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7.6 Interface Design

7.6.1  Main  Searching  Page  

FIGURE 7.6A:MAIN SEARCHING PAGE

● Clear & Easy-use Searching Function Design

From Figure 7.6A, we can see our beautiful home page which has a very clear searching module. This character makes users very easy to know how to search the flight they want to track. There are two types of searching methods. First one is searching by Flight Number, which is available now at our website. The other option is searching by route which will be developed in the future. The Airline text box is designed from all possible airlines. Now we will use American Airline as an illustration of the model we have built during this thesis. The last text box is for writing a flight number of American Airline. Figure 7.6 takes Flight No.59 as an example here. After inputting these parameters, we can click on SEARCH button to see the arrival delay prediction for Flight No.59.

● Comfortable Background & Elegant Design Style

Besides the above advantages we have shown, our page is using a comfortable background picture, which simulates the real scene of passengers waiting at the airport for their flights.

Meanwhile the whole layout and color assortment of the main page reflects the elegance of our arrival delay prediction web page. Users will have an immersive experience by using this web page.

7.6.2  Searching  Results  Page  

FIGURE 7.6B:SEARCHING RESULTS PAGE

As shown in Figure 7.6B, the search results returned information consists of Flight No., Departure Airport, Departure Gate/Terminal, Scheduled Departure time, Arrival Airport, Scheduled Arrival time, Arrival Gate/Terminal, Depart Boolean value which means the flight has departed or not and Estimated Delay time. Here we give a specific estimated arrival delay prediction instead of range possibilities, which is usually provided by other flight prediction website. Since a specific delay value is more directly for users and can be more accurate than range predictions.

Besides, the following Figure 7.6C: Actual Arrival Delay of Flight No.59 [] shows the actual delay is 27 minutes after Flight No.59 has landed. Compared to our prediction 36 minutes in Figure 7.6 B, we can see our estimated arrival delay prediction has a good accuracy. This example is one of the excellent proofs of our prediction model.

FIGURE 7.6C:ACTUAL ARRIVAL DELAY OF FLIGHT NO.59

C HAPTER 8 C ONCLUSIONS &

F UTURE W ORK

As a conclusion, both the two models we have created for this thesis show excellent performances and tolerances. The first model is for long term prediction and the second one is for short term real time prediction by using big real time data. Hence, they can be utilized by passengers to obtain flight status and also can be applied for customers to do risk management in different fields.

As for future work, we will improve and extended in two sides. One side is to seek a more accurate model to increase the accuracy of our predictions. As stated in introduction part, Bayesian theory is a good direction to further develop. Even though the priori probability is not easy to acquire, we can utilize some mathematical theories and years of historical data to generate the priori probability first. This will be conducive if we can combine the priori probability with real data. It will not only yield to a finest prediction model but also avoid over fit problem of data. The other side we can do is to develop our web page with more functions. Now the web page is mainly for American Airline at San Francisco International Airport, but it has the potentiality to extend for all airlines and all airports. By combining these two sides, we believe our model and web page will contribute more in flight delay risk management area.

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