Demystifying Transportation Using Big Data Analytics

摘要

With the ever-growing generation and collection of data, there are ample opportunities to extract useful information from big data. The transportation industry, particularly the taxi companies, are a significant contributor to this data age. This research analyzes a 2016 voluminous taxi dataset from the City of Chicago to find impactful transportation trends for determining city hotspots based on time and location. Customer satisfaction was used as a way of deciding which taxi companies need to look at improving their customer service. Linear regression models were used to estimate tips relative to the distance traveled and the time taken. The haversine distance was utilized to pair the latitude and longitude coordinates of drop-offs and their next pickup. To maximize the driver's earnings, information on tips, and to analyze the average range to drivers next fare were combined. Stakeholders, customers, and transportation authorities can use the results of this analysis to plan better commute patterns.