Search Results (4,329)

The rising market penetration of new energy vehicles (NEVs) is transforming urban traffic into a heterogeneous mix of battery electric (BEVs), hybrid electric (HEVs), and conventional fuel vehicles (FVs). For analytical brevity, traditional internal combustion engine vehicles (ICEVs) are hereafter referred to as ‘fuel vehicles (FVs)’ in the discussion of New Energy Vehicle (NEV) networks. This research investigates the efficacy of centralized coordination for NEVs within a localized region, as opposed to individualized speed control, in enhancing the mitigation of traffic congestion. Evaluating traffic efficiency and decarbonization strategies in such settings often requires extensive random sampling and Monte Carlo simulations over a large set of parameter combinations. However, conventional microscopic traffic simulators (e.g., SUMO), which rely on fine-grained modeling of vehicle dynamics and signal control, incur prohibitive computational time when scaled to large networks and numerous experimental scenarios. In this study, battery electric vehicles and hybrid electric vehicles are designed as density-aware vehicles, whose movement speed is adaptively adjusted according to the regional traffic density in their vicinity and the control parameter β. In contrast, fuel vehicles adopt a stochastic movement speed and, together with other vehicle types, exhibit either movement or stoppage in the lattice environment. This density-driven speed-adaptive control and lattice arbitration mechanism is intended to reproduce, in a simplified yet extensible manner, changes in mobility and traffic-flow stability under high-density traffic conditions. The simulation results indicate that, under the same Manhattan road network and vehicle-density conditions, tuning the β parameter of new energy vehicles to reduce their movement speed in high-density areas and to mitigate abrupt position changes can suppress traffic-flow oscillations, delay the onset of the congestion phase transition, and promote spatial equilibrium of traffic flow. Meanwhile, this study develops simplified energy-consumption and carbon emission models for battery electric vehicles, hybrid electric vehicles, and fuel vehicles, demonstrating that incorporating a speed-adaptive density strategy into mixed traffic flow not only helps alleviate abnormal congestion but also reduces potential energy use and carbon emissions caused by congestion and stop-and-go behavior. From a sensing and practical perspective, the proposed framework assumes that future connected and autonomous vehicles (CAVs) can estimate vehicle states and local traffic density through GNSS–IMU multi-sensor fusion and V2X communications, indicating methodological consistency between the proposed model and real-world CAV sensing capabilities and making it a suitable and effective experimental platform for investigating the relationships among new energy vehicle penetration, density-control strategies, and carbon footprint. Full article

In future active distribution networks with high penetrations of renewable energy, flexible loads are expected to play an increasingly important role as reserve resources to support the sustainable and reliable operation of power grids. Accurate evaluation of flexible load reserve capacity is therefore essential for reliable reserve scheduling. Existing research mainly focuses on the operational characteristics and physical constraints of flexible loads, while insufficiently accounting for user response willingness and the uncertainty of user decision-making behavior, which may lead to biased reserve capacity assessments and impair the sustainability of reserve supply in actual grid operation. To address this issue, this paper proposes a results-oriented reserve capacity evaluation method for flexible loads that explicitly incorporates user response willingness. Specifically, a fuzzy logic system is developed to quantitatively characterize the response willingness of electric vehicle (EV) and air-conditioning (AC) users under multiple influencing factors. Then, a probabilistic modeling approach for user decision-making behavior is established using the theory of planned behavior, enabling explicit representation of behavioral uncertainty. Furthermore, a comprehensive reserve capacity evaluation framework for flexible loads is constructed by integrating user willingness states, sustainable response duration, and operational power constraints. Finally, the case studies demonstrate that the proposed method can effectively improve the objectivity of flexible load reserve capacity assessments while maintaining high user participation willingness, thus supporting the long-term sustainable application of flexible loads as grid reserve resources. Full article

This study analyzes how dynamic disturbances influence the decisions made during the human supervision of autonomous unmanned aerial vehicles. While previous research has primarily focused on control algorithms and system stability, the effect of disturbances originating from system dynamics on operator intervention behavior has been less extensively investigated. To examine this problem, a hardware-in-the-loop (HIL) experimental framework was developed, which is based on a previously validated unmanned aerial vehicles (UAVs) test platform and was adapted in this study to enable the investigation of human supervisory decision-making. Participants observed the behavior of an autonomously operating system under controlled disturbances and were provided with the possibility to intervene by activating an emergency landing mechanism. The results indicate that the disturbance intensity had a significant effect on intervention decisions, while the reaction times did not show notable differences. This finding suggests that supervisory behavior is primarily determined by the evaluation of the system state rather than by timing characteristics. It also identifies that subjective risk perception plays a decisive role in the formation of intervention decisions, indicating the presence of an implicit decision threshold for participant behavior. The research findings offer a novel approach to the interpretation of human–UAV interaction by emphasizing the role of system dynamics in shaping user decisions. The presented method may provide a foundation for the development of predictive and adaptive supervisory systems that take into account the characteristics of human decision-making, thereby contributing to the design of safer and more efficient autonomous systems. Full article

Under the demand of electric vehicle DC fast-charging stations for medium-voltage DC–DC converters, the indirect series-connected hybrid clamped topology based on quasi-two-level (Q2L) modulation demonstrates significant application potential due to its balanced advantages in cost and performance, as well as its inherent voltage self-balancing capability. However, a unified analytical framework for the voltage self-balancing mechanism of the hybrid clamped topology under different Q2L modulation strategies has not yet been established, which limits further topology optimization and broader application expansion. To address this issue, this paper first investigates the key factors influencing voltage self-balancing and conducts a comprehensive analysis of the self-balancing behavior under different Q2L modulation strategies, thereby deriving the conditions required to maintain the self-balancing capability of the hybrid clamped topology. On this basis, an improved Q2L modulation method is proposed to ensure stable and reliable voltage self-balancing performance. Finally, both the simulation and experimental results verify the accuracy of the proposed analysis method and the effectiveness of the improved modulation strategy. Full article

This study presents a data-driven framework for the mechanistic interpretation of asphalt pavement responses using an integrated smart sensing and monitoring system deployed on a national highway in Thailand. A fully instrumented pavement test section was developed, incorporating a multi-sensor embedded network and a field data acquisition platform integrated with weigh-in-motion (WIM) technology. The system consists of 54 sensors, including strain gauges, pressure cells, moisture sensors, and thermocouples, installed at multiple depths to capture high-resolution stress–strain responses under controlled heavy-vehicle loading. Field measurements were analyzed and compared with classical mechanistic models, including Boussinesq’s theory, Odemark’s equivalent thickness method, and Burmister’s multilayer elastic theory. The results demonstrate good agreement for vertical stress predictions in deeper layers, while significant discrepancies were observed in strain responses, particularly in the asphalt layer, where measured tensile strains were up to 2.5 times higher than theoretical estimates. The findings indicate that conventional elastic models provide useful first-order approximations; however, discrepancies were observed in representing the viscoelastic behavior of asphalt materials under real loading conditions. Furthermore, the integration of sensor data with traffic loading information confirms that axle load magnitude is the dominant factor governing pavement responses, whereas vehicle speed primarily influences load duration. The proposed framework demonstrates the potential of smart sensing systems for enabling automated, data-driven pavement analysis and supporting digital twin-based infrastructure management. Full article

Driving behavior primitives serve as fundamental building blocks for modeling and semantically interpreting time-series driving behavior. Extracting behavior primitives is challenging due to the high dimensionality and complex interdependencies among behavioral variables, as well as the rich temporal dynamics of real-world driving maneuvers. This paper proposes an unsupervised two-stage framework that optimizes time-series segmentation and segment clustering to yield interpretable and context-aware behavior primitives. First, a Hierarchical Bayesian Model-based Agglomerative Sequence Segmentation (H-BMASS) method is introduced that decouples longitudinal and lateral driving behaviors and performs hierarchical segmentation. This design mitigates under-segmentation by ensuring that change points reflect genuine behavioral transitions. Second, to cluster driving segments of varying durations into a finite set of primitive types, an Integrating Numerical and Trend Discretization Latent Dirichlet Allocation (INT-LDA) model is developed. The model combines variables’ temporal trend discretization with numerical discretization to create symbolic representations of driving data, thereby preserving the essential time dependency of driving behavior and improving segment clustering accuracy. Evaluated on naturalistic driving data collected from a high-fidelity simulator, the proposed framework identifies five distinct behavior primitives with clear physical interpretations. The resulting primitives provide a compact, semantically rich representation of driving behavior, facilitating driver modeling, decision prediction, and scenario-based testing for autonomous vehicles. Full article

Electric vehicle (EV) adoption remains critically low in Thailand despite government initiatives, with limited understanding of how adoption factors vary across different user segments. This study investigates the determinants of EV adoption intentions across three distinct groups—internal combustion engine (ICE) users, hybrid electric vehicle (HEV/PHEV) users, and current EV users—to develop targeted adoption strategies. Data were collected from 3794 Thai vehicle users through on-site administered questionnaires and analyzed using multi-group structural equation modeling, integrating the Technology Acceptance Model, Theory of Planned Behavior, and environmental psychology constructs. Results reveal significant differences in adoption pathways across groups: ICE users show the strongest sensitivity to perceived ease of use, indicating technology apprehension as the primary barrier; HEV/PHEV users demonstrate transitional characteristics with the highest experience-usefulness relationship, while current EV users exhibit stronger influence from environmental identity and social norms. All 14 hypotheses were supported, though with varying effect magnitudes across groups. Surprisingly, the attitude-intention relationship was consistently weak across all segments, suggesting unmeasured cultural or contextual factors. This study contributes the first empirical evidence of segmented adoption patterns in an emerging market, revealing a progression pathway from technology-focused concerns (ICE) through balanced considerations (HEV/PHEV) to identity-driven adoption (EV). Findings provide actionable insights for policymakers to design segment-specific interventions: technology familiarization for ICE users, transition facilitation for hybrid users, and community-building for EV users. Full article

In the Internet of Vehicles, vehicular crowdsensing is crucial for alleviating traffic congestion and ensuring the safety of autonomous driving. However, practical vehicular crowdsensing processes face dual challenges of skewed spatial distributions of vehicles and inadequate data quality guidance. These issues cause sensing redundancy in high-participation areas (HPAs) and coverage deficits in low-participation areas (LPAs), while also leading to unstable data quality. Given that participants’ decisions are profoundly influenced by bounded rationality and psychological preferences, this paper proposes a collaborative incentive mechanism integrating behavioral economics and psychology (BEP-IM) to drive sustained spatial coverage and proactive sensing shaping. First, to mitigate coverage deficits in LPA, a reference-dependent two-sided selection and bidding strategy (RD-TSB) is designed to guide participants toward LPA via a reference-driven utility evaluation. Concurrently, a loss-aversion-based sustained incentive strategy (LA-RPI) is introduced to enhance their sustained participation within LPAs by amplifying loss perception. Furthermore, to overcome weak data quality constraints, an operant conditioning-based proactive sensing shaping strategy (OC-SFQ) is constructed, utilizing a closed-loop mechanism of relative improvement, variable-ratio reinforcement, and association updating to drive participants to output high-quality data. Simulation results demonstrate that the proposed mechanism effectively increases participation frequency in LPAs and optimizes sensing data quality. Full article

The transition to autonomous driving is creating mixed traffic environments in which human-driven vehicles, partially automated vehicles, and autonomous vehicles must continuously interact, adapt, and respond to one another. This paper presents a comprehensive review of driver behavior in mixed traffic with autonomous vehicles, with emphasis on the sociotechnical nature of human–machine coexistence. The review synthesizes recent evidence on behavioral adaptation in car-following and tactical decision-making, trust calibration, situational awareness, takeover performance, internal and external human–machine interface design, surrogate safety metrics, vehicle-to-vehicle communication, operational design domains, and data-driven scenario generation. The literature shows that drivers do not respond to autonomous vehicles uniformly. Instead, behavior varies by driving style, perceived predictability of the automated vehicle, interface transparency, and traffic context. The review also emphasizes that these interaction patterns are context-dependent and may differ substantially across regions, particularly in dense mixed traffic environments. While some adaptations can improve stability and safety, others can encourage opportunistic maneuvers, overtrust, confusion, or degraded takeover quality. The review also highlights that crash data alone are insufficient to assess safety in mixed traffic, and that near-miss analysis, surrogate conflict metrics, and scenario-based evaluation are essential for understanding safety-critical interactions. Across the literature, a central inference emerges: adaptation to autonomous vehicles is real, but it is not automatically stabilizing. Safe deployment therefore depends not only on technical vehicle performance but also on behavioral legibility, transparent communication, calibrated trust, and robust evaluation under diverse real-world conditions. The paper concludes by identifying major research gaps, including the lack of longitudinal studies, incomplete standardization of surrogate metrics, limited understanding of vehicle conspicuity effects, and the need for integrated frameworks that jointly assess driver behavior, system design, and scenario-based safety. Full article

A temporary traffic bridge system (TTBS) is proposed for immediate traffic opening during cast-in-place cement concrete pavement construction in high-traffic urban areas. The basic behaviors such as strains, stresses, and deflections of the TTBS fabricated using reinforced concrete slabs were analyzed numerically and verified through experiments. The concept of the TTBS was explained first, and a detailed description of its components was provided. Afterwards, a TTBS using reinforced concrete slabs was designed and a numerical analysis model for it was created. Using the numerical analysis model of the TTBS, the basic behaviors such as stresses and deflections of the slabs were analyzed when the loads of heavy vehicles such as buses were applied to the interior, joint, and edge of the reinforced concrete slabs. In addition, the behavioral characteristics according to the configuration of the joint between slabs of the TTBS were also analyzed. It was confirmed that the strength of the TTBS can be secured by designing with appropriate shear keys. These shear keys simply create a physical interlocking at the slab joints without applying special elements to the slab joints. They also enable the rapid assembly and disassembly of slabs suitable for the TTBS. To verify these numerical analysis results, small-scale reinforced concrete slabs were manufactured and a TTBS was constructed to conduct experiments. The behaviors obtained through experiments of the reinforced concrete slabs were compared with the behaviors obtained through numerical analyses, and it was confirmed that they were very similar, thus verifying the appropriateness of the numerical analysis model. This study eventually demonstrated that the TTBS can effectively be applied to convert existing asphalt pavements in congested urban areas to more durable cement concrete pavements while minimizing the public inconvenience caused by traffic control. Furthermore, the TTBS constructed using reinforced concrete slabs was evaluated as structurally safe and thus suitable for field application. Full article

This paper investigates the safety behavior of degraded lithium-ion battery modules taken from a 2016 Nissan Leaf (30 kWh, 106,394 km). The vehicle exhibited typical failure symptoms, including P33E6 faults, sudden range drops, and activation of turtle mode under load. Initial diagnostics based on LeafSpy data revealed strong cell imbalance, with a voltage spread exceeding 2.3 V under high current (≈170 A). The weakest cells dropped close to 1 V, suggesting severe internal degradation. To better understand this behavior, selected modules (cells 73–88) were removed and tested under controlled laboratory conditions. Capacity measurements in a 16S2P configuration showed 49.8 Ah in the 4.1–3.1 V range, corresponding to a state of health of about 59%, which is consistent with BMS estimates. However, high-current discharge tests on the weakest segment revealed a much more critical picture. One cell experienced rapid voltage collapse (from ~4.0 V to ~1.2 V), accompanied by a sharp increase in voltage divergence and visible thermal effects. Infrared observations indicated localized heating up to 43 °C and irreversible swelling, pointing to early-stage electro-thermal instability. These results suggest that moderate SOH values do not necessarily reflect actual safety margins under dynamic load conditions. Overall, the study shows that simple OBD-based diagnostics can help identify problematic modules, but additional load testing is necessary to assess real safety risks in aged EV battery systems. Full article

The requirement of sustainable mobility and a clean environment has accelerated the development and adoption of electric vehicles (EVs) and hybrid electric vehicles (HEVs) as an alternative, practical and promising solution against conventional vehicles globally. Such alternative energy vehicles not only provide a critical solution to mitigate fossil fuel dependency and reduce greenhouse gas emissions, but also contribute to producing an energy-efficient transportation system. However, the operational performance, efficiency, and cost-effectiveness of EVs and HEVs are hugely dependent on their powertrain architectures, selection of traction motors and associated control techniques. This paper systematically compares major hybrid architectures: series, parallel, and series–parallel, plug-in, as well as battery and fuel cell electric vehicle platforms, highlighting trade-offs in component sizing, cost, and system integration complexity. The paper critically analyses traction motor technologies with respect to torque–speed characteristics, efficiency behavior, material constraints, and power density. A detailed comparative assessment of traction motor technologies is presented. Furthermore, classical and advanced motor control strategies, including field-oriented control (FOC), direct torque control (DTC), model predictive control (MPC) and AI-enhanced control frameworks, are evaluated with respect to transient performance, robustness, computational requirements, and scalability. The review identifies key technological milestones, emerging next-generation drive technologies, existing limitations, and unresolved research challenges. Finally, critical research gaps and future development pathways are articulated to support the advancement of high-efficiency, reliable, and cost-effective EV/HEV powertrain systems. Full article

The public deployment of electric vehicle charging stations must simultaneously balance construction economics, user accessibility, queueing pressure, feeder security, tail risk under demand uncertainty, and spatial fairness. These criteria are strongly coupled, yet most existing studies either rely on static optimization with limited behavioral realism or use multi-agent reinforcement learning for short-term charging operation rather than for long-term siting. This paper proposes a preference-conditioned multi-agent deep deterministic policy gradient (PC-MADDPG) framework for the urban charging station siting problem in a coupled traffic–distribution environment. Candidate charging sites are modeled as cooperative agents under centralized training and decentralized execution. Each agent outputs a continuous pile-allocation action, which is repaired into an integer expansion plan under a budget constraint. The environment evaluates each plan through attraction-based demand assignment, queue approximation, LinDistFlow-style feeder analysis, and a six-objective performance vector, including annual net cost, travel burden, service inconvenience, grid penalty, CVaR of unmet charging demand, and equity loss. On a reproducible benchmark with 12 demand zones, 10 candidate sites, an 11-bus radial feeder, and 16 stochastic daily scenarios, the proposed framework generates a non-dominated archive with 42 unique feasible plans. A representative PC-MADDPG solution opens 5 of 10 candidate sites and installs 20 fast-charging piles, achieving 99.88% mean demand coverage with an annual profit of 2.083 M$ and a maximum line utilization of 0.999. Relative to the NoGrid ablation, the selected full model reduces grid penalty by 23.87% and equity Gini by 51.08%, with only a 0.35% profit concession. Relative to the NoRisk ablation, the CVaR of unmet demand is lowered by 69.70%. Compared with a demand-greedy baseline, the proposed method reduces grid penalty by 11.72% and equity Gini by 25.19% while preserving similar demand coverage. These results provide proof-of-concept evidence, on a reproducible coupled benchmark, that preference-conditioned multi-agent learning can serve as a practical many-objective siting engine for charging-infrastructure planning when coupled traffic and feeder constraints are explicitly modeled. Full article

The quality of Global Navigation Satellite System (GNSS) observations on smartphones is highly susceptible to multipath and non-line-of-sight (NLOS) effects in urban environments, resulting in complex and highly variable observation errors. These challenges highlight the necessity of a reliable stochastic model to ensure robust and unbiased parameter estimation. However, conventional empirical stochastic models, such as elevation-dependent or signal-to-noise ratio (SNR)-based weighting schemes, are often insufficient to capture the rapidly changing stochastic behavior of observations in dense urban environments. To overcome this limitation, an adaptive GNSS stochastic model based on a deep neural network (DNN) is developed by integrating SNR, satellite elevation angle, and post-fit pseudorange residuals, which provide a strong indicator of observation quality and environmental context. Specifically, a fully connected DNN is designed to use SNR, satellite elevation angle, and post-fit pseudorange residual as input features, representing signal strength, satellite geometry, and residual information, respectively, and to learn their nonlinear relationship with measurement uncertainty. The network output is then used to adaptively update the diagonal elements of the measurement noise covariance matrix, thereby realizing epoch-wise adaptive weighting within the Kalman filtering process. The proposed DNN-based stochastic model, together with several conventional models, was evaluated using GNSS observations collected by a low-cost u-blox ZED-F9P receiver (u-blox AG, Thalwil, Switzerland) and a Samsung Galaxy S21+ smartphone (Samsung Electronics Co., Ltd., Suwon, Republic of Korea) during vehicle experiments in dense urban canyons. The code-based single point positioning (SPP) results demonstrate that the DNN-based model consistently outperforms traditional stochastic models under both open-sky and urban conditions. The improvement is particularly pronounced for smartphone observations in severely obstructed environments. The proposed DNN-based model reduces the 3D RMSE from 14.25 m, 13.68 m, and 13.05 m, obtained with the elevation-, SNR-, and integrated elevation–SNR-based models, respectively, to 8.94 m, representing an improvement of approximately 35%. A similar improvement is observed for the u-blox ZED-F9P receiver, where the 3D RMSE decreases from 5.71 m, 4.69 m, and 5.15 m to 3.10 m. These results suggest the effectiveness of the proposed DNN-based stochastic model in mitigating complex observation errors and improving positioning accuracy, providing a promising solution for reliable positioning of low-cost GNSS receivers in challenging urban environments. Full article

Distracted and fatigue-related driving behaviors are major causes of road traffic accidents, creating an urgent need for reliable driver monitoring systems. Vision-based detection methods have garnered widespread attention due to their low cost of deployment and practical applicability. However, existing lightweight models often suffer from limited global contextual perception and insufficient preservation of fine details. Motivated by these challenges, this study introduces an improved distracted and fatigue-related driving behavior detection model, YOLOv12-LAD, built on the YOLOv12 architecture. The proposed framework integrates a Large Separable Kernel Attention module (LSKA) to enhance global contextual perception, an Adaptive Downsampling module (ADown) to mitigate information loss during feature compression, and a Dynamic Sampling module (DySample) to enable content-adaptive feature reconstruction and improve multi-scale behavior representation. Experimental results show that YOLOv12-LAD achieved 97.5% precision, 96.3% recall, and 98.4% mAP@50 with only 2.5 million parameters, 6.2 GFLOPs, and an inference speed of 249 FPS. Ablation studies, comparisons with representative models, cross-dataset evaluation, and real-vehicle tests further verify the effectiveness and robustness of the proposed method. The proposed method demonstrates strong performance while maintaining computational efficiency, making it suitable for real-time vision-based driver monitoring applications. Full article