Car occupant fatalities are overrepresented in car-Heavy Goods Vehicle (HGV) frontal impacts even with legislated crashworthiness requirements for both vehicle types. While most modern passenger cars in Europe exceed mandated safety levels by a wide margin, protecting occupants in car-HGV frontal impacts remains a major challenge. An offset frontal crash test (Test-1) conducted in Sweden highlighted that the front structures currently used in HGVs cannot fully engage with the front structures of a 5-star Euro NCAP-rated passenger car and absorb only a limited share of the crash energy. A new study and follow-up test (Test-2) were conducted to explore potential improvements in HGV design. In Test-1, the HGV’s Front Underrun Protection (FUP) beam fractured early in the impact, resulting in minimal energy absorption by the frontal structures. The car experienced extensive deformation because its longitudinal crash beams were not effectively engaged. Two key areas of improvement were identified: (1) horizontal load distribution across HGV FUP system and (2) energy absorption potential. A simulation study using a simplified HGV model was performed to develop an experimental front structure with integrated FUP to improve crash performance, taking advantage of the EU’s new regulation for extended HGV fronts. Improved load distribution was achieved by using a steel backup plate supported by bracing integrated into existing truck structures. A honeycomb structure based on the IIHS 2.0 side impact barrier was developed to improve energy absorption. The barrier’s geometry was designed to align with known passenger car interaction zones, thereby promoting activation of energy-absorbing components. The follow-up test (Test-2) was conducted with the experimental HGV front and compared with Test-1. The passenger car benefited from horizontal load distribution in Test-2 which reduced the deformation around the A-pillar and door. However, higher intrusions were observed in the driver’s seating area. The maximum acceleration recorded in the baseline test was 75g, which was reduced to 58g in the second test, closer to Euro NCAP MPDB test results (35g) for the same car. The impact energy in both tests was significantly higher than in any previous car-HGV impact study. The results demonstrated that dynamic loading of the HGV front imposes structural demands different from those assessed by the quasi-static UN R93 test requirements. The severity levels in this study reflect conditions possible in real-world traffic (even in low-speed urban environments), but they exceed practical thresholds for current design and regulatory frameworks. Future regulatory and consumer testing that integrate 1) active safety systems that reduce collision speed and mitigate crash severity with 2) improved structural performance will promote vehicle designs that address common crash scenarios on rural and urban roads.

COLA II and its predecessor project, COLA I, investigated mental fatigue in air traffic control. Both projects addressed fatigue in real-world field settings, aiming to close a gap in longitudinal, repeated field measurement of fatigue in air traffic control. Multiple field studies were conducted, all using a minimal-intrusion design: three software-based measurement timepoints per shift (Pre/Mid/Post) combining fatigue self-evaluation with a performance-based fatigue measure. COLA II extended the work through more detailed analysis of further fatigue factors, broadening scope via two online surveys (boredom/monotony, and break behaviour/recovery), a methodological study on the performance-based measure, and a systematic review of fatigue antecedents across eight aviation occupational groups. A central finding, replicated across studies, was a systematic divergence between subjective and objective fatigue. Consistent with prior findings, night shifts produced the highest fatigue on both measures, with subjective fatigue peaking around the circadian low regardless of the night-shift type. Performance-based fatigue generally leveled off toward the end of shifts. Age emerged as a moderator: older air traffic controllers showed slower reaction times in the performance-based measure but reported less subjective fatigue than younger air traffic controllers. This research report concludes that fatigue in ATC is a multidimensional phenomenon that cannot be adequately measured through a single measurement channel, nor managed through a simple one-size-fits-all regulatory approach. The overarching findings advocate a continued movement toward a risk- and data-based Fatigue Risk Management System approach.

Electric railway systems (ERS) are characterized by several particularities regarding the return current circuits, moving loads, multiple sources of waveform distortion, and extensive deployment of static converters from various manufacturers, topologies, and solutions. This work presents a methodology for application of load monitoring to classify rolling stock (RS) waveform distortion signatures. The proposed methodology combines the benefits and advantages of unsupervised deep learning and reconstruction error performance classification for performing non-intrusive load monitoring (NILM) in ERS. It consists of adapting autoencoder-based novelty detection for load classification problems. The method is applied to pantograph measurements from four rolling stock items using two types of data input (harmonic spectra up to kHz and VI diagram images), which are compared in binary classifications of the same kind of railway electrification. The methodology shows suitable classification performance with high accuracy, scoring an average of 98.81 % for spectrum input and 97.77 % for VI diagram input. It has also been validated with a NILM dataset (LIT) for multi-class applications showing 99.13 % for spectrum input and 94.28 % for VI diagram input. The proposed method has suitable computational times and scalability, allowing application to a wide range of NILM and classification problems using distortion signatures.

Assessing harmonic distortion measurements in the electric railway power systems (ERPS) requires evaluating the time-varying behavior, interactions, and performance in different time scales. This paper aims to map and assess harmonic voltage levels in 13 traction converter stations for the Swedish railway power supply system, with findings that have direct practical implications. For that, measurements from the public and railway grid sides for 69 weeks are analyzed. Statistical values are explored for the harmonic voltage spectra and total harmonic distortion (THD) variation. The public grid side measurements are investigated using 95th percentile weekly values, and performance is evaluated by comparing the recommended planning levels of IEC 61,000–3–6. The intraweek variation complements the information about the time-varying behavior of the THD. The 95th percentile, minimum daily values, and intraday variation are explored to understand the time-based behavior since there are no reference limits from standards for comparison, looking to the railway grid side. Extended analysis is placed on the railway grid side to highlight some aspects of measurement time-aggregation based on 10-min values, and time-series trend analysis is used to confirm traffic planning impact. Discussion and findings regarding railway operation, the technology deployed at the traction converter station, time-varying behavior, traffic planning impact, measurement time-aggregation, and spectra patterns were presented.

This work addresses the imbalance in the literature regarding the modeling of waveform distortion resonance and propagation in low-frequency railway electrification systems, focusing on the Swedish Electric Railway Power Supply (ERPS). A framework is proposed for modeling synchronous components of waveform distortion up to 25 kHz, incorporating local interactions between traction converter stations (TCSs) and rolling stock. The nodal admittance matrix is developed for an autotransformer-fed circuit with three railway sections and four TCSs. Rolling stock is represented through a measurement-based Norton model capturing high-order components and probabilistic harmonic emissions. Monte Carlo (MC) simulations assess resonance and propagation uncertainties under variable operating conditions. Results reveal key influences of railway section lengths, filters, and stochastic emission models on impedance characteristics, offering ERPS tools for future assessment.

This work proposes an application of unsupervised deep learning (DL) on 2-D images containing VI diagrams of measured railway pantograph quantities to find patterns in operating conditions (OCs) and waveform distortion. Measurement data consist of pantograph voltage and current measurements from a Swiss 15 kV 16.7 Hz commercial locomotive and a French 2x25 kV 50 Hz test-dedicated locomotive, containing more than 4000 records of 5-cycle snippets for each system. The variational autoencoder (VAE), followed by feature clustering, finds patterns in the input data. Each cluster captures patterns from the VI diagrams, which contain information from current and voltage waveshapes and sub-second variations. The time-domain admittance allows inference about the rolling stock (RS) operation and the waveform distortion spectra, including harmonics and supraharmonics characteristics from both RS and traction supply. The VAE successfully performs data embedding using only 16 channels in the latent space. The effectiveness of the method is quantified by means of the mean square reconstruction error (never larger than 1.5% and equal to 0.31% and 0.33% on average for the Swiss and French case, respectively). The t-SNE visualization confirms that overlapping of clusters is negligible, with a percentage of “misplaced” cluster points of 2.18% and 2.50%, again for the Swiss and French case, respectively. The computation time for the VAE prediction could be brought to some tens of ms representing a performance reference for future implementations. The proposed VI diagram assessment covers emissions for different OCs, rapid changes in power supply conditions, and background distortion caused by other trains on the same line, including line and impedance changes due to the moving load. In this perspective physical justification is found by domain knowledge integration for the identified clusters. A concluding discussion regarding advantages, limitations, and potential improvements or diversification is also included.