2026-03-09
Yamila S. Grassi, Mónica F. Díaz, Daniel A. Rossit
Assessing the sustainability of urban mobility requires clear indicators and robust decision-making tools, yet current knowledge remains fragmented and unevenly distributed across regions. This study conducts a structured literature review of 38 recent publications to identify the main indicators and multi-criteria decision-making (MCDM) methods used to evaluate sustainable urban mobility. Thirty-five representative indicators were identified, covering traditional sustainability dimensions (economic, environmental, and social) as well as emerging ones such as operational-technical and spatial-urban. Among the MCDM methods, the Analytic Hierarchy Process (AHP) is the most frequently applied for weighting indicators, while the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) is commonly used for prioritizing alternatives. The review also highlights key research challenges, including the need for indicator sets adapted to local contexts, the generation of more region-specific information for Latin America, and the development of approaches that account for data availability and local conditions. To address these gaps, a structured expert consultation was conducted in the medium-sized Latin American city of Bahía Blanca (Argentina), resulting in a set of twelve indicators considered suitable for assessing the sustainability of the local urban mobility system. Overall, the study provides an updated overview of current practices and methodological trends in sustainable urban mobility assessment.
DOI: 10.3389/ffutr.2026.17593142026-02-11
Ömer Kaya
Urban speed management in developing countries frequently relies on fixed physical speed humps. While effective for compliance, these devices can reduce comfort for compliant drivers, increase structural loads on heavy vehicles, and complicate winter maintenance operations. This study develops and evaluates a selective speed-management approach centred on an adaptive speed hump concept that remains flush for compliant drivers and actuates only when speeding is likely or detected. To support deployment decisions in data-scarce settings, a conditional generative (parametric) decision-support module is used to generate synthetic speed distributions from roadway and scenario attributes based on sparse observations. Segment-level speed-violation risk is estimated and combined with additional criteria to compute a Speed-Calming Suitability Index (SSI) for site prioritization. A low-cost laboratory prototype with real-time speed detection and a servo-driven movable surface demonstrates selective actuation at a single point. The modelling workflow produces actionable risk and SSI-based prioritization for targeted traffic calming, and the prototype demonstrates the feasibility of selective actuation. Together, these components support risk-informed selection of candidate locations and practical implementation of a selective traffic-calming mechanism. The results suggest that conditional generative modelling can support sustainable mobility by enabling risk-informed deployment of adaptive traffic-calming infrastructure under data scarcity. Here, “generative” denotes distributional speed sampling for risk inference; the implementation is a lightweight parametric conditional generator (mean plus dispersion) rather than GAN/VAE/diffusion-style architectures.
DOI: 10.3389/ffutr.2026.17659202026-02-09
Rende Cheng, An Liu, Xiaofei Sun, Fangliang Liu, Na Li, Yu Wang, Lu Yang, Quan Yu
This study proposes the FCM-RF-SMOTE framework to resolve the issue of data imbalance in real-time freeway traffic state classification. The framework integrates Fuzzy C-Means (FCM), Random Forest (RF), and the Synthetic Minority Over-sampling Technique (SMOTE). Traffic states are classified into four categories (smooth, stable, congested, and severely congested) based on quantitative thresholds derived from FCM clustering centers. The validation utilizes SUMO simulation with Gaussian noise and a 10 Hz sampling rate to approximate millimeter-wave radar characteristics. Results show that the proposed framework significantly increases the representation of the severe congestion class from 3.67% to 19.83%. Consequently, the overall classification accuracy is enhanced from 77.67% to 97.80%, demonstrating superior performance in handling imbalanced datasets compared to baseline methods. The findings demonstrate the robustness of the algorithm for traffic monitoring systems, particularly in identifying minority traffic states, with future work planned for physical sensor validation.
DOI: 10.3389/ffutr.2026.16624802026-01-28
Idowu Adetona Ayoade, Omowunmi Mary Longe
Wireless electric vehicle (EV) charging systems enhance user convenience and are fundamental to realising autonomous and contactless mobility. Nevertheless, efficiency at high power levels remains constrained by coil misalignment, magnetic leakage, and switching losses. This study presents the design of an analytical hypothesis model formulated to relate the coupling coefficient, mutual inductance, and load conditions to achieve power transfer. The model is then simulated and experimentally validated through a 5 kW at 85 kHz inductive power transfer (IPT) system employing a series–series compensated resonant topology. The mutual inductance coupling and the efficiency (k2Q1Q2) were developed to quantify the sensitivity of power transfer to variations in air gap and misalignment, as well as the quality factor, Q1,Q2. The proposed system achieved a peak simulated efficiency of 92.5% and a measured wall-to-battery efficiency of 88.4%, with harmonic distortion below 6.5% and stable soft-switching operation across the 85–88 kHz range. The experimental prototype maintained zero-voltage switching (ZVS), precise DC-link voltage regulation (310 ± 2 V), and stable constant-current/constant-voltage (CC–CV) battery charging for a 72 V, 40 Ah lithium-ion pack. Power loss analysis indicated that coil copper losses increased from 6.2% at nominal alignment to 10.5% under a 60 mm lateral offset, while inverter and rectifier losses accounted for 4.1% and 3.0%, respectively. Efficiency decreased from 5.02 kW (92.5%) at 10 mm air gap to 3.8 kW (86.7%) at 60 mm, validating the predicted dependence on coupling coefficient and mutual inductance (M≈25μH). Magnetic field mapping confirmed emissions below the ICNIRP 27 µT limit at 10 cm, ensuring user safety. Simulation and experimental results demonstrated strong alignment, confirming effective harmonic mitigation, robust inverter modulation, and accurate CC–CV control. The system’s validated performance, analytical model, and experimental results collectively verify the design’s robustness, safety, and scalability, meeting SAE J2954 standards and offering a high-efficiency solution for next-generation residential and light-commercial EV charging applications.
DOI: 10.3389/ffutr.2026.17399742026-01-07
Maya Arlini Puspasari, Beryl Putra Sanjaya, Richard J. Hanowski, Hardianto Iridiastadi, Salsabila Annisa Arista, Hasna Hamida Nurkamila, Claresta Yasmine Putri Pribadyo, Ahmad Ghanny, Keishandra Nabila Junistya
Urban transport research encompasses transport safety, as accident-related fatalities are a significant problem, particularly in developing countries. In Indonesia, motorcycle crashes account for over 70% of all vehicle crashes. These crashes primarily result from behavioral and performance factors associated with the drivers of other vehicles. Most studies on motorcycle riders focus mainly on riding behavior and skills. However, few have examined how distractions influence rider behavior and traffic incidents, particularly when comparing private riders with motorcycle taxi riders. This study aims to develop a model for motorcycle riders by examining the causal relationships between variables through partial least squares structural equation modeling (PLS-SEM). This study also compares differences in driving behavior among age groups, genders, and driver types (private riders and motorcycle taxi riders). The results show that distractions significantly increase both errors and incidents, while risk perception directly influences speeding behavior. Riding errors and the use of protective equipment also make significant contributions to incident occurrence. Chi-square analyses further reveal that male and older riders report more consistent use of protective gear, younger riders exhibit higher levels of speeding and distraction, and taxi riders adopt safer practices compared to private riders. Based on these findings, this study proposes targeted safety strategies that include strengthening rule enforcement, implementing technological systems, conducting regular infrastructure inspections, and promoting public safety campaigns to enhance rider safety.
DOI: 10.3389/ffutr.2025.17219972026-01-05
Jingwen Yang, Jing He, Wei Liu, Xiaowei Huang, Pan Li
This study addresses two major limitations in the current evaluation system for urban rail train drivers’ emergency handling capability: the lack of clearly defined criteria, and an overemphasis on technical skills to the neglect of psychological factors. We innovatively construct a multidimensional evaluation framework based on the Physio-Psycho-Machine-Environment-Management (PPMEM) model. Through a systematic analysis of the core components of emergency response capability and its influencing factors, a mechanism model rooted in “Human-Machine-Environment-Management” theory is established. Empirically, 30 key influencing factors were identified and categorized into seven dimensions: cognitive, physiological, skill-based, psychological, equipment, environmental, and managerial. A mixed-methods approach was adopted. During the qualitative phase, a system of influencing factors was determined through field studies and in-depth expert interviews. In the quantitative phase, a questionnaire survey was administered to employees of Kunming Rail Transit Operations Co., Ltd. (N = 538 valid responses), and a multidimensional evaluation model was developed using structural equation modeling (SEM) with Amos 26 Graphics. The results indicate that the total effects of latent variables on emergency handling capability, in descending order, are: psychological factors (β = 0.214) > physiological factors (β = 0.212) > environmental factors (β = 0.205) > equipment status (β = 0.126) > cognitive factors (β = 0.105) = skill-based factors (β = 0.105) > managerial factors (β = 0.102). Notably, psychological, physiological, and environmental factors all exhibited effect sizes exceeding the significant threshold of 0.2, constituting a core group of determinants for emergency response performance. Therefore, metro operators should prioritize improvements in drivers’ workload management, mental health support, and environmental adaptability, supplemented by targeted skill and cognitive training, as well as policy refinement. These measures will contribute to a systematic enhancement of emergency response capabilities. The findings provide both a theoretical foundation and practical guidance for strengthening emergency management systems in urban rail transit.
DOI: 10.3389/ffutr.2025.16906262025-10-07
Chen Yin, Naikan Ding, Jinrui Zhang, Zufeng Shao, Chenggang Tang
Drivers’ yielding behavior toward pedestrians is a key determinant of urban road safety. Although deterrence-based interventions such as fines and penalties are widely employed, little is known about the psychological rationalizations drivers use to justify non-compliance. To address this gap, this study integrates neutralization theory and deterrence theory to examine the determinants of yielding intentions. A structural equation model (SEM) was constructed using survey data from 400 licensed drivers in Wuhan, China, to evaluate the dual effects of neutralization techniques and deterrence mechanisms. The results show that three neutralization strategies—denial of injury, denial of victim, and defense of necessity—significantly undermine yielding intentions, while deterrence mechanisms such as formal sanctions and shame exert positive but comparatively weaker influences. Among these factors, denial of victim emerges as the strongest deterrent to yielding, and license-related penalties are perceived as more severe than monetary fines. Overall, the findings demonstrate that the negative impact of neutralization substantially outweighs the positive effect of deterrence, highlighting the limitations of overreliance on punitive measures and underscoring the importance of addressing drivers’ moral disengagement to enhance pedestrian safety.
DOI: 10.3389/ffutr.2025.16715652025-10-02
Samuel Petkac, Tetsuya Sato, Kun Xie, Yusuke Yamani
Trust is a crucial factor that influences human-automation interaction in surface transportation. Previous research indicates that participants tend to display higher levels of subjective trust toward lower-level automated systems compared to high-level automated systems. However, administering subjective trust measures via questionnaires can interfere with primary task performance, limiting researchers’ ability to measure trust continuously in a real-world manner. The current study investigated whether objective and subjective measures of trust exhibit similar patterns across different levels of automation in a simulated driving environment. Twenty-five drivers using an automated driving system (ADS) were randomly assigned to either an active (L2) or passive (L3) automated driving condition. Participants experienced eight near-miss driving scenarios with or without obstructions in a distributed driving simulator and rated their subjective trust before and after navigating the scenarios. Additionally, we coded hand positions from recorded video footage of the participants’ in-vehicle behavior. Hand placements were coded on a predefined five-point system near the time of the simulated connected vehicle technology’s collision alert. Results showed that drivers progressively lost trust in the automated system as they approached and passed the projected collision point in each scenario. Furthermore, drivers in the active condition displayed lower levels of trust than those in the passive condition. This finding contrasts with previous research suggesting that subjective trust ratings are comparable between Level 2 and Level 3 vehicle automation groups. These findings highlight a dissociation between subjective and behavioral measures of trust, suggesting that self-report methods may overlook important aspects of drivers’ trust that can be captured through behavioral measures.
DOI: 10.3389/ffutr.2025.16273682025-09-19
Ji Zheng
How to reduce CO2 emissions from private vehicles with low efficiency and excessive growth is a global challenge for big cities. This study conducted a questionnaire survey for private cars in Beijing in 2019 and developed a path analysis model to uncover the influencing mechanisms of vehicle attributes and driver socio-demographics, travel patterns, and driving behaviors on private car CO2 emissions. The results show that fuel consumption per 100 km (FC), engine displacement (Engine), and annual mileage (Mileage_y) have a positive direct effect on CO2 emissions (E). Driver’s characteristics, including age (Age_d), education level (Edu_d), monthly personal income (Income_p), family size (Pop), whether there are elderly people or children in the family (EK), and eco-driving behaviors (Eco) influence E completely indirectly. The total influence in absolute value is, in order, Mileage_y (0.934) > Engine (0.224) > FC (0.185) > Pop (0.078) > EK (0.032) > Income_p (0.018) > Eco (−0.010) > Edu_d (0.002) > Age_d (−0.001). Higher income and education levels are associated with higher emissions, warranting targeted policy interventions for carbon mitigation.
DOI: 10.3389/ffutr.2025.16049422025-08-07
Nicholas Omido, Rose Luke, Joash Mageto, Thomas Ombati
Commercial airlines face numerous challenges related to operational inefficiencies, poor maintenance practices, overcrowding of airports, supply chain complexity, lack of seamless customer experience, safety issues, data privacy and security issues, increasing fuel prices, and sustainability issues; however, Industry 4.0 technologies are widely regarded as a transformative solution, offering advanced tools and methodologies to address these challenges effectively. Despite the potential benefits of Industry 4.0 technologies, there remains a lack of comprehensive understanding regarding their extent and impact on commercial airlines. The study examined the current state of research on Industry 4.0 in commercial airlines, identified the most significant research topics within this domain, and proposed a future research agenda. The bibliometric analysis was based on 5,113 documents extracted from the Scopus and Web of Science databases, covering 2,109 journals, with an annual publication growth rate of 9.34%. However, Africa’s contribution remains minimal, accounting for less than 1% of the total research output analyzed, highlighting a significant research gap on the continent. The contemporary literature has focused on artificial intelligence, automation, big data analytics, the Internet of Things, and integrating Industry 4.0 technologies. The study was also used to identify the future research agenda of Industry 4.0 in commercial airlines, which includes human-centric approaches, integration of advanced technologies, cybersecurity, environmental sustainability, and ethical and legal implications.
DOI: 10.3389/ffutr.2025.1630011