Work-in-Progress: Real-Time Vehicular Traffic-Based Crowd Density Estimation for Reducing Epidemiological Risks

Many applications have been released for predicting the spread of the COVID-19 pandemic in different areas, which helps many countries control the spread of COVID-19 and other contagious diseases. The RT-CIRAM is a mobile phone-deployable application, which analyzes up-to-date data from multiple ope...

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Bibliographic Details
Main Authors: Alhazmi, Somaia, Lowy, Seren, Kim Cheng, Albert Mo
Format: Conference Proceeding
Language:English
Subjects:
COVID-19
Diseases
Privacy
Processor scheduling
Real-time systems
Roads
Routing
Social factors
Time factors
Turning
Online Access:Request full text
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Summary:Many applications have been released for predicting the spread of the COVID-19 pandemic in different areas, which helps many countries control the spread of COVID-19 and other contagious diseases. The RT-CIRAM is a mobile phone-deployable application, which analyzes up-to-date data from multiple open sources with the help of HPC/cloud computing and time-critical scheduling and routing techniques. This app aims to help users practice social distancing and reduce infection risks by advising about crowded areas in their environment. An important layer of data in the operation of this app is the density of people currently occupying different areas a user might travel through. However, this information is not directly available, since it depends on knowing the precise location of persons, for which individuals can choose to deny permission by turning off location services to protect their privacy. Our project implements a new approach to extrapolate from indirect information such as traffic speed on roads to derive the crowd density at each location to be visited. This approach can be useful for reducing infection risks from COVID-19 and other contagious diseases.
ISSN:2771-571X
DOI:10.1109/EMSOFT60242.2024.00009