Feature engineering from date, mean and standard deviation
Date fields are quite interesting data since the limit of what you can "feature engineer" with them is your imagination. However, it is difficult to know a priori if one of them will improve your model before you try it.

Here some ideas:


Year
Month (1-12)
Day of Month (1-31)
Day of week (1-7)
Week of year (1-52)
The quarter of the year (1-4)
Is it weekend or weekday (0,1)
Season (Winter, Spring, Summer, Autumn)


If you have also the time on the date field, then you can try these:


Hour of day (1-24)
Part of the day based on eating habits (breakfast, lunch, dinner etc)


If you have the destination of the flight, then you can use an external source and get the holidays or specific "big events"


Is the flight during a holiday (0,1)
What is the distance in days from the closest holiday (before and after the flight)
Any big event on the destination like Olympic Games, Superbowl, Football finals etc