The research paper proposes an algorithm to find congruence criteria between two convex polygons in Euclidean Geometry. It begins with a review of triangles, then extends to quadrilaterals and eventually gener- alizes the case to n-sided polygons. It attempts to prove said algorithm using a method of induction and a case-by-case analysis. It also states a corollary to said algorithm
<p>The COVID-19 pandemic has led to an unprecedented change in transportation, including shared mobility services. This study attempted to identify the user group of ride-share services by leveraging daily ride-sharing trip data for the year of 2020 associated with other socio-demographic and built-environment attributes of Chicago, Illinois. The study employed K-means clustering for user group segmentation. Results show: i) the cluster with the largest share of census tracts generate lowest average trips which is clearly an impact of the pandemic; ii) The high-income cluster generates short trip and coupled with high population, land-use, and employment density; iii) The low-income cluster generates longer trips coupled with diversity of land-use mx, employment and population density. Results of this study provide insights for policymakers and ride- sharing operators to ensure access to the services among the population irrespective of spatial diversity.</p>
<p>The COVID-19 pandemic has led to an unprecedented change in transportation, including shared mobility services. This study attempted to identify the user group of ride-share services by leveraging daily ride-sharing trip data for the year of 2020 associated with other socio-demographic and built-environment attributes of Chicago, Illinois. The study employed K-means clustering for user group segmentation. Results show: i) the cluster with the largest share of census tracts generate lowest average trips which is clearly an impact of the pandemic; ii) The high-income cluster generates short trip and coupled with high population, land-use, and employment density; iii) The low-income cluster generates longer trips coupled with diversity of land-use mx, employment and population density. Results of this study provide insights for policymakers and ride- sharing operators to ensure access to the services among the population irrespective of spatial diversity.</p>
<p>The COVID-19 pandemic has led to an unprecedented change in transportation, including shared mobility services. This study attempted to identify the user group of ride-share services by leveraging daily ride-sharing trip data for the year of 2020 associated with other socio-demographic and built-environment attributes of Chicago, Illinois. The study employed K-means clustering for user group segmentation. Results show: i) the cluster with the largest share of census tracts generate lowest average trips which is clearly an impact of the pandemic; ii) The high-income cluster generates short trip and coupled with high population, land-use, and employment density; iii) The low-income cluster generates longer trips coupled with diversity of land-use mx, employment and population density. Results of this study provide insights for policymakers and ride- sharing operators to ensure access to the services among the population irrespective of spatial diversity.</p>
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