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.
Urban mobility and spatial planning policies are intrinsically linked and jointly contribute to social equity as part of an integrated urban system. Developing urban mobility therefore requires careful consideration of residents’ everyday practices and perceptions, alongside the integration of emerging transport modes and technologies. While research on mobility in Algeria has largely focused on traffic engineering and motorization, the social dimensions of everyday transport practices remain insufficiently explored. This study addresses this gap by analyzing neighborhood-scale mobility patterns in Zeboudj, a district of Chlef (194 ha), using a Household Travel Survey (HTS) conducted with 100 households. The results reveal marked inequalities in access to mobility. Students and salaried workers benefit from higher levels of motility, mainly through private cars and collective taxis, whereas women, retirees, and low-income groups remain constrained by limited, costly, and poor-quality public transport. Urban form and planning deficits—including narrow streets, unplanned urban expansion, and the absence of pedestrian and cycling infrastructure—further reinforce car dependence and congestion. At the same time, residents demonstrate strong environmental awareness and express support for alternative and more sustainable mobility options, although these aspirations remain largely unrealized in everyday practice. By adopting a neighborhood-scale perspective, this article contributes to debates on mobility justice and spatial inequality in medium-sized cities of the Global South. It shows how everyday mobility practices reflect broader challenges of sustainable and inclusive urban development and offers practical insights for planners and policymakers seeking to promote more equitable mobility in Algerian cities.
DC-DC converters are vital in hybrid and electric vehicles, enabling efficient energy transfer and stable power management across batteries, auxiliary loads, and traction systems. Traditional Ćuk converters typically require high-voltage coupling capacitors, which increase size, cost, and reduce suitability for automotive use. This study presents a modified Ćuk-based DC-DC converter that reconfigures the conventional topology to utilize a lower-voltage coupling capacitor without changing the basic components. The design is analysed through state-space modelling, transfer function derivation, and integration of a control system to enhance dynamic response and stability. Simulation results obtained in MATLAB/Simulink under various duty cycles show significant improvements over the traditional Ćuk converter, including a 96.9% reduction in coupling capacitor voltage during steady-state operation and a 79.2% reduction in overshoot. These findings confirm the practicality, compactness, and efficiency of the proposed converter, making it a promising solution for reliable power management in hybrid and electric vehicles.
The transition to renewable energy is essential for long-term sustainability, particularly as fossil fuel reserves decline. This study investigates the development and economic feasibility of an affordable solar-powered vehicle tailored for emerging markets. The vehicle aims to reduce dependence on fossil fuels, mitigate air pollution, and offer financial advantages over traditional internal combustion engine (ICE) vehicles. The solar-powered vehicle operates by harnessing solar energy to charge a deep-cycle battery that powers an electric motor, eliminating fuel costs and emissions. Key engineering efforts focused on optimizing chassis design for stability and durability across varied driving conditions. To evaluate performance and predict user benefits, machine learning techniques were employed. A linear regression model assessed charging efficiency under different conditions, while a Random Forest Regression model was used to analyze market demand and travel patterns. Predictive models accurately forecasted travel range and energy consumption, enabling better planning and efficiency. The Solar-powered vehicle demonstrates strong potential for cost savings, low maintenance, and environmental impact reduction. Its integration of solar energy and AI analytics makes it a scalable, data-driven solution for sustainable mobility in emerging markets.
In many Iraqi cities, urban traffic congestion is still a problem, and the use of sophisticated analytical methods is constrained by the lack of trustworthy data. The present research combines both supervised learning and clustering algorithms to create a data-driven model to classify traffic levels in Nasiriyah. An example of K-means clustering was used to derive a categorical congestion-level variable by using field data that were collected on sixteen different districts to explain the underlying traffic patterns. The ability of three classification algorithms—J48 decision tree, Naive Bayes, and random forest (RF) to differentiate between low, medium, and high congestion circumstances was then assessed. With an accuracy of 81.25% and a kappa value of 0.70, the J48 model outperformed the other classifiers on the short dataset and had the most consistent performance. The findings also suggest that the lightweight hybrid strategy can provide authoritative congestion information in data-limited settings and, therefore, present a useful tool to support planning and traffic management decisions in fast-growing cities.
Compressed natural gas (CNG) is an alternative fuel that has less environmental impact than conventional fossil fuels. However, the availability of CNG is a constraint as it is a non-renewable source of energy and is being imported to meet the energy demand of India. Compressed Biogas (CBG), which is produced from renewable sources, has the potential to replace CNG. Due to renewable sources, the emissions from CBG are considered biogenic, and they do not contribute to the carbon bank of the atmosphere. However, the tailpipe emission quantification for automobiles can give an idea of the localised emission comparison for CBG and CNG. In this study, a detailed evaluation of tail-pipe emissions from the passenger car, with CNG and CBG fuels, was carried out using standard test protocol as per the Modified Indian Driving Cycle (MIDC). Statistical tools and techniques such as inter-fuel correlation, t-test, dynamic time warping (DTW), and Cosine Similarity test were utilised for critical evaluation of the emissions of the pollutants at different stages of the test cycle, like cold-phase, hot-phase, and extra-urban driving phase, to evaluate the emissions of CO, HC, NO$_x$, CO$_2$, and CH$_4$. Variability in emissions of CO, THC, NO$_x$, and CH$_4$ was observed in the cold-phase for CNG and CBG fuels. Aggregate CO, THC, and CH$_4$ tail-pipe emissions (mg/km) were found to be lower in the case of CBG than with CNG. Aggregate NO$_x$ (mg/km) and CO$_2$ (g/km) emissions were higher in CBG. Significant variation in THC and CH$_4$ emissions was observed. CO$_2$ emissions were found to be similar for both fuels in all three phases. A marginal reduction (2%) in fuel efficiency with CBG compared to CNG was observed. Tank-to-wheel (TTW) greenhouse gas emissions of a passenger car with CNG as fuel were found to be about 24% lesser with CBG. The granular information generated in this study through a critical evaluation will be useful for engine designers for devising mitigation strategies to control the pollutant levels and to reduce their impact associated with air pollution exposure at ground level.
The rapid growth of online transportation in Indonesia has raised significant concerns regarding the fairness and transparency of fare structures. Current pricing mechanisms, often reliant on dynamic algorithms, frequently fail to incorporate key socio-economic and operational factors, leading to disparities between fare levels and local economic conditions. This study proposes a hybrid multi-criteria decision-making (MCDM) model to evaluate and recommend more equitable minimum fares. The model integrates the analytic hierarchy process (AHP) to objectively determine criterion weights and the simple additive weighting (SAW) method for robust alternative ranking. Four critical criteria are employed: online transport fare affordability, average travel time, the size of the available fleet, and private vehicle ownership. A case study applying this model was conducted across five major Indonesian cities: Jakarta, Surabaya, Palembang, Bandung, and Medan. The results indicate that Jakarta achieved the highest preference score (0.8661), followed by Surabaya, Palembang, Bandung, and Medan. The analysis identified travel time and fare affordability as the most influential criteria in determining fare equity, whereas private vehicle ownership had a comparatively minor impact. The fare recommendations generated by the model demonstrate a closer alignment with local socio-economic realities than existing fares. These findings provide valuable insights for regulators and service providers, supporting the development of more adaptive, transparent, and equitable fare policies that contribute to sustainable urban mobility.
Public transport plays a key role in urban mobility, yet it continues to face persistent safety challenges, particularly in Latin American cities where speeding, traffic accidents, and drunk driving remain frequent. This study presents an Internet of Things (IoT)-based monitoring system built around an ESP32 microcontroller and a set of low-cost sensors, including a NEO-6M GPS module, an MQ-3 alcohol sensor, an MQ-2 gas sensor, and an HC-SR04 ultrasonic sensor. The system monitors critical operational variables such as vehicle speed, driver sobriety, the presence of hazardous gases, and short-range collision risk. Alert messages are generated automatically and delivered through a Telegram bot, while operational data are stored and visualized using a cloud-based platform. The prototype was deployed and tested under real public transport operating conditions. The results show that the system is capable of detecting speeding events, alcohol presence, and abnormal gas concentrations in a timely manner. In addition to vehicle-level monitoring, the collected data can support basic operational safety management by providing information that may assist transport operators in preventive decision-making. Due to its modular design, low implementation cost, and use of widely available technologies, the proposed system offers a practical solution for risk monitoring in public transport systems operating in resource-limited urban environments.
Since the problem of congestion in Jordanian cities is becoming more and more acute, the effectiveness of transit networks becomes critical. The current study will assess the level of user satisfaction and quality of services provided by the introduction of Bus Rapid Transit (BRT) in Amman, Jordan, to address the gap in the research on the topic after implementation. The study addresses the main questions related to the variation in rider satisfaction among the various attributes of service, changes in satisfaction and service quality after the implementation of the BRT, and also determines the priority services in the enhancement of user satisfaction. BRT users were contacted online in order to participate in a survey, in which 104 valid responses were collected through the distribution of the survey. With the findings, there is a positive perception towards accessibility, safety, and services. Also, users provide useful recommendations on how it can be improved, including the issues of queue management, expansion of routes, and improved information provision.
Motorcycle ride-hailing workers often operate under prolonged static postures and intense cognitive demands, exposing them to a heightened risk of musculoskeletal disorders. This study proposes an integrated assessment model that combines biomechanical posture analysis and mental workload evaluation to better characterize ergonomic risks in this rapidly expanding occupational sector. Posture was assessed using rapid upper limb assessment (RULA) and rapid entire body assessment (REBA), while cognitive load was quantified through the NASA-TLX technique. A House of Risk (HoR) approach was further employed to prioritize the contributing factors requiring mitigation. Data were collected from 58 ride-hailing motorcycle operators in active service. The results indicated that 72.4% of workers experienced high musculoskeletal risk levels that require immediate intervention, and mental workload scores exceeded overload thresholds in all six NASA-TLX dimensions. Risk prioritization identified inappropriate motorcycle ergonomics and prolonged working hours as the dominant contributors to health impairment. The integrated model provides actionable insights for ergonomic redesign and occupational risk management in informal transportation services. This framework can be adapted to similar gig-economy environments where combined biomechanical and cognitive stressors affect worker safety and performance.
Countries in the world are currently facing a common challenge: the climate crisis. Transportation and energy sectors contribute a large share of urban emissions. To mitigate climate change and achieve the 2050 Net Zero Carbon target, many countries develop various concepts, such as low-carbon cities (LCCs) and low-carbon mobility (LCM), with the specific intent to reduce urban carbon emissions. This research aims to observe the latest trends in LCM, identify the research gaps, and estimate potential research developments. We used the VOS viewer to analyse 990 Scopus publications related to LCM up to 2024. The results showed that developed countries like the UK, China, Germany, the US, and Japan are the top contributors of LCM studies, but the integration between LCM research and non-motorised transport modes, particularly walking and cycling, remains understudied. This gap allows future research to strengthen the linkage between LCC and LCM concepts, focusing on non-motorised mobility strategies applicable to Southeast Asian urban contexts.
Indonesian metropolitan centers are rapidly expanding into suburbs, which has increased the demand for transportation and commuter flows. Socioeconomic disparity between the central city and the surrounding suburbs in the Mamminasata Metropolitan Area is anticipated to have an impact on commuter well-being, mode of transportation selection, and travel behavior. However, there is still a dearth of actual data demonstrating how commuters’ views and socioeconomic factors interact to influence everyday travel patterns. This study uses covariance-based structural equation modeling (CB-SEM) on commuter data from 379 respondents in the Mamminasata Metropolitan Area to analyze the relationships between socioeconomic characteristics, commuter perceptions, daily mode choice, and travel patterns. The results indicate that commuter travel patterns (CTP) are significantly influenced by perceptions of comfort and safety (PCS) ($\beta$ = 0.626; $p$ $<$ 0.001) and social and health activities of commuters (SHAC) ($\beta$ = 0.222; $p$ = 0.009). Daily mode choice of commuters (DMCC) is mainly influenced by commuter economic activities and livelihoods (EALC), SHAC, LHO, and PCS. DMCC shows a positive relationship with CTP ($\beta$ = 0.320), but this relationship does not reach conventional statistical significance ($p$ = 0.098). Meanwhile, the perception of transport service quality (PTSQ) is related to socioeconomic conditions but does not show a significant direct effect on DMCC or CTP, suggesting that structural factors outweigh psychological considerations in determining commuter behavior. The model explains a moderate to high proportion of variance across endogenous variables ($R^2$ = 0.45–0.60). This study demonstrates that commuter travel behavior in Mamminasata is primarily shaped by service quality perceptions and socioeconomic conditions rather than comfort and safety considerations alone. The findings emphasize the importance of improving public transport service quality and aligning transport policies with commuters’ socioeconomic needs to enhance metropolitan resilience and promote sustainable mobility.
Public transport terminals are expected to operate as functional nodes within urban transport systems, facilitating transfers and supporting network efficiency. However, in many cases, newly developed terminals remain underutilized despite meeting infrastructure standards. This study investigates such a situation in Padang City, Indonesia, focusing on a Type A terminal that has not achieved its intended operational role. The analysis is based on a qualitative case study combining field observations, interviews with terminal managers, operators, and users, and a review of regulatory and operational documents. Rather than examining infrastructure conditions alone, the study looks at how the terminal is positioned within the wider transport system and how institutional arrangements influence its use in practice. The results indicate that low utilization is closely linked to weak system integration. In particular, limited last-mile access, the absence of reliable feeder services, and mismatched operating schedules reduce the practicality of using the terminal. These conditions affect both passengers and operators, making alternative departure points more attractive. At the same time, fragmented responsibilities between different levels of government reduce the consistency of implementation and enforcement, which further discourages compliance with terminal-based operations. Taken together, these factors create a situation in which the terminal functions below its intended capacity. Improving performance therefore requires more than infrastructure provision. Greater attention needs to be given to network integration, coordination between responsible agencies, and the alignment of operational practices with system-level objectives. This case study highlights the importance of viewing terminal governance performance as part of broader transportation system policy, rather than as an isolated facility.
Encouraging travellers to shift from private vehicles to public transport remains a persistent challenge in many urban areas, particularly where bus systems struggle to compete with more flexible modes. This study examines how different dimensions of service quality influence the intention to shift toward urban bus systems. A survey of 650 respondents was conducted in Yogyakarta, Indonesia, focusing on individuals currently relying on private or on-demand transport. A structural equation modelling approach was used to analyse the relationships between service attributes and behavioural intention. The results indicate that all service quality dimensions considered have a significant effect on mode shift intention, though their relative importance differs. Interpersonal aspects of service—particularly empathy and responsiveness—emerged as the strongest predictors, suggesting that user experience is shaped not only by operational performance but also by how passengers are treated. Reliability and tangible service features also contributed meaningfully, while accessibility and assurance played a more limited role. The model explained a substantial portion of the variance in behavioural intention, with an $R^2$ value exceeding 0.60. These findings point to the need for a more user-oriented approach in public transport planning. Improving operational performance alone may not be enough; how passengers experience service interactions matters just as much in shaping travel behaviour. For bus-based systems to become more competitive, attention to both service reliability and interpersonal quality appears essential. The study provides empirical support for strategies aimed at encouraging a sustained shift away from private transport in urban settings.
The rapid expansion of online motorcycle taxi services in Indonesia has been accompanied by a noticeable rise in traffic accidents, particularly in urban areas such as Semarang. In this context, driver-related factors appear to play a more decisive role than infrastructural conditions. This study examines the relationship between personality traits, driving behavior, and accident involvement among online motorcycle taxi drivers. Data were collected from 264 drivers and analyzed using partial least squares-structural equation modeling (PLS-SEM). The results show that honesty-humility, emotionality, agreeableness, conscientiousness, and openness to experience are associated with safer driving behavior, whereas extraversion is linked to riskier patterns. In turn, safer driving behavior is associated with a lower likelihood of accident involvement. By contrast, age and years of service do not show a consistent influence on driving behavior. Based on these findings, a set of behavior-oriented intervention strategies is outlined, with emphasis on aligning safety measures with individual personality profiles. Rather than focusing solely on regulatory enforcement, the results suggest that targeted behavioral approaches may offer a more effective pathway for reducing accident risk in urban motorcycle-based transport services.
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