ABSTRACT
Equity is becoming a higher priority for transportation agencies and organizations, particularly as the COVID-19 pandemic has highlighted gaps in access and as racial injustice has inspired new commitments to antiracist practices. Still, while questions of diversity, access, and bias have come into focus within our industry and have even, at times, led to more equitable public engagement during the planning stages of a project, centering equity should not stop there. Here, Thompson et al encourage transportation practitioners to remember that our work does not have an unspecified end--it impacts actual people whose lived experiences matter and who should be treated equitably. © 2022, Institute of Transportation Engineers. All rights reserved.
ABSTRACT
Equity is becoming a higher priority for transportation agencies and organizations, particularly as the COVID-19 pandemic has highlighted gaps in access and as racial injustice has inspired new commitments to antiracist practices. Still, while questions of diversity, access, and bias have come into focus within our industry and have even, at times, led to more equitable public engagement during the planning stages of a project, centering equity should not stop there. Here, Thompson et al encourage transportation practitioners to remember that our work does not have an unspecified end--it impacts actual people whose lived experiences matter and who should be treated equitably. © 2022, Institute of Transportation Engineers. All rights reserved.
ABSTRACT
Travel is important for every human being and it impacts in all aspects of life ranging from personal to societal development. COVID'19 pandemic has changed the way we think to travel. Exploring the impact of the Covid-19 pandemic in the place the user needs to travel thereby facilitating user perception on travel is becoming a mandate nowadays. Travel perception is also important for variety of day-to-day activities like transportation of goods and services, health related travel etc., This work aims to create comprehensive and efficient prediction models, facilitated by a convenient user interface to predict how risky or convenient it is for a user to travel in a time where COVID-19 is prevalent. The predictions made are based on the location they wish to travel using various Machine Learning models. The results are combined with the individual's health history to arrive at an optimized decision. The model is trained using a comorbidities dataset as well as a location- wise weather dataset, which allows us to make the prediction of whether travelling is dangerous for the user or not. The user interface is designed to show predictions for various districts. The trained model is tested using the data provided in social media and government websites provided for prediction © 2022 IEEE.