The EU project X-TEAM D2D focuses on future seamless door-to-door mobility, considering the experiences from Air Traffic Management and the currently available and possible future transport modalities in overall multimodal traffic until 2050. This paper deals with developing a Concept of Operations of an intermodal transport system with special consideration of the pabengers' satisfaction with up to 4-hour journeys. For this purpose, the influences of quality management systems and other organizational facilities on the quality of pabenger travel in the transport system were examined. In the study, integration of various management systems, like resources, traffic information, energy, fleet emergency calls, security and infrastructure, and applications such as weather information platforms and tracking systems, is expected.
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This study evaluates the maximum theoretical exposure to radiofrequency (RF) electromag- netic fields (EMFs) from a Fifth-generation (5G) New Radio (NR) base station (BS) while using four commonly used mobile applications: YouTube for video streaming, WhatsApp for voice calls, Instagram for posting pictures and videos, and running a Video game. Three factors that might affect exposure, i.e., distance of the measurement positions from the BS, measurement time, and induced traffic, were examined. Exposure was assessed through both instantaneous and time-averaged extrapolated field strengths using the Maximum Power Extrapolation (MPE) method. The former was calculated for every measured SS-RSRP (Secondary Synchronization Reference Signal Received Power) power sample obtained with a sampling resolution of 1 second, whereas the latter was obtained using a 1-min moving average applied on the applications’ instantaneous extrapolated field strengths datasets. Regarding distance, two measurement positions (MPs) were selected: MP1 at 56 meters and MP2 at 170 meters. Next, considering the measurement time, all mobile application tests were initially set to run for 30 minutes at both MPs, whereas the video streaming test (YouTube) was run for an additional 150 minutes to investigate the temporal evolution of field strengths. Considering the traffic, throughput data vs. both instantaneous and time-averaged extrapolated field strengths were observed for all four mobile applications. In addition, at MP1, a 30-minute test without a User Equipment (UE) device was conducted to analyze exposure levels in the absence of induced traffic. The findings indicated that the estimated field strengths for mobile applications varied. It was observed that distance and time had a more significant impact than the volume of data traffic generated (throughput). Notably, the exposure levels in all tests were considerably lower than the public exposure thresholds set by the ICNIRP guidelines.INDEX TERMS 5G NR, C-band, human exposure assessment, mobile applications, traffic data, maximum extrapolation method, RF-EMF.
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Liveability along four streets in Hanoi, Vietnam is assessed. Hanoi is a rapidly growing metropolis characterised by high levels of personal motorized traffic. Two high traffic volume streets and two low traffic volume streets were studied using a mixed methods approach, combining the collection and analysis of quantitative and qualitative data on traffic volumes and liveability perceptions of its residents. The research methodology for this study revisits part of the well-known Liveable Streets study for San Francisco by Appleyard et al. (1981). A Principal Component Analysis (PCA) shows that residents on both low traffic volume streets experience less traffic hazard and stress, including noise and air pollution, than neighbouring high traffic volume streets. In line with Appleyard, the study shows that low traffic volume streets were rated more liveable than high traffic volume streets. In contrast to Appleyard, however, the study also shows that traffic volumes are not correlated with social interaction, feeling of privacy and sense of home, which is likely caused by the high levels of collectivism typical for Vietnam. Finally, the study indicates a strong residential neighbourhood type dissonance, where a mismatch exists between preferences for living in peaceful and quiet streets and the actual home location of residents.
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Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
