As pollution and population continue to increase, new concepts of eco-friendly commuting evolve. One of the emerging concepts is the bicycle sharing system. It is a bike rental service on a short-term basis at a moderate price. It provides people the flexibility to rent a bike from one location and return it to another location. This business is quickly gaining popularity all over the globe. In May 2011, there were only 375 bike rental schemes consisting of nearly 236,000 bikes. However, this number jumped to 535 bike sharing programs with approximately 517,000 bikes in just a couple of years. It is expected that this trend will continue to grow at a similar pace in the future. Most of the businesses involved in this system of bike rental are faced with the challenge of balancing supply and inconsistent demand. The number of bikes needed on a particular day can vary on several factors such as season, time, temperature, wind speed, humidity, holiday and day of the week. In this paper, we have tried to solve this problem using SAS^® Forecast Studio. Incorporating the effects of all the above factors and analyzing the demand trends of the last two years, we have been able to precisely forecast the number of bikes needed on any day in the future. Also, we are able to do the scenario analysis to observe the effect of particular variables on the demand.

A leading killer in the United States is smoking. Moreover, over 8.6 million Americans live with a serious illness caused by smoking or second-hand smoking. Despite this, over 46.6 million U.S. adults smoke tobacco, cigars, and pipes. The key analytic question in this paper is, How would e-cigarettes affect this public health situation? Can monitoring public opinions of e-cigarettes using SAS^® Text Analytics and SAS^® Visual Analytics help provide insight into the potential dangers of these new products? Are e-cigarettes an example of Big Tobacco up to its old tricks or, in fact, a cessation product? The research in this paper was conducted on thousands of tweets from April to August 2014. It includes API sources beyond Twitter--for example, indicators from the Health Indicators Warehouse (HIW) of the Centers for Disease Control and Prevention (CDC)--that were used to enrich Twitter data in order to implement a surveillance system developed by SAS^® for the CDC. The analysis is especially important to The Office of Smoking and Health (OSH) at the CDC, which is responsible for tobacco control initiatives that help states to promote cessation and prevent initiation in young people. To help the CDC succeed with these initiatives, the surveillance system also: 1) automates the acquisition of data, especially tweets; and 2) applies text analytics to categorize these tweets using a taxonomy that provides the CDC with insights into a variety of relevant subjects. Twitter text data can help the CDC look at the public response to the use of e-cigarettes, and examine general discussions regarding smoking and public health, and potential controversies (involving tobacco exposure to children, increasing government regulations, and so on). SAS^® Content Categorization helps health care analysts review large volumes of unstructured data by categorizing tweets in order to monitor and follow what people are saying and why they are saying it. Ultimatel y, it is a solution intended to help the CDC monitor the public's perception of the dangers of smoking and e-cigarettes, in addition, it can identify areas where OSH can focus its attention in order to fulfill its mission and track the success of CDC health initiatives.