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NM3221 Project 3 Draft Proposal

Sunday, March 8 2020

Andrew B. Hennessy

Draft Proposal

Outline: Intro, Description of Dataset, Interview Summary, Justification of Dataset and Target Audience, Functionality of the proposed app

Intro

My application is a tool to help travelers maximize their time exploring their destinations while also ensuring they arrive at their port of travel on time. The audience of the app are travelers on boats, airplanes, busses, ferries, trains etc. The main functionality of the app is to provide users the latest time, given some variable amount of risk, that they should leave their current location to make their flight, ferry, train or bus trip. The personal goal of the application creator was to create a solution that would allow him to enjoy more of the destination rather than wasting time in transit. The data used to support the construction of this app consists of airline data collected by the US Department of Transportation. The data consists of the difference between posted and actual departure times of various airlines. In addition to this data the application presumes it also has access to the individual users location relative to their port of travel, their itinerary details, and posted data from the aforementioned ports regarding security and immigration wait times. Furthermore, the app will rely on crowdsourced and app published research on the details and intricacies of each port of travel. Finally, the app service will collect historical data to better optimize the user experience.

Summary of Collected Data.

Below are the various sources of data utilized to conduct the initial steps of the product proposal. They consist of government data as well examples of other types of data required to create a service that meets the functional requirements of the app. The data prints a bleak picture of the performance of airlines across the US following their posted departure times. It is evident that an air traveller will meet some sort of delay at the beginning of their journey. This is what the application seeks to exploit. More specifically, give travellers a better idea of how to invest their time in the purpose of their travel rather than wait idle during various delays.

  1. The Impact of Airline Flight Schedules on Flight Delays
  1. This dataset and paper shows data on airlines historical on time performance vs posted departure times. This is a special data point because it factors in boarding times which can often still be ongoing after the flight has been advertised as departing.

  1. MyTSA API Documentation
  1. The aforementioned API documentation published by the US Department of Homeland Security Transportation Security Administration gives developers access to Security Checkpoint wait times across the country.
  1. US Airport Rankings & On Time Flight Statistics [2020 Update]
  1. The aforementioned dataset and summary collates and presents various data points on airport efficiency and airline ontime data. The most surprising figure presented in the document is that 1 out of 6 flights agnostic of an airline departing from the US is delayed more than 15 minutes.
  1. Flight Delay EDA (Exploratory Data Analysis)
  1. The final dataset is a large compilation of data from various airports and airlines. The data is hosted on a website called kaggle that enables interactive online manipulation of the dataset. This dataset is the most detailed picture of the difference between posted boarding times and departure. The dataset also enables for discovery of delays and boarding times at specific airports on specific airlines during certain travel periods and air travel traffic data.

Interviews

I have selected two interviews. One a business traveler and the other a leisure traveller. My questions consistent between the two portions of my audience are how much time do you think you spend on getting prepared, travelling to, and waiting for a flight. However, if the respondent didn’t have a quantifiable answer I posed a simple question. Do you wish to spend more time exploring or conducting business than sit idle waiting at a port of disembarkation?

  1. Case 1: The Business traveler.
  1. Context: Frequent Flier, Lives in Hotel, no solid routine, Uber/Lyft/Grab is the main mode of transportation besides flying, Sometimes has access to lounges.
  2. Takeaways: Respondent mentioned that they travel on the busiest days of the week (leave on Monday or return on Friday). They often have work that prevents them from being productive while in transit. Often find themselves leaving early from meetings or engagements to catch a flight from an unknown airport. They had mentioned that if they knew how the airport worked and a live “look” into the airport, they would delay their departure accordingly. They also mentioned that they often budget lots of time in unfamiliar places to account for Uber/lyft procurement times.
  3. Response from Users about the tool: Thrilled to have a tool to optimize their time. Would only use it if it seamlessly integrated alongside their other travel habits. They wouldn't use it if it required an unheard amount of setup. My Idea: Forward your trip details/confirmation in an email to some fixed address like plans@time.it
  1. Case 2: The Leisure/Adventure Traveler
  1. Context: Flies only several times a year, ardent planner, low budget, travels lightly, stays at low budget hotels/hostels
  2. Takeaways:Respondents mentioned that they are inexperienced travelers. For the times they have flown, they regret baking in too much time to get to the airport. More specifically, they felt like they cheated themselves by spending time in the airport waiting rather than enjoying their destination. They also have trouble navigating new airports and transit options while abroad or visiting somewhere new.
  3. Response from Users about the tool: We want to share with the user that our tool could help them with their predicament. The user shared that they want some way to have all the information they need at their fingertips so they can plan ahead of time. They also want some way to store the data/application details while offline to prevent them from using roaming data if possible. They are eager to try a new tool rather than just rely on what scheduling process they use at the nearest airport close to home.

Justification of Dataset and Audience

The various datasets provided in the previous sections of this dataset are predominantly references to pre-computed and live data. Cursory research and interview experiences made it evident that travel and air travel more specifically, that people waste lots of time by improperly planning and waiting. Before COVID-19 was on the map, the air-travel and travel sectors in general were booming. According to the world bank, since 2013, on average there has been a steady 6% growth in worldwide passenger traffic. In 2018 alone, 4.233 Billion traveled on an airplane. To put this in perspective there are 7.7 Billion people on the planet. Their aviation industry is absolutely massive and if we consider how much people are delayed and wait, the loss of productive man hours is insane. A tool like this not only is useful to the handful of people who might use it, but has the ability to scale to extraordinary levels and help the lives of billions. The dataset only covers domestic US travel but the US is not alone in travel woes. The primary reason for choosing an aviation related problem is from a place of passion but also the massive scale.

Proposed Functionality

The proposed functionality of this app will be split into two sections. The first section is minimum viable product required functions and the second section will consist of reach functionality.

  1. MVP Functionality
  1. The ability to forward your travel documents to a service that then extrapolates relevant information for your trip.
  2. The ability to manually add trips at any time.
  3. Once a trip is planned the user will be able to set a risk factor that will change how late or how early the user desires to arrive at the port of disembarkation.
  4. Timeline: All parts of the timeline will be viewable at any time
  1. Getting to the Airport.
  1. A timeline to show the current status of the flight that will change to give users contextual information.
  2. The timeline view will show when users should start their journey to the airport or part of disembarkation.
  1. Depending on there preferences the timeline will adjust to compensate for Uber/Lyft hail times.
  1. At the airport
  1. The timeline will show the current status of security wait times and walking times from the checkpoint to the gate
  2. The timeline will update to show historical and crowd sourced information regarding airlines on the specific route at the specific airport.
  1. Timeline Details
  1. At every juncture in the process users will have the ability to have some sort of binary feedback mechanism whether the app provided them useful or bad information. This will be also opened up more if users want to provide user generated feedback and content for the system to use.
  2. Users will have the ability to opt in for more granular tracking to better improve the overall experience.
  1. Reach Functionality
  1. The option for airlines to sell last minute seats using the app as a platform. The value proposition is that airlines always have to fly with a minimum amount of fuel. When flights aren’t booked fully this causes the airline to lose money on the route. On the other hand, people sometimes like crazy good deals and would trade planning for a heavily discounted ticket. The app would supplement the lack of planning with its intelligence and simple way to get users to their port of disembarkation with a safe buffer of time.
  2. The option for users to share their experiences for in app rewards or access to information.
  3. A paid option that subsidizes the cost for more research to better the platform in return to more detailed/accurate information + maybe access to a mechanical turk based support portal that can give users realtime support.