Introduction: Cycling is on the rise in many urban areas across the United States. The number of cyclist fatalities is also increasing, by 19% in the last 3 years. With the broad-ranging personal and public health benefits of cycling, it is important to understand factors that are associated with these traffic-related deaths. There are more distracted drivers on the road than ever before, but the question remains of the extent that these drivers are affecting cycling fatality rates. Methods: This paper uses the Fatality Analysis Reporting System (FARS) data to examine factors related to cyclist death when the drivers are distracted. We use a novel machine learning approach, adaptive LASSO, to determine the relevant features and estimate their effect. Results: If a cyclist makes an improper action at or just before the time of the crash, the likelihood of the driver of the vehicle being distracted decreases. At the same time, if the driver is speeding or has failed to obey a traffic sign and fatally hits a cyclist, the likelihood of them also being distracted increases. Being distracted is related to other risky driving practices when cyclists are fatally injured. Environmental factors such as weather and road condition did not impact the likelihood that a driver was distracted when a cyclist fatality occurred.

SAS^® Embedded Process offers a flexible, efficient way to leverage increasing amounts of data by injecting the processing power of SAS^® directly where the data lives. SAS Embedded Process can tap into the massively parallel processing (MPP) architecture of Hadoop for scalable performance. Using SAS^® In-Database Technologies for Hadoop, you can run scoring models generated by SAS^® Enterprise Miner™ or, with SAS^® In-Database Code Accelerator for Hadoop, user-written DS2 programs in parallel. With SAS Embedded Process on Hadoop you can also perform data quality operations, and extract and transform data using SAS^® Data Loader. This paper explores key SAS technologies that run inside the Hadoop parallel processing framework and prepares you to get started with them.

Companies looking for an optimal solution to run their SAS^® Analytics need a seamless way to manage their data between many different systems, including commodity Hadoop storage and the more traditional data warehouse. This presentation outlines a simple path for building a single platform that integrates SAS^®, Hadoop, and the data warehouse into a single, pre-configured solution, as well as strategies for querying data within multiple existing systems and combing the results to produce even more powerful decision-making possibilities.

Read the paper (PDF) | View the e-poster or slides (PDF)