Search Results (48)

We present a nonlinear dynamical model for vehicular traffic jams and their dissolution based on the noise-perturbed onset of chaos. The model makes use of the bifurcation gap generated by addition of noise to quadratic iterated maps. The gap results from the elimination by noise of periodic and chaotic attractors with large periods and large numbers of chaotic bands, respectively. The bifurcation gap is recapitulated at the transition to chaos (vanishing Lyapunov exponent) as a crossover from noiseless to irregular, chaotic-like regimes at an iteration time $t_{cross}$ with value dependent on the noise amplitude. This behavior is employed in a model (with variants) that we design for multilane road congested traffic. We highlight four main model properties that are also present in the dynamics of glass formation: (i) plateau interrupted relaxation; (ii) Adam–Gibbs empirical law; (iii) aging; and (iv) diffusion arrest. The model bridges previous studies that have indicated analogies between glassy dynamics and vehicular traffic as well as nonlinear dynamics and same-name traffic. We also discuss the connection of the model with urban multilane road networks. Full article

Low Earth orbit (LEO) satellite networks require real-time routing to cope with dynamic topology variations caused by continuous orbital motion. As an alternative to conventional routing approaches, deep reinforcement learning (DRL) has recently gained attention as an effective means for optimizing routing paths. To solve routing problems modeled as a grid-based Markov decision process (grid-based MDP), DRL methods such as CNN-based Dueling DQN have been proposed. However, these approaches are difficult to implement in practice. In particular, the substantial floating-point computation and memory traffic of CNN inference make real-time onboard inference challenging under the stringent power and resource constraints of satellite platforms. To address these constraints, this paper proposes an INT8 quantization and hardware–software co-design framework using heterogeneous SoC FPGA acceleration. We offload compute-intensive CNN inference to the programmable logic (PL), while the processing system (PS) orchestrates overall control and data movement, forming a collaborative PS–PL architecture. Furthermore, we integrate the NITI-style two-pass scaling with PS–PL exponent propagation to preserve end-to-end integer consistency without floating-point conversion. To demonstrate its practical onboard feasibility, we employ standard accelerator implementation choices—such as output-stationary scheduling and on-chip prefetching—and conduct an ablation study over independently tunable axes (PE array size and PS-side buffer reuse) to quantify their incremental contributions. Experimental results show that the proposed PS–PL cooperative scheme dramatically reduces computation time compared to a PS-only reference implementation on the same platform. Full article

Indoor environmental quality (IEQ), influenced by ventilation and acoustic conditions, directly affects human health and comfort. Existing studies often concern either ventilation or sound insulation alone, neglecting the impact of the trickle ventilator's internal structure and its combination with windows on overall performance. This study introduced a double-chamber model to quantify the ventilation performance of three trickle ventilators using tracer-gas-decay and pressure-difference methods. We calculated the flow coefficient ($C_d$) and flow exponent ($n$) to reveal differences in pressure sensitivity, with trickle ventilator TV2 showing the highest-pressure sensitivity ($C_d$ = 1.34, $n$ = 0.89). The weighted sound reduction index ($R_W$) and weighted sound insulation index for traffic-noise correction ($R_W +{ C}_{tr}$) were measured, showing trickle ventilators TV1-1 and TV1-2, and TV2 were 29 dB, 30 dB, and 34 dB, respectively. And the sound insulation and ventilation performance of window-trickle ventilator combinations were analyzed. Trickle ventilators could enhance acoustic performance for low-insulation windows but reduce it for high-insulation windows. The study also quantitatively balanced ventilation and acoustics. This research provides data support and theoretical guidance for the synergistic optimization of ventilation and sound insulation in building environments and provides guidance on ventilation and noise control strategies suited to different floor levels and outdoor noise environments. Full article

Objective: Predicting driver fatigue degree is crucial for traffic safety. This study proposes a deep learning model utilizing electroencephalography (EEG) signals and multi-step temporal data to predict the next time-step fatigue degree indicator percentage of eyelid closure (PERCLOS) while exploring the impact of different EEG features on prediction performance. Approach: A CTL-ResFNet model integrating CNN, Transformer Encoder, LSTM, and residual connections is proposed. Its effectiveness is validated through two experimental paradigms, Leave-One-Out Cross-Validation (LOOCV) and pretraining–finetuning, with comparisons against baseline models. Additionally, the performance of four EEG features—differential entropy, $\alpha /\beta$ band power ratio, wavelet entropy, and Hurst exponent—is evaluated, using RMSE and MAE as metrics. Main Results: The combined input of EEG and PERCLOS significantly outperforms using PERCLOS alone validated by LSTM, and CTL-ResFNet surpasses baseline models under both experimental paradigms. In LOOCV experiments, the $\alpha /\beta$ band power ratio performs best, whereas differential entropy excels in pretraining–finetuning. Significance: This study presents a high-performance hybrid deep learning framework for predicting driver fatigue degree and reveals the applicability differences in EEG features across experimental paradigms, offering guidance for feature selection and model deployment in practical applications. Full article

As the demand for air transportation surges, issues like flight conflicts and air-route congestion within multi-airport terminal areas have grown progressively more serious. Analyzing the evolution of air traffic situations in these areas can effectively enhance the air traffic’s early-warning capability, reduce flight conflicts, and alleviate air-route congestion. This paper proposes a method for analyzing the evolution of air traffic situations in multi-airport terminal areas based on flight segment–flight state interdependent network. First, a flight segment–flight state interdependent network model is established. This interdependent network model consists of an upper-layer flight state network, a lower-layer air-route network, and coupling edges. The upper-layer network is constructed with aircraft as nodes and flight conflicts between aircraft as edges. The lower-layer network takes air-routes as nodes and the connection relationships between air-routes as edges. The inter-layer coupling edges are determined by judging the relationship between aircraft and air-routes. If an aircraft is on a certain air-route, there exists a coupling edge between the aircraft node and the air-route node. On this basis, by comprehensively considering three network indicators, namely node degree, weighted clustering coefficient, and node strength, the overall air traffic situation value is obtained. Finally, experimental verification and analysis were conducted in an actual flight scenario of a multi-airport terminal area in the Guangdong–Hong Kong–Macao Greater Bay Area. The results show that the proposed method can accurately reflect the air traffic situation. The time-series analysis of the situation evolution reveals that the evolution process has chaotic characteristics. Full article

Biomass burning processes affect many semi-rural areas in the Mediterranean, but there is a lack of long-term datasets focusing on their classification, obtained by monitoring carbonaceous particle concentrations and optical properties variations. To address this issue, a campaign to measure equivalent black carbon (eBC) and particle number size distributions (0.3–10 μm) was carried out from August 2019 to November 2020 at a coastal semi-rural site in the Basilicata region of Southern Italy. Long-term datasets were useful for aerosol characterization, helping to clearly identify traffic as a constant eBC source. For a shorter period, PM_2.5 mass concentrations were also measured, allowing the estimation of elemental and organic carbon (EC and OC), and chemical and SEM (scanning electron microscope) analysis of aerosols collected on filters. This multi-instrumental approach enabled the discrimination among different biomass burning (BB) processes, and the analysis of three case studies related to domestic heating, regional smoke plume transport, and a local smoldering process. The AAE (Ångström absorption exponent) daily pattern was characterized as having a peak late in the morning and mean hourly values that were always higher than 1.3. Full article

This study examines the seasonality of Black Carbon (BC) and Brown Carbon (BrC) spectral absorption characteristics at a continental background site (Kozani) in southern Balkans (NW Greece). It aims to assess the seasonality and impact of different sources on light absorption properties, BC concentrations, and the fraction of BrC absorption. Moderate-to-low BC concentrations were observed, ranging from 0.05 µg m⁻³ to 2.44 µg m⁻³ on an hourly basis (annual mean: 0.44 ± 0.27 µg m⁻³; median: 0.39 µg m⁻³) with higher levels during winter (0.53 ± 0.33), reflecting enhanced emissions from residential wood burning (RWB) for heating purposes. Atmospheric conditions are mostly clean during spring (MAM) (BC: 0.34 µg m⁻³), associated with increased rainfall. BC components associated with fossil fuel combustion (BC_ff) and biomass burning (BC_bb), maximize in summer (0.36 µg m⁻³) and winter (0.28 µg m⁻³), respectively, while the absorption Ångstrôm exponent (AAE_370–880) values ranged from 1.09 to 1.93 on daily basis. The annual mean total absorption coefficient (b_abs,520) inferred by aethalometer (AE33) was 4.09 ± 2.65 Mm⁻¹ (median: 3.51 Mm⁻¹), peaking in winter (5.30 ± 3.35 Mm⁻¹). Furthermore, the contribution of BrC absorption at 370 nm, was also high in winter (36.7%), and lower during the rest of the year (17.3–29.8%). The measuring station is located at a rural background site 4 km outside Kozani City and is not directly affected by traffic and urban heating emissions. Therefore, the regional background atmosphere is composed of a significant fraction of carbonaceous aerosols from RWB in nearby villages, a characteristic feature of the Balkan’s rural environment. Emissions from the lignin-fired power plants, still operating in the region, have decreased during the last years and moderately affect the atmospheric conditions. Full article

Vehicular Ad Hoc Networks (VANETs) serve as critical platforms for inter-vehicle communication within constrained ranges, facilitating information exchange. However, the inherent challenge of dynamic network topology poses persistent disruptions, hindering safety and emergency information exchange. An alternative generalised statistical model of the channel is proposed to capture the varying transmission range of the vehicle node. The generalised model framework uses simple wireless fading channel models (Weibull, Nakagami-m, Rayleigh, and lognormal) and the large vehicle obstructions to model the transmission range. This approach simplifies analysis of connection of vehicular nodes in environments were communication links are very unstable from obstructions from large vehicles and varying speeds. The connectivity probability is computed for two traffic models—free-flow and synchronized Gaussian unitary ensemble (GUE)—to simulate vehicle dynamics within a multi-lane road, enhancing the accuracy of VANET modeling. Results show that indeed the dynamic range distribution is impacted at shorter inter-vehicle distances and vehicle connectivity probability is lower with many obstructing vehicles. These findings offer valuable insights into the overall effects of parameters like path loss exponents and vehicle density on connectivity probability, thus providing knowledge on optimizing VANETs in diverse traffic scenarios. Full article

Unmanned Aerial Vehicles (UAVs) are integral to the development of smart city infrastructures, enabling essential services such as real-time surveillance, urban traffic regulation, and cooperative environmental monitoring. UAV-to-UAV communication networks, despite their adaptability, have significant limits stemming from onboard battery constraints, inclement weather, and variable flight trajectories. This work presents a thorough examination of energy and spectral efficiency in UAV-to-UAV communication over four frequency bands: 2.4 GHz, 5.8 GHz, 28 GHz, and 60 GHz. Our MATLAB R2023a simulations include classical free-space path loss, Rayleigh/Rician fading, and real-time mobility profiles, accommodating varied heights (up to 500 m), flight velocities (reaching 15 m/s), and fluctuations in the path loss exponent. Low-frequency bands (e.g., 2.4 GHz) exhibit up to 50% reduced path loss compared to higher mmWave bands for distances exceeding several hundred meters. Energy efficiency (${\eta }_e$) is evaluated by contrasting throughput with total power consumption, indicating that 2.4 GHz initiates at around 0.15 bits/Joule (decreasing to 0.02 bits/Joule after 10 s), whereas 28 GHz and 60 GHz demonstrate markedly worse ${\eta }_e$ (as low as ${10}^{-3}$–${10}^{-4}\mathrm{bits}/\mathrm{Joule}$), resulting from increased path loss and oxygen absorption. Similarly, sub-6 GHz spectral efficiency can attain $4\times {10}^{-12}\mathrm{bps}/\mathrm{Hz}$ in near-line-of-sight scenarios, whereas 60 GHz lines encounter significant attenuation at distances above 200–300 m without sophisticated beamforming techniques. Polynomial-fitting methods indicate that the projected ${\eta }_e$ diverges from actual performance by less than 5% after 10 s of flight, highlighting the feasibility of machine-learning-based techniques for real-time power regulation, beam steering, or multi-band switching. While mmWave UAV communication can provide significant capacity enhancements (100–500 MHz bandwidth), energy efficiency deteriorates markedly without meticulous flight planning or adaptive protocols. We thus advocate using multi-band radios, adaptive modulation, and trajectory optimisation to equilibrate power consumption, ensure connection stability, and meet high data-rate requirements in densely populated, dynamic urban settings. Full article

Road traffic significantly impacts global energy consumption and emissions, both of which contribute to climate change. Thus, energy conservation and emission reduction in road transportation are critical concerns, and traffic flow modeling is key to evaluating and improving these metrics. Therefore, this paper develops a microscopic traffic model to characterize energy consumption reflecting driver energy saving awareness. The well-known Intelligent Driver (ID) model cannot predict traffic dynamics within an energy saving driving environment because it is based on a fixed acceleration exponent. Simulation results are presented which demonstrate that the energy consumption in the proposed model decreases as driver energy saving awareness increases. Furthermore, traffic, in the proposed model, experiences smaller variations in flow, speed, acceleration, and density, higher speeds, and less congestion compared to the ID model. Thus, the proposed model can be employed to conserve energy and reduce emissions, thereby decreasing the overall carbon footprint of traffic and contributing to a more sustainable environment. Full article

In this study, we investigate the relationship between parameters and the dynamic behavior of traffic flow in road traffic systems, and we propose a segmented cost function to describe the effects of this flow on the dynamic gravity model at different saturation levels. We use single-parameter bifurcation analysis, maximum Lyapunov exponent calculation, and three-parameter bifurcation analysis to reveal the effects of parameter variations on the nonlinear dynamical behaviors of the modified gravity model, and we investigate the evolution laws of the traffic system in depth. In order to solve the problems of low efficiency and poor visualization ability in traditional dynamics analysis techniques, this paper proposes the Hilbert curve dimensionality reduction technique, which can completely retain the original data features. The three-dimensional pseudo-Hilbert curve is used to traverse the three-parameter bifurcation data, realizing the transformation of data from three- to one-dimensional. Then, the two-dimensional pseudo-Hilbert curve is used to traverse the reduced one-dimensional data, and the two-dimensional visualization of the three-parameter bifurcation diagram is successfully realized. The dimensionality reduction technique provides a new way of thinking for parameter analysis in the engineering field. By analyzing the two-dimensional bifurcation plan obtained after this reduction, it is found that the modified gravity model is more stable compared with the original model, and this conclusion is also verified by the wavelet transform results. Finally, a new robustness evaluation index is defined based on the dynamics of the model, and the simulation results reveal the intrinsic correlation between the saturation parameter and road congestion, which provides an important basis for promoting sustainable transportation in the road network. Full article

The development of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs), and autonomous driving (AD) has progressed rapidly in recent years, driven by artificial intelligence (AI), the internet of things (IoT), and their integration with dedicated short-range communications (DSRC) systems and fifth-generation (5G) networks. This has led to improved mobility conditions in different road propagation environments: urban, suburban, rural, and highway. The use of these communication technologies has enabled drivers and pedestrians to be more aware of the need to improve their behavior and decision making in adverse traffic conditions by sharing information from cameras, radars, and sensors widely deployed in vehicles and road infrastructure. However, wireless data transmission in VANETs is affected by the specific conditions of the propagation environment, weather, terrain, traffic density, and frequency bands used. In this paper, we characterize the path loss based on the extensive measurement campaign carrier out in vehicular environments at 700 MHz and 5.9 GHz under realistic road traffic conditions. From a linear dual-slope path loss propagation model, the results of the path loss exponents and the standard deviations of the shadowing are reported. This study focused on three different environments, i.e., urban with high traffic density (U-HD), urban with moderate/low traffic density (U-LD), and suburban (SU). The results presented here can be easily incorporated into VANET simulators to develop, evaluate, and validate new protocols and system architecture configurations under more realistic propagation conditions. Full article

As part of the research in the recently ended project SANET II, we were trying to create a new machine-learning system without a teacher. This system was designed to recognize DDoS attacks in real time, based on adaptation to real-time arbitrary traffic and with the ability to be embedded into the hardware implementation of network probes. The reason for considering this goal was our hands-on experience with the high-speed SANET network, which interconnects Slovak universities and high schools and also provides a connection to the Internet. Similar to any other public-facing infrastructure, it is often the target of DDoS attacks. In this article, we are extending our previous research, mainly by dealing with the use of various statistical parameters for DDoS attack detection. We tested the coefficients of Variation, Kurtosis, Skewness, Autoregression, Correlation, Hurst exponent, and Kullback–Leibler Divergence estimates on traffic captures of different types of DDoS attacks. For early machine recognition of the attack, we have proposed several detection functions that use the response of the investigated statistical parameters to the start of a DDoS attack. The proposed detection methods are easily implementable for monitoring actual IP traffic. Full article

A microscopic traffic flow model is developed that incorporates vehicle vibrations due to pavement condition. The Intelligent Driver (ID) model employs a fixed exponent so traffic behavior is the same regardless of the road condition. Thus, it ignores the underlying physics. To address this limitation, the proposed model employs the Pavement Condition Index (PCI) in describing traffic behavior. The performance of both models is evaluated on a $3000$ m circular road using the Euler numerical discretization technique. The results show that the performance of the proposed model varies with the pavement condition (PCI), as expected. Furthermore, the traffic flow increases with vehicle speed. The oscillations in speed and density with the proposed model decrease as the PCI increases, and are larger when the speed is higher. Consequently, the results with the proposed model align more closely with reality as they are based on the PCI, and so are a more accurate representation of traffic behavior. Full article

Among the effects of space weather, the degradation of air traffic communications and satellite-based navigation systems are the most notable. For this reason, it is of uttermost importance to understand the nature and origin of ionospheric irregularities that are at the base of the observed communication outages. Here we focus on polar cap patches (PCPs) that constitute a special class of ionospheric irregularities observed at very high latitudes in the F region. To this purpose we use the so-called PCP flag, a Swarm Level 2 product, that allows for identifying PCPs. We relate the presence of PCPs to the values of the first- and second-order scaling exponents and intermittency estimated from Swarm A electron density fluctuations and to the values of the Rate Of change of electron Density Index (RODI) for two different levels of geomagnetic activity, over a time span of approximately 3.5 years starting on 16 July 2014. Our findings show that values of RODI, first- and second-order scaling exponents and intermittency corresponding to measurements taken inside PCPs differ from those corresponding to measurements taken outside PCPs. Additionally, the values of the first- and second-order scaling exponents and of intermittency indicate that PCPs are in a turbulent state. Investigation of the coincidence of loss of lock (LoL) events with PCPs displayed that approximately 57.4% of LoLs in the Northern hemisphere and 45.7% in the Southern hemisphere occur in coincidence of PCPs when disturbed geomagnetic activity is considered. During quiet geomagnetic conditions these percentages decrease to 51.4% in the Northern hemisphere and to 20.1% in the Southern hemisphere. Full article