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Flight delays and cancellations are, unfortunately, part and parcel of air travel. When a delay or cancellation occurs, it is indiscriminate about who it attacks and frequently causes stress and inconvenience. While the reasons for flight delays and cancellations are numerous, the overall outcome is the same: an unexpected hindrance to a traveler’s itinerary.
Skybnb visualizes the flight delay patterns of 7 airlines over the year between 10 airports, empowering travelers to make smart decisions when planning trips, and allowing airports to adjust their flight schedules to provide better service.
When a traveler plans a trip from one city to another they are looking for the best travel experience, considering both comfort and ease of travel. Ease of travel considers which airlines have satisfactory service as well as fewer delays and cancellations between the traveler’s point of departure and destination. The traveler also wants to know if they should expect more delays at certain airports, especially during certain weather conditions, to help plan their itinerary.
For airline companies, they want to know the history delay data at an airport under certain conditions, thus allowing them to approximately forecast the delay and arrange standby flight plans. Also, they can adjust their flight plans to provide better service.
The data is collected from Bureau of Transportation Statistics (BTS). BTS is a statistical agency, the premier provider of up-to-date (latest available data from July 2016) transportation statistical knowledge that offers researchers data that is to a large degree guaranteed in both accuracy and completeness.
We want to visualize the arrival and departure delay for every domestic flight in 2015 of seven major airlines in 10 United States airports. Each item represents one flight and has attributes including flight number, date, airline, origin airport, destination airport, scheduled departure time, departure delay, scheduled arrival time and arrival delay.
Fl_num: flight number, which is unique when combined with Airline ID. Nominal data.
Date: The date of departure, ranging from January 1st, 2015 to December 31st, 2015. Ordinal data.
Airline: Each airline has a unique ID. We decide to use ID to store the airline because of the large scale of the dataset. Nominal data.
Origin: Origin Airport of the flight. Nominal data.
Dest: Destination Airport of the flight. Nominal data.
S_Dep_time: Scheduled departure time of the flight in 24 hours. The first two digits represent hour, and the last two digits represent minute. Ordinal data.
Dep_delay: Departure delay. Negative value means the flight took off earlier than its scheduled departure time, while positive value means a delay in departure.Quantitative data.
S_Arr_time: Scheduled arrival time of the flight in 24 hours.The first two digits represent hour, and the last two digits represent minute. Ordinal data.
Arr_delay: Arrival delay. Negative value means the flight arrived at the destination earlier than its scheduled arrival time, while positive value means the arrival delay time. Quantitative data.
We made it clear that our visualization project would serve as more of an exploratory tool for our target users, to help with flight delay trend/pattern detections over time. Design and implementation of the tool should therefore focus more on the handiness and flexibility of the tool-utilizing experience, instead of providing solutions for specific delay problems, say, offering users flight suggestions to take in order to avoid severe delays.
Our design proposal has multi-coordinated views, containing 4 parts: a menu bar at the top, an x-y coordinate view on the left, two bars section (contains 2 bars) at the bottom, and a cards list on the right.
The data covers each single non-stop flight of the 7 airlines, departed and arrived in the 10 airports in the year of 2015. It communicates a small part of a bigger story, therefore only serves for individual exploration purpose, and should not be shared as facts.