Series
Master's Projects

Permanent Link
https://hdl.handle.net/1853/71003
Series Type
Publication Series
Description
Associated Organization(s)
Associated Organization(s)

Filters

2020 3
3
3
3
3
Reset filters

Settings
End date: 2022 × Start date: 2020 × search.filters.applied.f.resourcesubtype: Capstone Paper ×

Publication Search Results

Now showing 1 - 3 of 3
Thumbnail Image
Item

A Text-Mining and GIS Approach to Understanding Transit Customer Satisfaction

2020-07-24 , Yap, Soo Huey

Performance evaluation is a concept that most can understand. Examples of performance evaluation include evaluating the performance of students in schools via assignments and exams, and corporations and boards evaluating departmental and corporation-wide performance. In many of these instances, the objectives of performance evaluation are clear. In our first example, the aim of schools may be the education of students, and therefore performance evaluation is conducted to measure students’ understanding and learning of the syllabi. In our second example, the aim of corporations may be to improve efficiency (reduce costs) and increase income. Performance measures used by private sector corporations may include number of sales, customer satisfaction ratings, and number of clicks on advertisements.

View
Thumbnail Image
Item

Covid-19 Vulnerability Index for United States Counties

2020 , Nair, Shruthy

COVID-19 is a highly infective virus with a rapid transmission rate. It has led to a pandemic that has impacted millions of people all around the world. In the United States alone, over 3 million people have being directly affected by COVID-19 as they tested positive and millions more have been affected indirectly due to the virus. The purpose of this study is to determine if a COVID-19 Vulnerability Index can be created using GIS, that would enable one to identify high risk counties within the United States. A Vulnerability Index measures how vulnerable a population or region is to a particular illness. Multiple socio-economic, demographic, transportation and health related factors were utilized in the development of the Vulnerability Index. Principal Component Analysis were applied to analyze the distribution and correlation in the factors and create the index values. The COVID-19 case rates, death rates and the COVID-19 Vulnerability Index values were compared using spatial clustering and then their actual results were compared to see if the Vulnerability Index is a good measure for COVID-19 case rates and death rates. Results indicated that the COVID-19 Vulnerability Index is a good measure to identify counties that are at risk of increasing their case rate, but not death rates. Furthermore, ordinary least squares regression and spatial lag model were run to evaluate the effectivity of the COVID-19 Vulnerability Index in identifying counties with increasing risk of COVID-19 cases. The regression models indicated that the Vulnerability Index is a relatively good measure determining high risk counties.

View
Thumbnail Image
Item

Quantifying Impact of Weather Condition on Travel Time

2020 , Joshi, Sambhavi

Most transportation systems operate at capacity. Minor changes in the system could result in congestion and delays. One of the many impacting factors of transportation is weather condition. Weather conditions might lead to a totally different setting for management of transportation systems. Since weather is predictable, being able to measure the impact of weather conditions on transportation systems would help in better transportation management. Estimating dependency of travel time on weather condition will enable us to predict more accurate travel time. But it is possible that not all components of weather impact travel time equally. There are several other factors associated with travel time that interact with weather conditions to affect travel time. Other questions raising from this are: 1. Which weather component impacts travel time the most? 2. Is the impact of weather on travel time a function of time? The exercise investigates regression models to understand the effect of weather condition, accidents, and time on travel duration. Based on the identified factors parametric and non-parametric classifiers are implemented to provide class-based predictions. Lastly, the machine learning models are the rated based on accuracy, precision, recall, and Cohen Kappa score, and envisioned for various use cases.

View