Well, due to its breathtaking innovation, deep learning has left various industries astonished, and the transportation industry is no exception. The industry is so large that the annual GDP amounts to 471.90 U.S.D. billion in the U.S. alone. However, this industry that forms the backbone of any economy faces some severe challenges as the scale of operation escalates. Grand View Research's latest report stated that the deep learning market would reach a value of $10.2 billion by the end of the year 2025, which highlights the growing popularity of this technology. When both the industry and the technology are growing at such a rate, why not use deep learning in transport to up the performance of this sector.
This article was originally published on ETFTrends.com. Watch a complimentary webinar below to learn more about Apache Spark's MLlib, which makes machine learning scalable and easier with ML pipelines built on top of DataFrames. In MapR Technologies video, you'll get to learn about the following: Review Machine Learning Classification and Random Forests Use Spark SQL and DataFrames to explore real historic flight data Use [...]
One of the most stressful parts of traveling happens between heading to the airport and waiting to board your flight, as you start checking to see if your flight is on time. Flights already shows delays, and now we're sharing reasons for those delays and delay predictions too. Using historic flight status data, our machine learning algorithms can predict some delays even when this information isn't available from airlines yet--and delays are only flagged when we're at least 80% confident in the prediction.