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COVID-19 TravelCover: Post-Lockdown Smart Transportation Management System
Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis ; : 19-43, 2021.
Article in English | Scopus | ID: covidwho-2325723
ABSTRACT
Public transportation is not safe during the COVID-19 pandemic even after post lockdown, because it will be very hard to maintain social distancing in public transport. The COVID-19 pandemic is a worldwide pandemic because of which it became risky to allow public transportation without the proper mechanism to maintain social distancing. So to resolve this problem we came up with an idea of making an intelligent application to schedule the timings of transportation, avoiding over occupancy of public transport, providing them the shortest route to reach their desired destination, providing them proper guidelines, also providing them the information of the nearest hospitals for any emergency. In the proposed work, we have tested the result on 100 random users from various locations, determined the shortest distance of the vehicles, booked online tickets based on mask detection, and maintained the social distancing based on government guidelines. We have applied only 50% ticket booking for maintaining the social distancing and the ticket validation system has been checked with the user's images and it was successfully able to distinguish between masked and unmasked images. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study / Randomized controlled trials Language: English Journal: Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study / Randomized controlled trials Language: English Journal: Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis Year: 2021 Document Type: Article