 24.367 and a cross-validation mean of 0.892. The RMSE accuracy of 0.1236 m/km enables reliable pavement condition classification within established IRI thresholds, supporting timely maintenance decisions. This streamlined approach addresses critical infrastructure management challenges by enabling cost-effective maintenance planning with minimal data requirements, particularly valuable for developing countries with limited pavement monitoring infrastructure. The model’s computational efficiency facilitates network-wide deployment for long-term planning and strategic resource allocation. Road agencies can apply this model for maintenance budget prioritization, network-level condition assessment, and multi-year intervention scheduling, particularly in resource-constrained environments where comprehensive pavement monitoring systems are unavailable. This study establishes a structured approach to optimize ANN for IRI prediction, enhance the effectiveness of Pavement Management Systems (PMS), and support sustainable transportation infrastructure through improved maintenance scheduling.