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BUS 103 – Uber Data Case Study Spring 2022 (30 Points) Student Introd
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BUS 103 – Uber Data Case Study Spring 2022 (30 Points) Student
Introduction & Instructions = For this case study assignment please read the case study. After reading the case study determine the key issues presented in the case study and answer the questions at the end of the case. The purpose of this case study is to identify and analyze the facts. It is your job to use the facts of the case study to answer the questions presented at the end of the case. Remember to only use the facts written in the case study, and not to bring in other information that not written in the case when answering the questions at the end of the case presentation. Since this is a business class, the emphasis of your answers should be including basic business issues and how they help improve company performance. With case study assignments, there is no single correct answer. Each student should complete the case study only using the information presented in the case to support their answers. Each student should complete this assignment by themselves, without help from any person or outside resource. This is not a group assignment so each student should work independently to answer the questions. The college academic integrity guidelines will be enforced. Using outside sources such as Chegg, Coursera, Yahoo Answers, or other similar sites is considered a violation of the school’s academic integrity code. Please number all responses and answer all questions shown at the end of the case. This case study is worth 30 points.
Uber Case Study Presentation
Uber is a ride-sharing service that was rolled out in 2009 after two of its founders Travis Kalanick and Garrett Camp couldn’t get a taxi in Paris during a snowstorm. It originated from the idea “What if you could request a ride from your phone?” Its vision and mission is to “bring transportation-for everyone everywhere…that is safer cheaper and more reliable…create more jobs opportunities and incomes for drivers.” Today, Uber boasts an annual revenue of over $11 billion, a market capitalization rate of $74 billion, and over 19,000 employees. With 75 million global customers and three million dedicated drivers in 83 countries, Uber has been a legitimate game-changer in the ride-sharing services market.
The name Uber is derived from the German word meaning “above all the rest,” a bedrock principle Kalanick and Camp wanted for their young company. All the riders need to do is to open their mobile phone, tap a button, and find an affordable ride in minutes. In direct competition with Google and Tesla Motors, Uber was also a frontrunner in the future of driverless cars.
However, they faced some Intellectual Property (IP) issues when Alphabet Inc’s Waymo sued Uber in 2018 for theft of its self-driving technology. In March 2018, a self-driving car fatally struck a pedestrian, causing the company to temporarily suspend all testing. In May 2018, Uber announced that it would halt its Arizona testing program but would conduct it elsewhere. In July 2018, Uber’s self-driving cars made their return in Pittsburgh, but it was never the same. In December 2020, it was announced that Uber would sell its autonomous vehicle business to Aurora Innovations, a start-up in San Francisco. Uber had invested more than $1 billion in the business at the time of the sale.
The Theory Behind How Autonomous Vehicles Could Save Over 350K Lives in the US and Millions Worldwide
In theory, fatal accidents could drop dramatically in the future as autonomous vehicles become mainstream and make deaths from driver error a thing of the past. American roads are deadly. In 2016, 37,461 people died in traffic accidents in the US, a 5.6 percent increase over 2015, according to the US Department of Transportation (DoT). This is down from 1970, when around 60,000 people died in traffic accidents in the US. The addition of safety features such as seat belts and air bags have reduced the number of deaths, and new technology from autonomous vehicles could help even more as driver error is eliminated.
DoT researchers estimate that fully autonomous vehicles, also known as self-driving cars, could reduce traffic fatalities by up to 94 percent by eliminating those accidents that are due to human error. Using 2016 numbers as a baseline, and multiplying 37,461 by 10, this means that there could be 374,610 deaths in a 10-year span, and 94 percent of these — or 352,133 — could possibly be prevented through fully autonomous cars by eliminating driver error. And globally there were 1.25 million traffic fatalities in 2013, according to the World Health Organization. So, there are millions of lives that could be saved around the world every decade with fully autonomous cars.
In developing countries some accidents are caused by unsafe roads, not driver error, so the 94 percent calculation wouldn’t be applicable, although many lives could still be saved through autonomous vehicles, said Mark Zannoni, analyst at IDC. “I think that most people, most experts, would say that there’s a strong possibility that automated technology can prevent the crashes that are related to human error, and there is a pretty hard number that’s about 94 percent of fatal crashes in the US are attributable, or caused by, human error,” said John Maddox, CEO of the American Center for Mobility.
Elderly drivers and teenagers are particularly likely to benefit from autonomous vehicles because the cars can monitor a situation that a driver might not be able to themselves, said Wayne Powell, vice president of electrical engineering and connected technologies for Toyota Motors North America. “Teen drivers are classically a high-risk category of people. If you put a teen driver in a car that was looking out for that person, it won’t let them make bad choices. That could also have an immediate benefit,” Powell said.
People are optimistic about autonomous technology in cars because it works well in areas where humans tend to not work well. “For example, human error often includes lack of vigilance. They’re distracted for whatever reason, whether texting or eating or talking with kids in the back seat. Or they could be impaired. Or they could be driving in conditions where they have a hard time, like dark night in an urban area with pedestrians, etcetera,” Maddox said.
Ever Vigilant, Always Sober
Cars with automated technology have sensors that never lose vigilance. “They’re always looking for pedestrians. They’re always looking for the edge of the road. They’re always watching the car in front. They don’t become distracted or drunk, and I think that’s really the main reason why most experts would say that there is a definite possibility that automation can significantly reduce those human error caused fatal crashes,” Maddox said. However, there is a learning curve, as drivers in cars with automated technology operate in an environment with drivers who are not in cars with any level of autonomy.
With five levels of autonomy, as defined by the DoT’s National Highway Traffic Safety Administration (NHTSA), there is a range of how much autonomy a driver can choose, with Level 1 providing a specific function, such as steering or accelerating done automatically by the car, and Level 3 where the automated driving system begins to monitor the driving environment. Sometimes drivers might be frustrated with a slower-moving vehicle that is actually an autonomous car, even though the other driver doesn’t know it. And this could result in accidents as frustrated drivers can often act aggressively. Maddox said he’s been in his own vehicle at a Level 2 of automation, and spotted aggressive drivers trying to get around his slower-moving vehicle. “Really, the jury’s still out [on the safety of autonomous vehicles], and what we need is lots of data.
We know a lot about human-caused crashes, because we’ve been studying that for 100 years. We don’t have the same level of data, the same breadth of data, on automated vehicles. Not even close. So, to really be sure on the effects, we need to acquire and analyze lots of data,” Maddox said. “While it will take us years to collect the data that even starts to rival what we have today, the good news is that automated vehicles are data-collecting machines. That’s how they work. They collect data about their environment and other road users. So, if we can correctly and effectively tap into that data, we don’t have to wait 100 years. The data collection and analysis process can go a lot faster because of the data that’s generated on board and off board these vehicles,” he said.
Every vehicle on the road doesn’t need to be autonomous before safety benefits can be realized. Benefits can be realized from earlier levels of automation, said Carrie Morton, deputy director of the Mcity autonomous vehicle test facility at the University of Michigan. “I think that pretty much for every mistake that a human makes there’s an opportunity for automation and artificial intelligence to replace that flawed behavior with a safe behavior,” Morton said. Some of the types of accidents that can be potentially avoided in an autonomous vehicle include front-to-rear crashes, with real-world testing showing a 40 percent decline, said Susan Beardslee, senior analyst for ABI Research.
The infrastructure of a city will change to accommodate autonomous vehicles. The first is providing electric vehicle (EV) stations, since many vehicles will be electric because EVs have a lower total cost of operation, said Paul Stith, director of strategy and innovation for transformative technologies at Black & Veatch, which outlines some of the strategies in its 2018 report on smart cities and utilities. Cities will need to prepare with infrastructure investments for EV charging stations and ensuring there is an adequate communications structure in place to collect the data from the autonomous vehicles on the road.
“There will be terabytes of data that each vehicle will need to convey,” Stith said. One thing to keep in mind is that in the beginning, there will still be accidents caused by autonomous vehicles. “Aviation is extremely safe. But in the early years of aviation, there were more crashes as well. There were more in the beginning with traditional cars. Anything new, whether FDA drugs or new surgical procedures, get safer as they get better and better. But when a new product comes out initially, it might break down. But eventually it can get better,” Zannoni said.
Uber Data Case Study Questions (30 Points Total)
Q1) Please list the names of two (2) “data” sources written in this Uber case study. Note, the question ask’s students’ names of “data” sources not commentary, opinion, analysis, or information. (5 Points)
Q2) After reading the case study, please answer all three (3) “data” questions below. (15 Points)
Q2a) Please list three (3) different types of “data” or “facts” used or written about in this Uber case study. (5 Points)
Q2b) Please list the single most common kind of data, data variable, characteristic, or most frequently mentioned data measurement that has been gathered, organized and presented in a meaningful format in this case study to support autonomous vehicle or self-driving car development? Yes, only one word or a couple of words are needed to answer this question. (5 Points)
Q2c) The choice of variables for this autonomous vehicle or self-driving car analysis is important. It is also important to read and recognize what important data is not included in a presentation. Please list one or two basic “data variables” for the United States “not presented” in the case study that could be important when comparing car accident risk prediction for autonomous vehicle or self-driving cars with human-controlled driver cars. (5 Points)
Q3) After reading the article please answer both questions below (Q3a and Q3b). (5/5 Points)
Q3a) Please answer directly “yes” or “no” to the following question: Is there any indication written in the case study that shows doubt about the safety of autonomous vehicles relative to human driver-controlled vehicles? (2/2 Points)
Q3b) Please support your direct “yes” or “no” answer with a quote or words written in the case study that shows doubt about the safety of autonomous vehicles relative to human driver-controlled vehicles? (3/3 Points)
Q4) Based on the case study, please list and describe one specific car accident classification or the single car accident type those autonomous vehicles are theoretically expected to reduce the most? (5/5 Points)
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