Carbon Dioxide Monitoring in Refuse Collection Vehicle Cabins to Reduce the Risk of SARS-CoV-2 Airborne Transmission

During the COVID-19 pandemic, essential workers such as waste collection crews continued to provide services in the UK, but due to their small size, maintaining social distancing inside waste collection vehicle cabins is impossible. Ventilation in cabins of 11 vehicles operating in London was assessed by measuring air supply flow rates and carbon dioxide (CO2) in the driver's cabin, a proxy for exhaled breath. The indoor CO2 indicated that air quality in the cabins was mostly good throughout a working day. However, short episodes of high CO2 levels above 1500 ppm did occur, mainly at the beginning of a shift when driving towards the start of their collection routes. This data indicated that the ventilation systems on the vehicles were primarily recirculating air and the fresh air supply made up only 10-20 % of the total airflow. Following recommendations to partly open windows during shifts and to maintain ventilation systems, a second monitoring campaign was carried out, finding on average, an improvement in ventilation on board the vehicles. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Used Car Price Prediction in Surabaya Using Random Forest Regressor Algorithms

The use of private vehicles during the Covid-19 pandemic has increased because private vehicles, especially cars, are considered as the safest mode of transportation to maintain distance and prevent transmission of the Covid-19 virus. Based on data from two different Indonesian secondary car market place, a comparison of a price sample of Car X in the city of Surabaya with the specifications for the 2015 to 2018 car years with car milage under 1000 kilometers, the used cars have a variety of prices hence a used car price prediction system is needed so that people can find out the average price of used cars sold in the market. In this study the author will use the Random Forest Regressor as a machine learning algorithm to predict the price of a used car with a dataset from the AtapData website. The reason for choosing the Random Forest Regressor is because the algorithm has the power to handle large amounts of data with high dimensions with categorical and numerical data types. The evaluation method used in this study is the Root Mean Absolute Error which produces a value of 0.55612 for validation data and 0.56638 for testing data, while the evaluation proceed with Mean Absolute Error produces a value of 0.45208 for validation data and 0.47576 for testing data. © 2022 IEEE.

An Incremental Learning-based Framework for Non-stationary Traffic Representations Clustering and Forecasting

To curb the growth of COVID-19, many rules, including a work-from-home policy, were issued in 2020. While these limits successfully prevented the virus's transmission, they completely altered original mobility patterns, resulting in considerable reductions in travel time and vehicle miles traveled. Under this non-stationary data stream, the US Department of Transportation struggled to anticipate future traffic conditions. Obviously, two essential challenges need to be addressed immediately: 1) it is challenging for transportation agencies to learn representative traffic patterns from the continually changing traffic circumstances. And 2) when and how should the forecasting model be updated to learn new patterns without forgetting previous tasks? We proposed an incremental learning-based framework for non-stationary data clustering and forecasting in transportation scenarios to tackle the issues mentioned above. It is a dual-module architecture that includes a Temporal Neighborhood Clustering module and an Incremental Learning module. The objective of the first component is to dynamically detect the optimal boundary for clustering statistically similar neighbors by enlarging both the in-group similarity and between-group dissimilarity. The second module applies the online-EWC approach, which is commonly used in image classification tasks but rarely in regression models, to learn new tasks and avoid catastrophic forgetting, which is a typical occurrence while training neural networks with multiple tasks. Experiments on the Greater Seattle Area employed loop detector data collected in 2020 yielded reliable prediction performance in both robustness and accuracy. The dual-module framework can generate promising results from pre-COVID-19 to post-COVID-19 time frames. This framework would aid government agencies and the general public in developing long-term policies and strategies for post-pandemic intelligent transportation systems. © 2022 IEEE.

A Mean Optimization Filter to Improve Bluetooth AoA Indoor Positioning Accuracy for Ship Environments

Currently, the most effective way to reduce transmission of COVID-19 is to differentiate between close contacts. Location points of close contact are essential for differentiation. As a major mode of transportation, ships provide a vehicle for virus transmission. Timely detection location of close contacts inside a ship can prevent the spread of viruses. Location-based services can be provided for ship passengers. Bluetooth is widely available in many wearable devices. The Bluetooth 5.1 angle of arrival (AoA) indoor positioning algorithms can provide a certain indoor positioning accuracy for ship passengers. The two most essential parameters in Bluetooth 5.1 AoA indoor positioning are elevation angle and azimuth angle. Elevation and azimuth are often not accurate enough due to noise, which increases indoor positioning errors. As a result, this paper proposes a mean optimization filter for ship environments, which combines the box plot method to improve Bluetooth 5.1 AoA indoor positioning accuracy, with an RMSE of 0.34 m. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Adoption of a COVID-19 Contact Tracing App Among Older Internet Users in Finland

The outbreak of the COVID-19 pandemic created an unequal need for limiting physical contacts and tracing possible exposures to a novel coronavirus. Smartphone-based contact tracing applications (CTAs) were presented as a vehicle for stopping virus transmission chains and supporting the work of contact tracing teams. In this study, older adults’ adoption of a CTA was studied using socioeconomic background factors, satisfaction with health, and the measure of digital activity as predictors. The data were drawn from a larger questionnaire survey targeted at older internet users. A subsample of older Finnish internet users (N = 723) was analyzed using a logistic regression model. Results showed that older internet users had widely adopted the Finnish CTA called Koronavilkku irrespective of demographic background factors, level of education, and self-assessed satisfaction with health. Besides high income and retirement status, digital activity measured through the breadth of mobile phone features used and the use of an online symptom checker increased the likelihood of having the CTA installed on a smartphone. The results of the study lend themselves to be used for future epidemics and other occasions that require a real-time and/or retrospective tracing of people and their physical encounters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Double Hybrid Tailsitter Unmanned Aerial Vehicle with Vertical Takeoff and Landing

We propose a new concept and architectural design for a double hybrid tailsitter unmanned aerial vehicle with vertical takeoff and landing capability. Basically, it consists of a modified flying wing with a single combustion powertrain set and a multirotor with 2 powertrain sets with electric motors. To this end, we have designed, built, and tested a prototype that spends less energy on vertical taking off and landing and also on horizontal flight, for maximizing flight endurance and distance.With electric propellers fixed at the leading wing edge, the tailsitter has two standard surfaces for elevation control and two vertical stabilizers that are used to give the necessary direction on vertical takeoff and landing. Experiments and results show the versatility of our hybrid tailsitter for operations in a restricted field. We performed several tests starting with the aircraft on the ground in vertical positioning. These tests include executing vertical takeoffs and landing, transitions from vertical to horizontal flight modes and transitions back from horizontal to vertical flight modes, and hovering, which were carried out successfully. Transition fourth and back from combustion to multirotor modes are inherent to some of those flight mode transitions, which have been performed smoothly.We also performed tests (in bench) to estimate the flight endurance. Final autonomous flight adjustments were not performed due to the Covid-19 pandemic caused by SARS-CoV-2. To this end the proposed and currently built prototype has proven to be functional as an effective hybrid UAV system. Author

Modeling the Airborne Transmission of SARS-CoV-2 in Public Transport

This study presents the transmission of SARS-CoV-2 in the main types of public transport vehicles and stations to comparatively assess the relative theoretical risk of infection of travelers. The presented approach benchmarks different measures to reduce potential exposure in public transport and compares the relative risk between different means of transport and situations encountered. Hence, a profound base for the selection of measures by operators, travelers and staff is provided. Zonal modeling is used as the simulation method to estimate the exposure to passengers in the immediate vicinity as well as farther away from the infected person. The level of exposure to passengers depends on parameters such as the duration of stay and travel profile, as well as the ventilation situation and the wearing of different types of masks. The effectiveness of technical and behavioral measures to minimize the infection risk is comparatively evaluated. Putting on FFP2 (N95) masks and refraining from loud speech decreases the inhaled viral load by over 99%. The results show that technical measures, such as filtering the recirculated air, primarily benefit passengers who are a few rows away from the infected person by reducing exposure 84–91%, whereas near-field exposure is only reduced by 30–69%. An exception is exposure in streetcars, which in the near-field is 17% higher due to the reduced air volume caused by the filter. Thus, it can be confirmed that the prevailing measures in public transport protect passengers from a high theoretical infection risk. At stations, the high airflows and the large air volume result in very low exposures (negligible compared to the remaining means of transport) provided that distance between travelers is kept. The comparison of typical means of transport indicates that the inhaled quanta dose depends primarily on the duration of stay in the vehicles and only secondarily on the ventilation of the vehicles. Due to the zonal modeling approach, it can also be shown that the position of infected person relative to the other passengers is decisive in assessing the risk of infection. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.