Due to the rapid urbanization, economic growth, and the related environmental and other externalities, pivotal challenges can be detected related to sustainable transport systems within the Middle East and North Africa (MENA) territory. The main aim of the study is to identify and present the characteristics of the current sustainable transport environment in the MENA region. Based on the available relevant information in the selected cities of the MENA countries, it analyzes the main trends, the key challenges and also the related opportunities considering sustainable mobility. The sustainable mobility landscape covers, among other themes, sustainable transport, including urban mobility, public transport developments also considering the integration of new technologies. The study highlights the MENA region's commitment to the transition towards a sustainable transport sector, supported by strategic plans and policy reforms, while also addressing the main obstacles to responding to the challenges in the region. Finally, it focuses on policy recommendations to be able to foster the practical implementation of a more resilient and greener transport sector in the examined region.
Previous studies on the correlation between road geometry and pavement condition index have focused on flexible pavement types, which assume constant traffic volume in their modeling approaches. This creates limitations in understanding the behavior of different pavement types under varying traffic conditions and geometries. To address this gap, this study analyzes the relationship between road geometry and the Pavement Condition Index (PCI) across different surface types—specifically, flexible and rigid pavements—while including traffic volume as an independent variable. In Indonesia, road infrastructure is largely dominated by flexible pavements, with a total of 329,189 kilometers in 2024, compared to 218,908 kilometers of other pavement types, including rigid pavements. Reflecting this distribution, the study was conducted on six urban road sections, consisting of four flexible pavement roads and two rigid pavement roads. A non-linear regression approach was applied to develop separate models for each pavement type. The flexible pavement model achieved a coefficient of determination (R2) of 0.570, while the rigid pavement model produced an R2 of 0.561. Among the geometry parameters evaluated, road gradient was found to be the most significant factor affecting PCI in both pavement types. Rigid pavement is recommended for roads with varying alignments to minimize geometry effects on pavement conditions, extending maintenance intervals and improving safety.
Due to rapid urbanization, building new developments leads to increased attraction activities that negatively affect traffic on the existing road network. The influence of land use has increased impacts from the generation of additional traffic, which causes increased congestion with a negative effect on the environment. In this study, we analyze the effects of the construction of the Al Shaab Complex on the traffic volume of a major arterial road located in the north entrance area of Baghdad, which represents one of the main entrances to Baghdad city. Traffic volume data were collected for each day before the opening of the complex and in the four years following the opening. This study's goal is to determine the impact of the new developments on the surrounding roads and manage future traffic operations to facilitate future trips that would be generated by the development activity. Impact assessment was analyzed using PTV Vissim software and the authors found that the control measures could manage the traffic conditions at the road entrance, but they do not solve the problem for long-term planning. A necessary traffic management plan must be created following the impact assessment to lessen the detrimental effects of land use change on the road network.
Pedestrian safety at unsignalized crossings is a critical concern in urban transport, where priority negotiation often depends on informal interactions rather than strict regulatory control. Over the past two decades, researchers have employed diverse methods to model pedestrian priority, ranging from gap-acceptance theory and statistical regression to simulation-based approaches. However, these methods have struggled to capture the complexity and variability of pedestrian–vehicle interactions across different contexts. Recent advances in machine learning (ML) offer new possibilities by enabling the integration of large-scale, high-resolution datasets and the identification of latent behavioral patterns with greater predictive accuracy. This article presents a literature review of the conceptual, methodological, and policy-oriented research on modeling pedestrian priority at unsignalized crossings. It synthesizes contributions from traditional traffic engineering approaches and highlights the emergence of ML-based methods, including supervised learning, deep learning, and hybrid models. Contextual determinants such as infrastructure design, pedestrian and driver characteristics, and multimodal interactions are also reviewed to frame the broader applicability of predictive modeling. The review identifies critical gaps in data availability, model interpretability, and policy translation, calling for future work on explainable, context-aware ML frameworks and interdisciplinary collaboration. By systematically mapping the state of the art, this literature review underscores the potential of ML-driven insights to inform evidence-based transport policy, enhance pedestrian safety, and support the development of inclusive and sustainable mobility systems.
The paper addresses the development of a virtual vehicle model for an electric motorcycle and the design and implementation of a universal measurement system. By integrating the measurement system with the virtual model, accurate estimation of energy consumption data can be achieved in the future. Currently, in Europe, emission standards and other regulations are predominantly focused on passenger cars due to their higher numbers. However, on a global scale, the energy consumption of motorcycles, along with their range and usability, must also be addressed, as they serve as primary means of transportation in many regions. Furthermore, in Europe and other developed countries, replacing traditional internal combustion engine motorcycles with efficient electric motorcycles could significantly reduce harmful emissions in urban areas.The research began with the design of the virtual vehicle model and the measurement system and concluded with the validation of the measurement system. It was established that a universally applicable, low-cost measurement system can be developed, requiring minimal modifications for installation, which allows for the measurement of real-world energy consumption and the necessary drivetrain data. Additionally, it is important to highlight that a virtual vehicle model requiring low computational power can be developed, providing sufficiently accurate predictions of energy consumption.
The goal of zero road fatalities is a global issue. The UN Decade of Action for Road Safety 2011–2020 has successfully reduced road traffic fatalities through global awareness campaigns, aiming to halve road traffic fatalities by 2030. Road safety strategies also support the Sustainable Development Goals (SDGs). Factors influencing crashes include road design, traffic behavior, vehicles, and the environment. On the other hand, the number of crashes in mountainous tourist areas is still very high. This study examines road safety conditions in mountainous regions, focusing on three roads leading to Mount Bromo, East Java, Indonesia. Using the International Road Assessment Program (iRAP), the study evaluates road infrastructure based on crash risk, providing targeted improvement strategies for each road section. The study reveals a high crash frequency on the Lumbang, Sukapura, and Bromo roads, highlighting significant deficiencies in road geometry, signage, and pedestrian facilities. Applying a risk analysis methodology, the study identifies critical areas requiring immediate intervention and categorizes risk levels from "No Hazard" to "Very Hazardous." The findings highlight the need for increased lane widths, improved shoulder areas, better signage, and pedestrian safety measures. Tailored recommendations for each road segment from the road section include road widening, land access controls, traffic signage, and improved street lighting. The results provide valuable insights for policymakers in developing countries, contributing to improved road safety in mountainous and rural areas. The study concludes by urging a proactive approach to road safety, integrating engineering solutions with sustainable practices to reduce crashes and fatalities.
In-wheel-motors have emerged as a transformative technology in vehicle electrification, offering superior efficiency, compactness, and design flexibility over traditional off-wheel-motors. This study focuses on the advanced axial flux permanent magnet synchronous motors used as in-wheel-motors, which are critical for electric vehicle applications due to their high power and torque densities and minimized axial length. Automotive manufacturers increasingly adopt axial flux motors for their in-wheel-motors due to numerous advantages. The most appropriate configuration of axial flux motors is the single-stator double-rotor which supports a higher number of Neodymium permanent magnets, resulting in significantly improved power and torque densities, reduced iron losses, and enhanced efficiency compared to radial flux machines. This paper examines the state-of-the-art axial flux in-wheel-motors specifically designed for electric vehicles, highlighting the design considerations and engineering methodologies that contribute to their superior performance. By analyzing key performance metrics, including power and torque densities, efficiency, magnetic circuit topology, stator/rotor topology, winding configuration, drivetrain integration, and target applications, this paper demonstrates the potential of axial flux IWMs to drive a substantial transition into electric vehicles with allocated wheel torque control.
Electric vehicle battery packs and other components are vulnerable to vibration excitation caused by external variables like road profile because of their unique properties and dynamic reactivity. The application of passive, semi active, and active vibration isolation methods in suspension systems, drive engines, and other vehicle components presents both advantages and disadvantages. This study examines the optimization of battery pack damper parameters using magnetorheological elastomer (MRBD) material, employing a genetic algorithm (GA) technique with magnetic fields produced by 1A and 2A electric currents integrated into electric vehicle pack batteries. 1A and 2A MRBD battery pack dampers have a lower peak transmissibility amplitude than without dampers (WOMRBD), according to simulation results of the dynamic response with input parameters of vehicle speed variations of 10, 20, and 30 km/h. In particular, there is a significant decrease in the peak MRBD 2A, which is more optimal at 0.086 at a medium frequency of 13.4 Hz. Additionally, the power dissipated by MRBD 1A and 2A while absorbing vibrational energy is sent to the battery pack at efficiencies of 51.4% and 39.3%, respectively, in contrast to the scenario without a damper (WOMRBD), specifically at a velocity of 20 km/h.
With the development of intelligent transportation and autonomous driving technology, how to achieve precise and robust synchronous positioning and mapping has become a research focus. To improve the navigation accuracy and environmental perception capability of multi-sensor fusion systems in complex road environments, this study constructs a high-precision synchronous positioning and mapping model that integrates inertial measurement units, LiDAR, cameras, and wheel speed sensors. In addition, a closed-loop detection mechanism and graph optimization method have been introduced to enhance trajectory consistency and drift correction capability. The experiment showed that the positioning accuracy of the proposed method has increased from 85% to 97%, and the false alarm rate has decreased from 22% to 7%. The initial heading deviation was controlled within 0.8 degrees, with a root mean square error of 0.53 m and an average processing time of 60 milliseconds per frame. Further simulation showed that the model had an average positioning accuracy of 93.8%, detection coverage of over 95%, trajectory smoothness better than 0.07 m, and cumulative error drift controlled within 0.37 m/km in three typical road scenarios. The research shows that the model has significant performance in improving positioning accuracy, mapping consistency and environmental adaptability, and has the potential to be popularized and applied in actual autonomous driving systems.
The COVID-19 pandemic posed a great challenge in railway industry, as it changed passengers' behavior towards travelling, which affected their mobility choice in turn. This paper focuses on factors influencing railway passengers' behavior in the new normal, based on 3,318 valid responses collected through an online survey. Variance analysis and exploratory factor analysis were conducted to identify key determinants of passengers' mode choice. Building on these results, a structural equation model (SEM) was developed to describe the interrelationships among passengers' personal attributes, mode characteristics, and travel intentions. The whole modelling process involved selecting latent variables, designing the initial theoretical framework, developing the questionnaire, and estimating the model using maximum likelihood in AMOS, followed by calibration and modification until acceptable parameter significance and model fit were achieved. Considering the trends and uncertainties related to railway industry, 4 development scenarios were constructed based on political, economic, and social factors. The sample data from 4 scenarios were input into the modified SEM model separately. In the end, we could obtain 4 similar SEMs adapting to different scenarios by adjustment. This will provide a scientific basis for formulating railway development strategies in the future for the new normal of post-COVID-19 era.
While operational and structural optimizations for metro stations are well-studied individually, their synergistic effects and resilience under extreme conditions remain poorly understood. This paper establishes a systematic „simulation-optimization-evaluation” framework to quantitatively assess and compare the robustness of different intervention strategies. Taking Liyuan South Road Station of the Ningbo Metro as a case study, we evaluate three scenarios: 1. operational adjustments, 2. structural modifications, and 3. an integrated approach. While the integrated scenario yields the most significant density reduction (9.39% on average, 2.31% in maximum density) under forecasted peak flows, its primary advantage lies in its resilience. Stress tests reveal that the integrated scenario maintains safe operational levels even when passenger flow reaches 1.7 times the forecast, whereas the original and single-strategy scenarios exhibit severe congestion at flow multipliers of 1.3 to 1.4. This study demonstrates that the proposed framework is a valuable tool for decision-makers, enabling a holistic assessment that balances efficiency, safety, and resilience against future demand uncertainty. The findings confirm that integrated strategies, despite marginal increases in transfer times, provide superior safety margins and are essential for the long-term sustainability of high-traffic interchange hubs.
The article analyses employees' competencies in the logistics and transport sector in the Baltic countries - Lithuania, Latvia and Estonia. Based on the assessment of 8 key competencies (including communication in native and foreign languages, digital competence, entrepreneurship, assimilation of new knowledge, etc.), an analysis of three organisational levels (employees, managers, management) was conducted. The results reveal significant differences between countries and organisational levels in the approach to the importance of competencies. Estonia stands out as a leader in digital and technological maturity. In Latvia, a consistent orientation towards learning and intercultural skills is observed, whereas in Lithuania, employees' identity competencies dominate, but management does not emphasise them. Cultural, structural, and managerial factors that determine the devaluation or recognition of competencies are discussed. The article emphasises that competencies must be developed at all organisational levels, integrated into strategic planning, and assessed not only as functions, but also as value and cultural foundations of organisations.
The rapid urbanization and escalating traffic congestion in Penang, Malaysia, have intensified transportation challenges for low-income workers. While shuttle bus services are increasingly recognized as a sustainable mobility solution, there remains limited empirical research on their adoption among economically disadvantaged commuters in urban Malaysia. This study addresses that gap by exploring the factors influencing the use of shuttle bus services among low-income workers in Penang, guided by the Theory of Planned Behavior and Mobility Transition Theory. Employing a mixed-methods approach, the study integrates quantitative data from 306 respondents identified through the Penang eKasih Welfare Program 2016 – selected via stratified random sampling – with qualitative insights from 10 regular shuttle bus users. Factor analysis reveals that the most influential determinant in choosing a mode of commute is the shuttle's capacity to ensure reliable and direct access to workplaces. Notably, qualitative findings indicate that some users continue to opt for the state-provided shuttle bus despite owning private vehicles, citing significant savings on fuel, tolls, and parking as key motivators. This highlights a crucial insight: affordability and accessibility outweigh convenience for this demographic. The novelty of this study lies in its focus on shuttle services as an equity-driven transport intervention within a Malaysian urban context. The findings support the expansion of such services, particularly to employment-dense areas, and provide actionable recommendations for policymakers aiming to promote inclusive, efficient, and sustainable urban mobility.
The 14th of July Arterial Road is one of the most important urban roads in western Baghdad, experiencing heavy traffic congestion during rush hour from 7:30 a.m. to 8:30 a.m. It has become a major traffic congestion problem. It is one of the most attractive main roads in Baghdad due to its location, extending from the north to the city center, and its connection to several major roads. In this research, three links on the 14th of July Arterial Road were selected: link 1 from Aden intersection to Sana'a intersection, link 2 from Sana'a to Tobji intersection, and link 3 from Tobji intersection to Al-Shaljiya intersection. The performance of the 14th of July Road and its intersections was analyzed using Synchro11 software under current operating conditions to study the impact of intersections on travel time on the 14th of July Arterial Road. The analysis results showed that the travel time northbound was 860.6 s with a level of service of F, and the travel time southbound was 989.9 s with a level of service of F. To improve the travel time and level of service on the main road, overpasses were proposed at the intersections. The analysis conducted after improving the intersections of 14th July Road revealed that the travel time to the northbound became 372.5 sec with service level C, and the travel time to the southbound became 249.6 sec with service level B.
The paper presents the results of a study applying numerical simulation to investigate the effects of load, pressure and speed on the performance of Bridgestone Ecopia EP422 tires. The 3D model of the research object was built using Siemens NX software and analyzed using Ansys Workbench. The main output parameters including deformation, stress and contact area were analyzed under various operating conditions. The results show that load directly affects the contact area; low pressure increases the contact area but reduces fuel efficiency; high speed reduces contact time and causes stress concentration at the sidewall. The simulation results are consistent with previous experimental studies, confirming the reliability of the model and its applicability in tire design and optimization. The topic opens up the potential for integrating advanced factors such as material nonlinearity, environmental impact and artificial intelligence in subsequent studies.
As the electric vehicle industry rapidly develops, the problems of cumbersome operation and insufficient anti-offset capability of traditional wired charging technology are becoming increasingly prominent. Wireless charging technology, as an innovative solution to range anxiety, has received widespread attention. To raise the performance and efficiency of wireless charging systems for electric vehicles, an improved double-layer orthogonal DD coil structure is developed, and an efficient magnetic field-coupled wireless energy transmission system based on bipolar coupling is established. Practical application denoted that the efficiency of the system reaches 92.35% at a power of 3 kW and remains at 87.64% at 9 kW. The power fluctuation rate is less than 4.30%, and the dynamic response time is shortened to 20.50 ms. Through magnetic field closed-loop control and dynamic parameter optimization, the system's environmental adaptability (operating temperature range −20~85 ºC) and reliability (mean time between failures, MTBF 50,000 hours) have been improved. This study provides theoretical support and engineering practice reference for the large-scale application of wireless charging technology for electric vehicles and promotes the commercialization process of wireless charging technology.
Image recognition, a key technique in deep learning within the realm of computer vision, has found extensive application in the transportation sector in recent years. However, traditional image recognition technologies suffer from low efficiency and weak analytical capabilities. This research proposes a traffic sign recognition model that embeds a reconstructed squeeze and excitation network channel attention mechanism into the You Only Look Once Version 4 framework. Specifically, depthwise separable convolution is adopted to reconstruct the attention module, and a soft threshold denoising module is integrated before multi-scale feature fusion. The model also utilizes a soft threshold denoising module for feature extraction of complex semantic information. Experimental results show that when the attention mechanism fusion algorithm iterates five times, the accuracy reaches 98.5%. The highest recognition accuracy, prediction recall rate, and harmonic mean of recall rate are 96.35%, 95.88%, and 95.12%, respectively. The evaluation of the fusion model shows that the model has the highest recognition accuracy of 0.98 for different types of traffic signs. Compared with the highest accuracy of 0.89 of Faster Region-based Convolutional Neural Network and the highest accuracy of 0.90 of Field-Programmable Gate Array, the research method has significantly higher recognition accuracy. These results suggest that the improved traffic sign recognition model can effectively identify real-world road traffic signs for autonomous vehicles, with excellent feature-capturing performance. This research contributes to the future development of road traffic and autonomous driving fields.
Our study conducts bibliometric and co-occurrence network analysis to explore key trends in the field of electric micromobility research. The paper is focuses on examining how academic attention has shifted across research domains, identifying changes in thematic keywords, assessing the evolution of multidisciplinary studies, and pinpointing influential authors in micromobility. Using the Scopus database, we analyzed 1,471 articles (as of March 2024), revealing that while engineering initially dominated this field, since the 2020s, both social sciences and medicine have emerged as critical areas of study for electric micromobility vehicles (EmV). Our analysis of key terms shows that "electric scooters" has consistently been central across different periods, underscoring a sustained focus on micromobility's core themes. Additionally, we observed a marked increase in interdisciplinary research after 2015, particularly at the intersection of engineering, social sciences, and environmental science, highlighting a collective effort to address complex urban mobility issues. This article also identifies Cherry Christopher as the most frequently cited author in micromobility, reinforcing the foundational role of earlier research in guiding contemporary academic discussions. By uncovering these trends, the analysis points out the increasing relevance of social science perspectives as a promising future research direction in the context of EmVs - encompassing topics such as user behavior, public acceptance, regulatory frameworks, social equity, and gender-based approaches. These aspects complement the traditionally engineering-driven approaches. While the growing thematic diversity suggests a shift toward more nuanced and multifaceted investigations, the precise role and impact of interdisciplinarity in this field remain subject to further exploration.
Road traffic accidents constitute a significant societal challenge, leading to substantial losses in human life and material resources. The escalating number of vehicles, driven by increasing motorization rates and population growth, has exacerbated this problem. Although traffic collisions are stochastic events in temporal and spatial dimensions, their global impact remains severe, with millions of fatalities and injuries recorded annually.A survey-based methodology was employed to assess public perception of this proposed traffic management solution. The findings indicate that the introduction of such signage could contribute to a reduction in accident frequency on Polish roads. Despite isolated dissenting opinions, statistical analysis reveals majority support among respondents for adopting green-bordered advisory speed signs.The research underscores the potential efficacy of non-binding speed recommendations as a supplementary measure to enhance road safety while highlighting the importance of aligning traffic regulations with driver behavior and preferences.The article analyzes the possibility of introducing a traffic sign with a green border in Poland. The survey showed that the majority of respondents (63%) support the introduction of a sign with a green border indicating the recommended speed on Polish roads. The sign is seen as a tool that can improve traffic safety, educate drivers and increase the smoothness of driving. Among the advantages of the proposed solution, respondents pointed out to the following: educating drivers on safe speed (38%), reducing the stress of mandatory speed limits (23%), increasing traffic flow (18%) and reducing exhaust emissions (10%).
This paper presents a comparative study between microscopic traffic simulation and vehicle dynamics simulation to evaluate their consistency and applicability for driver behavior analysis. The study focuses on four representative driving scenarios: roundabouts, four-way intersection, highway overtake, and U-turn. Traffic simulations were conducted using SUMO, while vehicle dynamics simulations utilized the double-track vehicle model in MATLAB SIMULINK, driven by the Pure Pursuit control algorithm. Trajectories were visually compared using x-y plots, and the maximum positional deviations were calculated. Speed profiles and heading angles were analyzed as functions of distance, complemented by a quantitative metric based on phase, amplitude, and topology errors. This metric, developed in earlier work, provides a method for comparing vehicle behaviors. The research results highlight the need to incorporate detailed vehicle dynamics into traffic simulations for improved realism, especially in scenarios with high lateral acceleration and abrupt steering inputs. Refining traffic simulations with improved vehicle behavior will also make road traffic flow predictions more realistic and reliable.
