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The following results are related to COVID-19. Are you interested to view more results? Visit OpenAIRE - Explore.
39 Research products, page 1 of 4

  • COVID-19
  • Publications
  • 2013-2022
  • Conference object
  • Transport Research

10
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  • Embargo
    Authors: 
    Tsiktsiris, Dimitris; Lalas, Antonios; Minas Dasygenis; Votis, Konstantinos; Tzovaras, Dimitrios;
    Publisher: Springer International Publishing

    The COVID-19 pandemic has created significant restrictions to passenger mobility through public transportation. Several proximity rules have been applied to ensure sufficient distance between passengers and mitigate contamination. In conventional transportation, abiding by the rules can be ensured by the driver of the vehicle. However, this is not obvious in Autonomous Vehicles (AVs) public transportation systems, since there is no driver to monitor these special circumstances. Since, AVs constitute an emerging mobility infrastructure, it is obvious that creating a system that can provide a sense of safety to the passenger, when the driver is absent, is a challenging task. Several studies employ computer vision and deep learning techniques to increase safety in unsupervised environments. In this work, an image-based approach, supported by novel AI algorithms, is proposed as a service to increase the COVID-19 safety rules adherence of the passengers inside an autonomous shuttle. The proposed real-time service, can detect deviations from proximity rules and notify the authorized personnel, while it is possible to be further extended in other application domains, where automated proximity assessment is critical.

  • Publication . Conference object . Preprint . Article . 2021 . Embargo End Date: 01 Jan 2021
    Open Access
    Authors: 
    Myssar Jabbar Hammood Al-Battbootti; Iuliana Marin; Nicolae Goga; Ramona Popa;
    Publisher: arXiv

    The vast network of oil and gas transmission pipelines requires periodic monitoring for maintenance and hazard inspection to avoid equipment failure and potential accidents. The severe COVID-19 pandemic situation forced the companies to shrink the size of their teams. One risk which is faced on-site is represented by the uncontrolled release of flammable oil and gas. Among many inspection methods, the unmanned aerial vehicle system contains flexibility and stability. Unmanned aerial vehicles can transfer data in real-time, while they are doing their monitoring tasks. The current article focuses on unmanned aerial vehicles equipped with optical sensing and artificial intelligence, especially image recognition with deep learning techniques for pipeline surveillance. Unmanned aerial vehicles can be used for regular patrolling duties to identify and capture images and videos of the area of interest. Places that are hard to reach will be accessed faster, cheaper and with less risk. The current paper is based on the idea of capturing video and images of drone-based inspections, which can discover several potential hazardous problems before they become dangerous. Damage can emerge as a weakening of the cladding on the external pipe insulation. There can also be the case when the thickness of piping through external corrosion can occur. The paper describes a survey completed by experts from the oil and gas industry done for finding the functional and non-functional requirements of the proposed system. Comment: 14th International Conference of Education, Research and Innovation (ICERI2021)

  • Closed Access
    Authors: 
    D Prawinsankar; M. Gunasekaran; B. Gopalakrishnan; P. Purusothaman;
    Publisher: IEEE

    Today metro cities in India are facing more problems due the traffic congestion in the roads that connects different neighboring cities. Due to overwilling of population and covid - 19 pandemic that increased the usage private vehicles by the mankind. Many of the existing methods use the sensor to detect the number of vehicles in that location and implied on to the google maps. The model has been proposed to overcome the issue by considering the existing video cameras been installed in different location in the signals and crowed places. The proposed model incorporated in the edge computing through Nvidia Jetson nano for the object detection using Common Objects in Context (COCO) to detect the boundary boxes for the ten categories of the object. The detection Score is obtained by considering probability of the detection boxes identified in the particular location with respect to time. The accuracy level of each object in the frame is detected and filtered based on the threshold value such that the false positive and true negative can be avoided in the Contributory matrix. Based on the results obtained from the model the location wise table that classify the images with respect low medium and High. The proposed model proves that accuracy of predicting the traffic based on three categories as Low, Medium and High are estimated correctly. The results are apprised based on the accuracy of the particular object identified in the image or frame with respect to number of images tested for the accuracy. The mean square errors are assessed based on the number of objects identified on each category in a particular image.

  • Open Access English
    Authors: 
    Sergi Mas-Pujol; Esther Salamí; Enric Pastor;
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Country: Spain

    In recent years, prior to COVID-19, capacity shortfalls in airspace and airports inevitably caused an increase in aircraft delays. Therefore, when it returns to normal conditions, the airspace will exhibit the same capacity limits, even under normal weather conditions. To ensure that air traffic remains safe, reliable, and efficient in adverse weather conditions, planning and coordination activities through a Collaborative Decision Making process are required to deliver the most effective Air Traffic Flow and Capacity Management services to Air Traffic Control and Aircraft Operators. Nowadays, this task is based on air traffic controllers’ experience and historical data. That means that the Flow Manager Positions and the Network Manager operators have to process a huge amount of information, and the detection of future overloads is based on past experiences. Moreover, due to the inherent uncertainty of weather information, a reliable decision support framework is required to handle these situations as efficiently as possible. We propose a Deep Learning model able to extract the relationship between both the historical data and the implemented actions, accurately identifying the intervals of time that must be regulated. The proposed model achieves an accuracy between 80% and 90% across six traffic volumes belonging to both the MUAC and REIMS regions, a recall higher than 85%, and an F1-score higher than 0.8 in all the cases. Furthermore, the confidence-level analysis shows a really high activation when making a prediction. Finally, the SHapley Additive exPlanations method is applied to identify the most relevant input features. This work was funded EUROCONTROL under Ph.D. Research Contract No. 18-220569-C2 and by the Ministry of Economy, Industry, and Competitiveness of Spain under GrantNumber TRA2016-77012-R. Peer Reviewed

  • Closed Access
    Authors: 
    Rainer Koelle; Sam Peeters; Enrico Spinielli;
    Publisher: IEEE

    The COVID-19 pandemic accelerated the use, sharing, and distribution of data on a global basis. Higher levels of transparency were achieved with continual updates of pandemic related information. The air transportation sector – while by definition an information rich industry – is a notable exception. While different organizations offered aggregated data on air traffic developments on national or airport level, complementary data on air traffic movements for further analysis are not available publicly. This creates a deadlock between addressing the societal needs of monitoring how aviation recovers from the COVID-19 pandemic and addresses the aspirational environmental goals. This paper investigates the feasibility of utilizing open data for the operational performance monitoring at airports. The exploratory work focusses on a subset of the indicators proposed under ICAO’s Global Air Navigation Plan used to assess the operational performance in the arrival phase. A novel approach to characterize and assess the arrival flow management and level of traffic synchronization is presented. This will allow to evaluate on-going air traffic recovery and identify operational bottlenecks. The study is performed as a use-case analysis for three major European airports by comparing the observed performance in the months of March and May for the successive years 2019, 2020, and 2021. The results demonstrate the general feasibility and utility of open data for operational performance monitoring. The classical performance measure for the arrival flow are determined based on the open trajectory data. A geospatial-temporal evaluation support the tracking of traffic synchronisation effort. A higher level of transparency therefore available to the interested public, policy decision-makers and strategic planners with direct feedback on the recovery and actual operational performance. The suitability of the traffic synchronization measure and its parameterization requires further validation across a wider set of airports and will be iteratively refined.

  • Closed Access
    Authors: 
    Michael Finke; Rabeb Abdellaoui; Marco-Michael Temme; Matthias Kleinert; Heiko Ehr;
    Publisher: IEEE
    Country: Germany
    Project: EC | GREAT (875154)

    After COVID-19, a full recovery compared to the 2019 situation with a subsequent growth of global air traffic is expected for the next three to six years [1]. Regarding carbon dioxide emissions, Coronavirus lockdown helped the environment to bounce back, but this will be a temporary situation. It is important to continue investigating additional mitigation measurements to achieve long-term environmental benefits, especially after the recovery. At that point, the question of how to reduce aviation's impact on the climate change will certainly arise again, and will re-gain its importance for the world-wide community. Since no fundamental breakthroughs in CO 2 reduction in aviation are expected in the near future, research should focus on several measures to sustainably reduce the environmental impact of aviation. The air traffic management can contribute to an overall reduction of emissions of greenhouse gases by optimizing traffic flows not only towards maximum airspace capacity and maximum efficiency, but also increasingly towards minimum environmental impact. A set of concept elements that were investigated in the frame of the European-Chinese project Greener Air Traffic Operations (GreAT) can already constitute simple and suitable means towards a greener air traffic management. One of these concept elements is the 'Lowest Impact of Deviation' principle: Whenever two flights need to deviate from their most fuel-efficient route, speed or altitude due to de-conflicting, this deviation should be done by the flight with the lowest fuel consumption, and consequently, with the lowest amount of emissions produced with this maneuver. This principle is currently neither reflected in air traffic control regulations, nor in common practices. In the frame of the work presented in this paper, this principle has been further investigated and analyzed with a fast-time simulation, which models a free route airspace environment under ideal conditions. The flights are generated according to a configurable traffic density. De-conflicting is done automatically either by following the standard right of way rules, which also often serve as a guiding principle for air traffic controllers; or by following the 'Lowest Impact of Deviation' principle. Based on EUROCONTROL’s Base of Aircraft Data (BADA), the simulation estimates the fuel consumption for each flight as well as for the whole simulation, and consequently also the CO 2 emissions, as a function of traffic density.This paper gives basic information about the principle itself, which is then further tailored down and applied to a free route airspace environment for en-route traffic. It briefly describes the used fast time simulation and illustrates the obtained results. This paper quantifies the theoretical benefit that can be achieved by applying the mentioned principle in the described way. When knowing the traffic density of real air traffic control sectors, the results can easily and directly be transferred to them.

  • Publication . Conference object . 2021 . Embargo End Date: 01 Jan 2021
    Open Access English
    Authors: 
    Zwick, Felix; Fraedrich, Eva; Axhausen, Kay W.; id_orcid0000-0003-3331-1318;
    Publisher: ETH Zurich
    Country: Switzerland

    The mobility provider MOIA operates Europe’s largest contiguous ride-pooling service in Hamburg, representing a testbed of how shared and digitized transport can help foster the transformation of urban mobility. To determine demand characteristics of ride-pooling through space and time and thus understanding usage and travel patterns, we use statistical analyses and spatial regressions. MOIA trip data from three different time periods are analyzed, a) before the COVID-19 pandemic in 2019, b) during the time of the first lockdown in Germany in spring 2020, and c) after the first lockdown in summer and autumn 2020. We observe a strong positive effect on ride-pooling demand for number of inhabitants, workplaces, gastronomy, airport and rail stops throughout almost all models. In the course of COVID- 19, hospitals attract more demand than previously, and areas with high car ownership rates face reduced ride-pooling demand compared to pre-pandemic times.

  • Open Access
    Authors: 
    Gargi Ghosh;
    Publisher: WIT Press

    Shared mobility has been documented as one of the most common urban transport services and one of the fastest growing service markets of recent times. In India, the shared mobility market was poised to grow at a CAGR of 13.7% in the period 2019–2025 with a fleet size of two million units in 2019. However, the global pandemic has had an extraordinary impact on the shared mobility space. Bangalore is one of the most prominent metropolises of Asia. The city is India’s Silicon Valley, and attracts people from all across the country. Planning for mobility is an important aspect of a growing metropolis, and Bangalore appears to be an excellent area for exploratory research. This study presents the challenges and opportunities presented by the COVID-19 on shared mobility in Bangalore. The World Health Organization (WHO) has recognised the novel coronavirus thereafter called COVID-19, as the greatest pandemic in a century. The global pandemic has impacted human activity in unprecedented ways. Working situations have changed globally with a significant segment of workers moving to remote working situations. Schools, colleges and other educations institutions have fast adapted to the online mode. Leisure trips have taken a back seat. In light of these significant changes in lifestyle several research works in the field of urban transportation highlight the changing travel pattern and preferences of the urban users. The study presents the user perceptions towards shared mobility modes and changed travel patterns in Bangalore in the wake of COVID-19 through a user survey. The study includes a documentation of the government issued COVID related standard operating procedures (SOPs), altered travel patterns, perceived and real barriers to travel and the attitude of users to shared mobility. © 2021 WIT Press

  • Open Access
    Authors: 
    Sorin Eugen Zaharia; Casandra Venera Pietreanu; Adina Petruta Pave; Ruxandra – Elena Boc;
    Publisher: Editura ASE
  • Closed Access
    Authors: 
    Claus Zehner;
    Publisher: IEEE

    Since the onset of the COVID-19 pandemic in early 2020, many countries worldwide implemented a series of social distancing and containment measures as an attempt to limit its spread. Those measures have led to a significant slowing down of economic activities, drastic drops in road and air traffic, and strong reductions of industrial activities in nonessential sectors, which in turn affected atmospheric emissions and air quality worldwide. Concentrations of short-lived pollutants, such as nitrogen dioxide, are indicators of changes in economic slowdowns and are comparable to changes in emissions. Nitrogen oxides are mainly produced by human activity and the combustion of (fossil) fuels, such as road traffic, ships, power plants and other industrial facilities. Nitrogen Dioxide can have a significant impact on human health, both directly and indirectly through the formation of ozone and small particles. The Copernicus Sentinel-5P satellite nitrogen dioxide concentrations measurements have been used to investigate COVID-19 impact on air quality from space. Global maps of Copernicus Sentinel-5P tropospheric Nitrogen Dioxide measurements have been included - together with other Sentinel measurements - into an on-line tool (dashboard) to provide investigations/results about changes to the Earth environment caused by the COVID-19 pandemic to the public: race.esa.int.

Advanced search in Research products
Research products
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
Include:
The following results are related to COVID-19. Are you interested to view more results? Visit OpenAIRE - Explore.
39 Research products, page 1 of 4
  • Embargo
    Authors: 
    Tsiktsiris, Dimitris; Lalas, Antonios; Minas Dasygenis; Votis, Konstantinos; Tzovaras, Dimitrios;
    Publisher: Springer International Publishing

    The COVID-19 pandemic has created significant restrictions to passenger mobility through public transportation. Several proximity rules have been applied to ensure sufficient distance between passengers and mitigate contamination. In conventional transportation, abiding by the rules can be ensured by the driver of the vehicle. However, this is not obvious in Autonomous Vehicles (AVs) public transportation systems, since there is no driver to monitor these special circumstances. Since, AVs constitute an emerging mobility infrastructure, it is obvious that creating a system that can provide a sense of safety to the passenger, when the driver is absent, is a challenging task. Several studies employ computer vision and deep learning techniques to increase safety in unsupervised environments. In this work, an image-based approach, supported by novel AI algorithms, is proposed as a service to increase the COVID-19 safety rules adherence of the passengers inside an autonomous shuttle. The proposed real-time service, can detect deviations from proximity rules and notify the authorized personnel, while it is possible to be further extended in other application domains, where automated proximity assessment is critical.

  • Publication . Conference object . Preprint . Article . 2021 . Embargo End Date: 01 Jan 2021
    Open Access
    Authors: 
    Myssar Jabbar Hammood Al-Battbootti; Iuliana Marin; Nicolae Goga; Ramona Popa;
    Publisher: arXiv

    The vast network of oil and gas transmission pipelines requires periodic monitoring for maintenance and hazard inspection to avoid equipment failure and potential accidents. The severe COVID-19 pandemic situation forced the companies to shrink the size of their teams. One risk which is faced on-site is represented by the uncontrolled release of flammable oil and gas. Among many inspection methods, the unmanned aerial vehicle system contains flexibility and stability. Unmanned aerial vehicles can transfer data in real-time, while they are doing their monitoring tasks. The current article focuses on unmanned aerial vehicles equipped with optical sensing and artificial intelligence, especially image recognition with deep learning techniques for pipeline surveillance. Unmanned aerial vehicles can be used for regular patrolling duties to identify and capture images and videos of the area of interest. Places that are hard to reach will be accessed faster, cheaper and with less risk. The current paper is based on the idea of capturing video and images of drone-based inspections, which can discover several potential hazardous problems before they become dangerous. Damage can emerge as a weakening of the cladding on the external pipe insulation. There can also be the case when the thickness of piping through external corrosion can occur. The paper describes a survey completed by experts from the oil and gas industry done for finding the functional and non-functional requirements of the proposed system. Comment: 14th International Conference of Education, Research and Innovation (ICERI2021)

  • Closed Access
    Authors: 
    D Prawinsankar; M. Gunasekaran; B. Gopalakrishnan; P. Purusothaman;
    Publisher: IEEE

    Today metro cities in India are facing more problems due the traffic congestion in the roads that connects different neighboring cities. Due to overwilling of population and covid - 19 pandemic that increased the usage private vehicles by the mankind. Many of the existing methods use the sensor to detect the number of vehicles in that location and implied on to the google maps. The model has been proposed to overcome the issue by considering the existing video cameras been installed in different location in the signals and crowed places. The proposed model incorporated in the edge computing through Nvidia Jetson nano for the object detection using Common Objects in Context (COCO) to detect the boundary boxes for the ten categories of the object. The detection Score is obtained by considering probability of the detection boxes identified in the particular location with respect to time. The accuracy level of each object in the frame is detected and filtered based on the threshold value such that the false positive and true negative can be avoided in the Contributory matrix. Based on the results obtained from the model the location wise table that classify the images with respect low medium and High. The proposed model proves that accuracy of predicting the traffic based on three categories as Low, Medium and High are estimated correctly. The results are apprised based on the accuracy of the particular object identified in the image or frame with respect to number of images tested for the accuracy. The mean square errors are assessed based on the number of objects identified on each category in a particular image.

  • Open Access English
    Authors: 
    Sergi Mas-Pujol; Esther Salamí; Enric Pastor;
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Country: Spain

    In recent years, prior to COVID-19, capacity shortfalls in airspace and airports inevitably caused an increase in aircraft delays. Therefore, when it returns to normal conditions, the airspace will exhibit the same capacity limits, even under normal weather conditions. To ensure that air traffic remains safe, reliable, and efficient in adverse weather conditions, planning and coordination activities through a Collaborative Decision Making process are required to deliver the most effective Air Traffic Flow and Capacity Management services to Air Traffic Control and Aircraft Operators. Nowadays, this task is based on air traffic controllers’ experience and historical data. That means that the Flow Manager Positions and the Network Manager operators have to process a huge amount of information, and the detection of future overloads is based on past experiences. Moreover, due to the inherent uncertainty of weather information, a reliable decision support framework is required to handle these situations as efficiently as possible. We propose a Deep Learning model able to extract the relationship between both the historical data and the implemented actions, accurately identifying the intervals of time that must be regulated. The proposed model achieves an accuracy between 80% and 90% across six traffic volumes belonging to both the MUAC and REIMS regions, a recall higher than 85%, and an F1-score higher than 0.8 in all the cases. Furthermore, the confidence-level analysis shows a really high activation when making a prediction. Finally, the SHapley Additive exPlanations method is applied to identify the most relevant input features. This work was funded EUROCONTROL under Ph.D. Research Contract No. 18-220569-C2 and by the Ministry of Economy, Industry, and Competitiveness of Spain under GrantNumber TRA2016-77012-R. Peer Reviewed

  • Closed Access
    Authors: 
    Rainer Koelle; Sam Peeters; Enrico Spinielli;
    Publisher: IEEE

    The COVID-19 pandemic accelerated the use, sharing, and distribution of data on a global basis. Higher levels of transparency were achieved with continual updates of pandemic related information. The air transportation sector – while by definition an information rich industry – is a notable exception. While different organizations offered aggregated data on air traffic developments on national or airport level, complementary data on air traffic movements for further analysis are not available publicly. This creates a deadlock between addressing the societal needs of monitoring how aviation recovers from the COVID-19 pandemic and addresses the aspirational environmental goals. This paper investigates the feasibility of utilizing open data for the operational performance monitoring at airports. The exploratory work focusses on a subset of the indicators proposed under ICAO’s Global Air Navigation Plan used to assess the operational performance in the arrival phase. A novel approach to characterize and assess the arrival flow management and level of traffic synchronization is presented. This will allow to evaluate on-going air traffic recovery and identify operational bottlenecks. The study is performed as a use-case analysis for three major European airports by comparing the observed performance in the months of March and May for the successive years 2019, 2020, and 2021. The results demonstrate the general feasibility and utility of open data for operational performance monitoring. The classical performance measure for the arrival flow are determined based on the open trajectory data. A geospatial-temporal evaluation support the tracking of traffic synchronisation effort. A higher level of transparency therefore available to the interested public, policy decision-makers and strategic planners with direct feedback on the recovery and actual operational performance. The suitability of the traffic synchronization measure and its parameterization requires further validation across a wider set of airports and will be iteratively refined.

  • Closed Access
    Authors: 
    Michael Finke; Rabeb Abdellaoui; Marco-Michael Temme; Matthias Kleinert; Heiko Ehr;
    Publisher: IEEE
    Country: Germany
    Project: EC | GREAT (875154)

    After COVID-19, a full recovery compared to the 2019 situation with a subsequent growth of global air traffic is expected for the next three to six years [1]. Regarding carbon dioxide emissions, Coronavirus lockdown helped the environment to bounce back, but this will be a temporary situation. It is important to continue investigating additional mitigation measurements to achieve long-term environmental benefits, especially after the recovery. At that point, the question of how to reduce aviation's impact on the climate change will certainly arise again, and will re-gain its importance for the world-wide community. Since no fundamental breakthroughs in CO 2 reduction in aviation are expected in the near future, research should focus on several measures to sustainably reduce the environmental impact of aviation. The air traffic management can contribute to an overall reduction of emissions of greenhouse gases by optimizing traffic flows not only towards maximum airspace capacity and maximum efficiency, but also increasingly towards minimum environmental impact. A set of concept elements that were investigated in the frame of the European-Chinese project Greener Air Traffic Operations (GreAT) can already constitute simple and suitable means towards a greener air traffic management. One of these concept elements is the 'Lowest Impact of Deviation' principle: Whenever two flights need to deviate from their most fuel-efficient route, speed or altitude due to de-conflicting, this deviation should be done by the flight with the lowest fuel consumption, and consequently, with the lowest amount of emissions produced with this maneuver. This principle is currently neither reflected in air traffic control regulations, nor in common practices. In the frame of the work presented in this paper, this principle has been further investigated and analyzed with a fast-time simulation, which models a free route airspace environment under ideal conditions. The flights are generated according to a configurable traffic density. De-conflicting is done automatically either by following the standard right of way rules, which also often serve as a guiding principle for air traffic controllers; or by following the 'Lowest Impact of Deviation' principle. Based on EUROCONTROL’s Base of Aircraft Data (BADA), the simulation estimates the fuel consumption for each flight as well as for the whole simulation, and consequently also the CO 2 emissions, as a function of traffic density.This paper gives basic information about the principle itself, which is then further tailored down and applied to a free route airspace environment for en-route traffic. It briefly describes the used fast time simulation and illustrates the obtained results. This paper quantifies the theoretical benefit that can be achieved by applying the mentioned principle in the described way. When knowing the traffic density of real air traffic control sectors, the results can easily and directly be transferred to them.

  • Publication . Conference object . 2021 . Embargo End Date: 01 Jan 2021
    Open Access English
    Authors: 
    Zwick, Felix; Fraedrich, Eva; Axhausen, Kay W.; id_orcid0000-0003-3331-1318;
    Publisher: ETH Zurich
    Country: Switzerland

    The mobility provider MOIA operates Europe’s largest contiguous ride-pooling service in Hamburg, representing a testbed of how shared and digitized transport can help foster the transformation of urban mobility. To determine demand characteristics of ride-pooling through space and time and thus understanding usage and travel patterns, we use statistical analyses and spatial regressions. MOIA trip data from three different time periods are analyzed, a) before the COVID-19 pandemic in 2019, b) during the time of the first lockdown in Germany in spring 2020, and c) after the first lockdown in summer and autumn 2020. We observe a strong positive effect on ride-pooling demand for number of inhabitants, workplaces, gastronomy, airport and rail stops throughout almost all models. In the course of COVID- 19, hospitals attract more demand than previously, and areas with high car ownership rates face reduced ride-pooling demand compared to pre-pandemic times.

  • Open Access
    Authors: 
    Gargi Ghosh;
    Publisher: WIT Press

    Shared mobility has been documented as one of the most common urban transport services and one of the fastest growing service markets of recent times. In India, the shared mobility market was poised to grow at a CAGR of 13.7% in the period 2019–2025 with a fleet size of two million units in 2019. However, the global pandemic has had an extraordinary impact on the shared mobility space. Bangalore is one of the most prominent metropolises of Asia. The city is India’s Silicon Valley, and attracts people from all across the country. Planning for mobility is an important aspect of a growing metropolis, and Bangalore appears to be an excellent area for exploratory research. This study presents the challenges and opportunities presented by the COVID-19 on shared mobility in Bangalore. The World Health Organization (WHO) has recognised the novel coronavirus thereafter called COVID-19, as the greatest pandemic in a century. The global pandemic has impacted human activity in unprecedented ways. Working situations have changed globally with a significant segment of workers moving to remote working situations. Schools, colleges and other educations institutions have fast adapted to the online mode. Leisure trips have taken a back seat. In light of these significant changes in lifestyle several research works in the field of urban transportation highlight the changing travel pattern and preferences of the urban users. The study presents the user perceptions towards shared mobility modes and changed travel patterns in Bangalore in the wake of COVID-19 through a user survey. The study includes a documentation of the government issued COVID related standard operating procedures (SOPs), altered travel patterns, perceived and real barriers to travel and the attitude of users to shared mobility. © 2021 WIT Press

  • Open Access
    Authors: 
    Sorin Eugen Zaharia; Casandra Venera Pietreanu; Adina Petruta Pave; Ruxandra – Elena Boc;
    Publisher: Editura ASE
  • Closed Access
    Authors: 
    Claus Zehner;
    Publisher: IEEE

    Since the onset of the COVID-19 pandemic in early 2020, many countries worldwide implemented a series of social distancing and containment measures as an attempt to limit its spread. Those measures have led to a significant slowing down of economic activities, drastic drops in road and air traffic, and strong reductions of industrial activities in nonessential sectors, which in turn affected atmospheric emissions and air quality worldwide. Concentrations of short-lived pollutants, such as nitrogen dioxide, are indicators of changes in economic slowdowns and are comparable to changes in emissions. Nitrogen oxides are mainly produced by human activity and the combustion of (fossil) fuels, such as road traffic, ships, power plants and other industrial facilities. Nitrogen Dioxide can have a significant impact on human health, both directly and indirectly through the formation of ozone and small particles. The Copernicus Sentinel-5P satellite nitrogen dioxide concentrations measurements have been used to investigate COVID-19 impact on air quality from space. Global maps of Copernicus Sentinel-5P tropospheric Nitrogen Dioxide measurements have been included - together with other Sentinel measurements - into an on-line tool (dashboard) to provide investigations/results about changes to the Earth environment caused by the COVID-19 pandemic to the public: race.esa.int.