Assignment 1 [Updated 28 October 2017]

Assignment 1 [Updated 28 October 2017]

  October 28, 2017

Source: TECH NEW TODAY

The article entitled “How Uber Engineering Increases Safe Driving with Telematics” by Beinstein and Sumers (2016) provides insight into Uber’s utilization of telematics data to improve road safety and eradicate unsafe driving habits. Under the company’s mobile architecture, Global Positioning System (GPS) information from each trip is processed under Trip Services. This is combined with the information gathered via Uber’s Vehicle Movement Processor (VMP), which analyses key indicators including harsh braking and sudden acceleration, which will complement and enhance the credibility of the telematics data obtained from the drivers. Combining the information from Uber’s VMP, the resultant data will subsequently be processed under Apache’s distributed streaming platform, Kafka, before they are committed to long-term storage under the HADOOP Distributed File System (HDFS). Beinstein and Sumers (2016) also highlighted Uber’s use of various computational softwares, including Apache Hive and Apache Spark, to compute and derive telematics statistics such as “daily city-level averages for hard brakes.” After the data is being analysed, it will be stored under the Elasticsearch cluster and made available via an Application Programming Interface (API) for developers.

The emergence of rail-hailing app Uber has revolutionized and transformed the ride-hailing industry, providing commuters with a safer alternative to the traditional taxis. This is supported by Uber’s embracement and effective deployment of innovative technology which ushered in fundamental changes to the traditional transportation and ride-hailing industry, “adding to the safety of the service far more than any government regulation could” (Shaffer, 2017).

In contrast to traditional taxi companies, Uber has employed numerous key safety features  which includes collecting and analyzing of sentiment and vehicle telematics data and utilizing of sensors on drivers’ devices to detect and predict drivers’ behaviours (Kashyap, 2017), (can be reshaped into more effective thesis)

In fact, Uber has also entered into partnership with various non-profit organizations such as the Governor’s Highway Safety Association and MADD (Sheehey-Church, 2016) to introduce numerous safety pilots to its consumers. Some examples include personalized travel reports for drivers, speed displays and other safety reminders pertaining to the drivers’ use of the app.

In addition, to sift out drivers who portray dangerous or aggressive driving practices on the roads, a bi-directional rating framework (known as the Uber Star Rating) is adopted. As mentioned by Isaac (2014), both drivers and riders will be prompted to provide ratings to each other after each trip. Under this rating system, drivers who received a collective rating of less than 4.6 out of 5.0, will be notified to be at risk of being “deactivated”; which encourages drivers to consistently improve and correct bad driving habits.

In conclusion, Uber’s utilization of telematics data and innovative technology, such as speed tracking via GPS technology and the use of a rating system, illustrates Uber’s long-term commitment to promote safe driving through leveraging of telematics technologies.


References (APA):

Beinstein, A., & Sumers, T. (2017). How Uber Engineering Increases Safe Driving with Telematics. Uber Engineering Blog. Retrieved October 02, 2017, from https://eng.uber.com/telematics/

Isaac, E. (2014). Disruptive Innovation: Risk-Shifting and Precarity in the Age of Uber. Retrieved October 01, 2017, from https://pdfs.semanticscholar.org/0d90/07be68160ee0c27e2abb5e10f92a42075e66.pdf.

Kashyap, S. (2017), How Uber is harnessing technology to ensure safe rides. Retrieved October 07, 2017, from https://yourstory.com/2017/09/uber-harnessing-technology-ensure-safe-rides

Shaffer, S. (2017), Uber, Lyft are safer than cabs. Retrieved October 07, 2017, from http://www.baltimoresun.com/news/opinion/readersrespond/bs-ed-uber-lyft-letter-20170103-story.html

Sheehey-Church, C. (2016). New App Features and Data Show How Uber Can Improve Safety on the Road. Retrieved October 12, 2017, from https://www.uber.com/newsroom/safety-on-the-road-july-2016

Assignment 1

Assignment 1

  October 12, 2017

Source: TECH NEW TODAY

The article entitled “How Uber Engineering Increases Safe Driving With Telematics” by Beinstein and Sumers (2016) provides insight on Uber’s utilization of telematic data to improve road safety and eradicate unsafe driving habits. Under the company’s mobile architecture, Global Positioning System (GPS) information from each trip is processed under Trip Services. This is combined with the information gathered via Uber’s Vehicle Movement Processor, which analyses key indicators including harsh braking and sudden acceleration, which will complement and enhance the credibility of the telematics data obtained from the drivers. Combining the information from Uber’s Trip Services Vehicle Movement Processor, the resultant data will then be processed under Apache’s distributed streaming platform, Kafka, before they are committed to long-term storage under the HADOOP Distributed File System (HDFS). Beinstein and Sumers (2016) also highlighted Uber’s use of various computational software, including Apache Hive and Apache Spark, to compute and derive telematic statistics such as “daily city-level averages for hard brakes.” After being analysed, these data will be indexed under the Elasticsearch cluster and made available via an Application Programming Interface (API) for developers.

Uber’s embracement and effective deployment of innovative technology has ushered in fundamental changes to the traditional transportation and ride-hailing industry and “adds to the safety of the service far more than any government regulation could” (Shaffer, 2017). In contrast to traditional taxi companies in Singapore, Uber has employed numerous key safety features including analysing of sentiment data, collecting of vehicle telematics data, and utilizing of sensors on drivers’ devices to detect and predict behaviours of drivers (Kashyap, 2017).

In fact, Uber has also entered into partnership with various non-profit organizations such as the Governor’s Highway Safety Association and MADD (Sheehey-Church, 2016) to introduce numerous safety pilots to its consumers. Some examples include personalized travel reports for drivers, speed displays and other safety reminders pertaining to the drivers’ use of the app.


In addition, to sleeve out drivers who continuously demonstrate poor driving skills or exhibits poor driving habits, a bi-directional rating framework (known as the Uber Star Rating) is adopted. As mentioned by Isaac (2014), both drivers and riders will be prompted to provide ratings to each other after each trip. Under this rating system, driver who received a collective rating of less than 4.6 out of 5.0 will be notified to be at risk of being “deactivated”; which encourages drivers to consistently improve and correct bad driving habits.

In conclusion, the emergence and evolution of Uber has revolutionized the ride-hailing industry while providing commuters with a safer alternative to the traditional taxis. In addition, Uber’s utilization of telematics data and innovative technology, such as speed tracking via GPS technology and the use of a rating system, further illustrates Uber’s long-term commitment to promote safe driving through leveraging of telematics technologies


References (APA):

Beinstein, A., & Sumers, T. (2017). How Uber Engineering Increases Safe Driving with Telematics. Uber Engineering Blog. Retrieved October 02, 2017, from https://eng.uber.com/telematics/

Isaac, E. (2014). Disruptive Innovation: Risk-Shifting and Precarity in the Age of Uber. Retrieved October 01, 2017, from https://pdfs.semanticscholar.org/0d90/07be68160ee0c27e2abb5e10f92a42075e66.pdf.

Kashyap, S. (2017), How Uber is harnessing technology to ensure safe rides. Retrieved October 07, 2017, from https://yourstory.com/2017/09/uber-harnessing-technology-ensure-safe-rides

Shaffer, S. (2017), Uber, Lyft are safer than cabs. Retrieved October 07, 2017, from http://www.baltimoresun.com/news/opinion/readersrespond/bs-ed-uber-lyft-letter-20170103-story.html

Sheehey-Church, C. (2016). New App Features and Data Show How Uber Can Improve Safety on the Road. Retrieved October 12, 2017, from https://www.uber.com/newsroom/safety-on-the-road-july-2016

Summary Report - Draft 2

Summary Report - Draft 2

  October 05, 2017

Introduction to Uber’s mobile architecture

The article entitled “How Uber Engineering Increases Safe Driving With Telematics” by Beinstein and Sumers (2016) attempts to give insight on Uber’s utilization of telematic data to improve road safety and eradicate unsafe driving habits. Under the company’s mobile architecture, Global Positioning System (GPS) information from each trip is processed under Trip Services; while information gathered via Uber’s Vehicle Movement Processor, which analyses key indicators including harsh braking and sudden acceleration, will complement and enhance the credibility of the telematics data obtained from the drivers. Combining these two data sources, the resultant data will then be processed under Apache’s distributed streaming platform, Kafka, before they are committed to long-term storage under the HADOOP Distributed File System (HDFS).

Beinstein and Sumers (2016) also highlighted Uber’s utilization of various computational software, including Apache Hive and Apache Spark, to compute and derive telematic statistics such as “daily city-level averages for hard brakes”. And upon completion of the analyses, these data will be indexed under the Elasticsearch cluster and made available via an Application Programming Interface (API) for developers.

Advantages for the adoption of its current architecture

The company’s current architecture, which enables them to continuously support an adaptable, yet fault-tolerant data-distribution system (Beinstein and Sumers, 2016), is a prime example of Uber’s commitment towards achieving high safety standards through innovation and engineering. Plus, with its architecture being horizontally-scaled, system performance for every component can be optimized based on the on-going demands for its services; all these can be achieved without compromising its service quality.

Product Analysis

According to Abrosimova (2014), the advancement in mobile networks and near-ubiquitous smartphone use has enabled Uber to enhance its user experience. Leveraging on the built-in CoreLocation framework and Google’s Location APIs in iOS and Android devices respectively, Uber is able to detect the locations of their app users accurately with high precision. Using Apple’s Mapkit and Google Maps Android API, point-to-point directions can be derived instantaneously, providing users with a smooth in-app experience to the users. In fact, geolocation technology is a fundamental pillar in Uber's innovation stack as evident by Uber’s acquisition of highly-sophisticated mapping technology organizations from Microsoft (Popper, 2015) to better enhance and rectify any coordinates issues. This illustrates Uber’s goal to become a leader of "neighbourhood coordination and conveyance of individuals and things."

A better alternative to conventional taxies?

Uber is becoming more popular and is a substitute of taxi. In fact, Uber is a better alternative to taxi. Taxi being more expensive and also becoming undependable has make Uber a better choice. One key reason is the unique features that is rating system for Uber drivers. As mentioned in the paper by Issac (2014),  Uber utilizes a bidirectional rating framework to direct the market and flush out poor drivers. After a trip is done, the passenger and the driver rate each other out of five stars. Hence, Uber drivers are under a huge stress to convey a satisfying, protected, immediate, and clean encounter for the passengers.This is accomplished through a client rating of at least 4.6 out of 5.0.

References:

Abrosimova, K. (2014). Uber Underlying Technologies and How It Actually Works. Retrieved October 01, 2017, from https://medium.com/yalantis-mobile/uber-underlying-technologies-and-how-it-actually-works-526f55b37c6f

Popper, B. (2015). Uber acquires mapping tech and talent from Microsoft as it prepares to take on Google. Retrieved October 02, 2017, from https://www.theverge.com/2015/6/29/8863687/uber-acquires-mapping-data-tech-and-talent-from-microsoft-bing

Gil, P. (n.d.). How Uber Works - A Helpful Primer on the Ride-Hailing Service. Retrieved from https://www.lifewire.com/how-does-uber-work-3862752

Isaac, E. (2014). Disruptive Innovation: Risk-Shifting and Precarity in the Age of Uber. Retrieved October 01, 2017, from https://pdfs.semanticscholar.org/0d90/07be68160ee0c27e2abb5e10f92a42075e66.pdf.

Beinstein, A. & Sumers, T. (2017). How Uber Engineering Increases Safe Driving with Telematics. [online] Uber Engineering Blog. Available at: https://eng.uber.com/telematics/ [Accessed 29 Sep. 2017].

Summary Draft 1 - Week 4

Summary Draft 1 - Week 4

  September 28, 2017

The article entitled “How Uber Engineering Increases Safe Driving With Telematics” by Beinstein and Sumers (2016) attempts to give insight on Uber’s utilization of telematic data to improve road safety and eradicate unsafe driving habits. Under the company’s mobile architecture, Global Positioning System (GPS) information from each trip is processed under Trip Services; while information gathered via Uber’s Vehicle Movement Processor, which analyses key indicators including harsh braking and sudden acceleration, will be utilized to complement and enhance the credibility of the telematics data obtained from the drivers.

Combining the two data sources, the resultant data will then be processed under Apache’s distributed streaming platform, Kafka, before they are committed to long-term storage under the HADOOP Distributed File System (HDFS). Using programs like Hive and Spark, batch data analyses are conducted to compute and derive statistics such as the ‘daily city-level averages for hard brakes’. Upon completion of the analyses, these data will be indexed under Elasticsearch cluster and made available via an Application programming interface (API) for developers.


References:
BEINSTEIN, A. and SUMERS, T. (2016). How Uber Engineering Increases Safe Driving with Telematics. [online] Uber Engineering Blog. Available at: https://eng.uber.com/telematics/ [Accessed 22 Sep. 2017].

Formal Introduction Email

Formal Introduction Email

  September 21, 2017

Dear Mr. Brad Blackstone,

My name is Keith Chong. I am an undergraduate student at the Singapore Institute of Technology (SIT) studying towards my bachelor’s degree in telematics. I graduated in the year 2014 with a diploma in information technology at Nanyang Polytechnic (NYP), specializing in social media analytics. While pursuing my diploma in NYP, I was given the opportunity (during one of my computer lab sessions for the module to explore emerging technologies in the ICT field) to explore upcoming technological trends in the field of ICT.

My curiosity was piqued when articles related to autonomous vehicle technology surfaced, which kept my project team members captivated till the end of our lab session. Fascinated about this subject, my team and I chose this topic as our topic of choice for our final presentation. The time spent researching this field cemented our understanding on this subject whilst providing us better appreciation of both the ICT and automotive industry. This has consequently fueled my interest to pursue higher education in autonomous technology and affirmed my decision to pursue a degree in the fields of telematics and be part of the SIT family.

Pertaining to my communications skills, I have found report writing to be the lesser of two evils when compared to my presentation skills. I admit that I have many areas for improvement, most notably on areas involving public speaking, and I hope to cultivate and strengthen my soft skills in communication while reinforcing my technical report writing skills during our classes.

Being able to learn your guidance will be a tremendous learning experience for me. I would like to take this opportunity to thank you for your time and I look forward to our next technical communications lesson.

Yours sincerely,

Keith Chong

TLM1010 Tutorial Group 3

Initial Draft: 21 September 2017
First Revision: 05 October 2017